Symbolic Expert System

In expert system, symbolic expert system (likewise understood as classical expert system or logic-based expert system) [1] [2] is the term for the collection of all techniques in expert system research study that are based upon top-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI used tools such as reasoning programming, production guidelines, semantic webs and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in seminal ideas in search, symbolic programs languages, agents, multi-agent systems, the semantic web, and the strengths and constraints of official knowledge and thinking systems.

Symbolic AI was the dominant paradigm of AI research from the mid-1950s till the mid-1990s. [4] Researchers in the 1960s and the 1970s were convinced that symbolic techniques would eventually succeed in creating a maker with artificial general intelligence and considered this the supreme objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and promises and was followed by the very first AI Winter as moneying dried up. [5] [6] A second boom (1969-1986) happened with the increase of specialist systems, their pledge of capturing corporate knowledge, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later on disappointment. [8] Problems with troubles in knowledge acquisition, maintaining large understanding bases, and brittleness in handling out-of-domain issues occurred. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on addressing underlying problems in dealing with uncertainty and in understanding acquisition. [10] Uncertainty was addressed with official techniques such as covert Markov models, Bayesian reasoning, and analytical relational learning. [11] [12] Symbolic device learning resolved the understanding acquisition issue with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive logic programs to learn relations. [13]

Neural networks, a subsymbolic approach, had actually been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not deemed effective until about 2012: « Until Big Data ended up being commonplace, the general agreement in the Al neighborhood was that the so-called neural-network approach was helpless. Systems simply didn’t work that well, compared to other approaches. … A transformation came in 2012, when a number of individuals, including a team of scientists working with Hinton, worked out a method to use the power of GPUs to enormously increase the power of neural networks. » [16] Over the next numerous years, deep knowing had incredible success in dealing with vision, speech recognition, speech synthesis, image generation, and device translation. However, since 2020, as intrinsic problems with bias, explanation, coherence, and robustness became more evident with deep knowing methods; an increasing number of AI scientists have actually called for integrating the finest of both the symbolic and neural network techniques [17] [18] and dealing with areas that both techniques have trouble with, such as common-sense reasoning. [16]

A brief history of symbolic AI to the present day follows below. Period and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia post on the History of AI, with dates and titles varying slightly for increased clearness.

The first AI summer: unreasonable spirit, 1948-1966

Success at early attempts in AI happened in three primary areas: artificial neural networks, knowledge representation, and heuristic search, adding to high expectations. This area summarizes Kautz’s reprise of early AI history.

Approaches influenced by human or animal cognition or habits

Cybernetic techniques tried to replicate the feedback loops in between animals and their environments. A robotic turtle, with sensing units, motors for driving and steering, and 7 vacuum tubes for control, based upon a preprogrammed neural internet, was developed as early as 1948. This work can be viewed as an early precursor to later work in neural networks, reinforcement learning, and situated robotics. [20]

An essential early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it had the ability to prove 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to produce a domain-independent problem solver, GPS (General Problem Solver). GPS fixed issues represented with official operators via state-space search utilizing means-ends analysis. [21]

During the 1960s, symbolic techniques accomplished great success at imitating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in 4 organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own design of research study. Earlier methods based on cybernetics or synthetic neural networks were abandoned or pressed into the background.

Herbert Simon and Allen Newell studied human problem-solving abilities and attempted to formalize them, and their work laid the structures of the field of expert system, as well as cognitive science, operations research and management science. Their research study team used the outcomes of psychological experiments to develop programs that simulated the methods that individuals used to solve problems. [22] [23] This tradition, focused at Carnegie Mellon University would eventually culminate in the advancement of the Soar architecture in the center 1980s. [24] [25]

Heuristic search

In addition to the extremely specialized domain-specific kinds of understanding that we will see later on used in specialist systems, early symbolic AI scientists discovered another more general application of . These were called heuristics, guidelines that guide a search in appealing instructions: « How can non-enumerative search be useful when the underlying problem is exponentially hard? The technique advocated by Simon and Newell is to employ heuristics: fast algorithms that might fail on some inputs or output suboptimal solutions. » [26] Another crucial advance was to find a method to apply these heuristics that guarantees an option will be found, if there is one, not holding up against the occasional fallibility of heuristics: « The A * algorithm provided a general frame for total and ideal heuristically assisted search. A * is utilized as a subroutine within virtually every AI algorithm today but is still no magic bullet; its guarantee of completeness is purchased the cost of worst-case exponential time. [26]

Early deal with knowledge representation and reasoning

Early work covered both applications of official thinking emphasizing first-order logic, along with attempts to handle sensible reasoning in a less official manner.

Modeling official thinking with reasoning: the « neats »

Unlike Simon and Newell, John McCarthy felt that devices did not need to simulate the precise mechanisms of human thought, however could instead search for the essence of abstract reasoning and problem-solving with logic, [27] regardless of whether individuals used the same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on using formal reasoning to resolve a wide range of problems, including knowledge representation, preparation and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and in other places in Europe which led to the advancement of the programs language Prolog and the science of logic programming. [32] [33]

Modeling implicit sensible understanding with frames and scripts: the « scruffies »

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] discovered that fixing tough issues in vision and natural language processing needed ad hoc solutions-they argued that no simple and basic concept (like logic) would catch all the elements of smart habits. Roger Schank described their « anti-logic » methods as « shabby » (as opposed to the « cool » paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of « shabby » AI, considering that they should be developed by hand, one complex concept at a time. [38] [39] [40]

The first AI winter: crushed dreams, 1967-1977

The first AI winter season was a shock:

During the very first AI summer season, lots of people believed that device intelligence could be accomplished in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research study to use AI to solve issues of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battleground. Researchers had begun to understand that attaining AI was going to be much harder than was supposed a years earlier, however a mix of hubris and disingenuousness led many university and think-tank scientists to accept financing with pledges of deliverables that they need to have understood they could not meet. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had been created, and a dramatic backlash set in. New DARPA management canceled existing AI funding programs.

Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the United Kingdom was spurred on not so much by dissatisfied military leaders as by rival academics who viewed AI scientists as charlatans and a drain on research funding. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research in the country. The report mentioned that all of the problems being dealt with in AI would be better dealt with by scientists from other disciplines-such as applied mathematics. The report likewise claimed that AI successes on toy problems might never ever scale to real-world applications due to combinatorial surge. [41]

The second AI summer season: understanding is power, 1978-1987

Knowledge-based systems

As constraints with weak, domain-independent methods became more and more evident, [42] scientists from all 3 customs started to construct knowledge into AI applications. [43] [7] The knowledge revolution was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.

Edward Feigenbaum said:

– « In the knowledge lies the power. » [44]
to explain that high performance in a specific domain requires both general and extremely domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:

( 1) The Knowledge Principle: if a program is to perform an intricate job well, it should understand a lot about the world in which it runs.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are 2 additional capabilities essential for smart habits in unanticipated scenarios: drawing on progressively general understanding, and analogizing to particular however remote knowledge. [45]

Success with expert systems

This « understanding transformation » resulted in the advancement and deployment of professional systems (introduced by Edward Feigenbaum), the first commercially successful type of AI software application. [46] [47] [48]

Key specialist systems were:

DENDRAL, which found the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and recommended additional lab tests, when necessary – by analyzing lab outcomes, patient history, and medical professional observations. « With about 450 rules, MYCIN had the ability to carry out along with some professionals, and considerably much better than junior physicians. » [49] INTERNIST and CADUCEUS which took on internal medicine diagnosis. Internist attempted to catch the know-how of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately diagnose up to 1000 different illness.
– GUIDON, which demonstrated how a knowledge base built for specialist problem resolving could be repurposed for teaching. [50] XCON, to configure VAX computers, a then laborious procedure that could use up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is considered the first specialist system that relied on knowledge-intensive problem-solving. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

One of individuals at Stanford thinking about computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I told him I desired an induction « sandbox », he stated, « I have simply the one for you. » His lab was doing mass spectrometry of amino acids. The question was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we started the DENDRAL Project: I was good at heuristic search approaches, and he had an algorithm that was proficient at creating the chemical problem area.

We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, developer of the chemical behind the contraceptive pill, and likewise among the world’s most respected mass spectrometrists. Carl and his postdocs were world-class specialists in mass spectrometry. We began to contribute to their understanding, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL a growing number of knowledge. The more you did that, the smarter the program became. We had very excellent outcomes.

The generalization was: in the understanding lies the power. That was the big idea. In my career that is the substantial, « Ah ha!, » and it wasn’t the method AI was being done previously. Sounds easy, however it’s most likely AI’s most effective generalization. [51]

The other specialist systems mentioned above followed DENDRAL. MYCIN exhibits the timeless professional system architecture of a knowledge-base of rules combined to a symbolic reasoning mechanism, including using certainty aspects to handle uncertainty. GUIDON demonstrates how a specific knowledge base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a particular kind of knowledge-based application. Clancey revealed that it was not adequate just to use MYCIN’s guidelines for guideline, but that he also needed to add rules for dialogue management and trainee modeling. [50] XCON is significant because of the millions of dollars it saved DEC, which triggered the specialist system boom where most all significant corporations in the US had expert systems groups, to record business expertise, maintain it, and automate it:

By 1988, DEC’s AI group had 40 expert systems deployed, with more en route. DuPont had 100 in usage and 500 in development. Nearly every significant U.S. corporation had its own Al group and was either utilizing or investigating specialist systems. [49]

Chess specialist knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the assistance of symbolic AI, to win in a game of chess versus the world champion at that time, Garry Kasparov. [52]

Architecture of knowledge-based and professional systems

A crucial component of the system architecture for all specialist systems is the understanding base, which shops realities and rules for analytical. [53] The most basic method for an expert system knowledge base is just a collection or network of production guidelines. Production guidelines connect symbols in a relationship comparable to an If-Then statement. The expert system processes the guidelines to make deductions and to determine what additional information it needs, i.e. what concerns to ask, utilizing human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools operate in this style.

Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – way. More innovative knowledge-based systems, such as Soar can also carry out meta-level reasoning, that is reasoning about their own reasoning in regards to deciding how to solve issues and keeping track of the success of problem-solving strategies.

Blackboard systems are a 2nd sort of knowledge-based or skilled system architecture. They model a community of specialists incrementally contributing, where they can, to fix an issue. The issue is represented in several levels of abstraction or alternate views. The professionals (knowledge sources) offer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on an agenda that is updated as the issue scenario changes. A controller chooses how helpful each contribution is, and who need to make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was originally inspired by studies of how human beings plan to perform multiple tasks in a journey. [55] An innovation of BB1 was to use the exact same blackboard model to solving its control problem, i.e., its controller performed meta-level reasoning with knowledge sources that monitored how well a plan or the analytical was continuing and might switch from one method to another as conditions – such as goals or times – changed. BB1 has been used in multiple domains: building site preparation, smart tutoring systems, and real-time client monitoring.

The second AI winter season, 1988-1993

At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP devices specifically targeted to accelerate the development of AI applications and research. In addition, numerous expert system business, such as Teknowledge and Inference Corporation, were offering expert system shells, training, and speaking with to corporations.

Unfortunately, the AI boom did not last and Kautz finest explains the second AI winter that followed:

Many reasons can be offered for the arrival of the 2nd AI winter. The hardware business failed when much more affordable general Unix workstations from Sun together with good compilers for LISP and Prolog came onto the marketplace. Many business implementations of specialist systems were terminated when they proved too costly to maintain. Medical expert systems never ever captured on for a number of factors: the difficulty in keeping them approximately date; the challenge for doctor to learn how to use a bewildering range of various professional systems for different medical conditions; and maybe most crucially, the hesitation of physicians to rely on a computer-made diagnosis over their gut instinct, even for specific domains where the expert systems might surpass an average physician. Venture capital cash deserted AI practically over night. The world AI conference IJCAI hosted a massive and extravagant exhibition and countless nonacademic guests in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]

Including more extensive foundations, 1993-2011

Uncertain thinking

Both analytical approaches and extensions to logic were attempted.

One statistical method, hidden Markov models, had currently been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl promoted the usage of Bayesian Networks as a sound but effective method of dealing with unsure reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were used effectively in specialist systems. [57] Even later on, in the 1990s, analytical relational knowing, a technique that combines possibility with logical formulas, enabled possibility to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order logic to support were also tried. For instance, non-monotonic reasoning might be used with reality maintenance systems. A fact maintenance system tracked assumptions and reasons for all inferences. It enabled inferences to be withdrawn when assumptions were discovered out to be incorrect or a contradiction was obtained. Explanations might be offered an inference by discussing which rules were applied to develop it and after that continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had presented a various sort of extension to manage the representation of vagueness. For example, in choosing how « heavy » or « tall » a man is, there is often no clear « yes » or « no » response, and a predicate for heavy or tall would instead return values in between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy reasoning further supplied a way for propagating combinations of these worths through logical formulas. [59]

Machine knowing

Symbolic maker discovering approaches were investigated to attend to the knowledge acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to create plausible guideline hypotheses to test against spectra. Domain and job understanding lowered the variety of candidates evaluated to a workable size. Feigenbaum explained Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s relating to theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of knowledge to steer and prune the search. That knowledge got in there since we spoke with individuals. But how did individuals get the understanding? By looking at thousands of spectra. So we desired a program that would take a look at thousands of spectra and infer the knowledge of mass spectrometry that DENDRAL might utilize to fix specific hypothesis formation problems. We did it. We were even able to release brand-new understanding of mass spectrometry in the Journal of the American Chemical Society, giving credit only in a footnote that a program, Meta-DENDRAL, actually did it. We were able to do something that had actually been a dream: to have a computer system program created a brand-new and publishable piece of science. [51]

In contrast to the knowledge-intensive approach of Meta-DENDRAL, Ross Quinlan created a domain-independent technique to statistical category, decision tree learning, beginning initially with ID3 [60] and after that later on extending its capabilities to C4.5. [61] The decision trees developed are glass box, interpretable classifiers, with human-interpretable category guidelines.

Advances were made in comprehending machine knowing theory, too. Tom Mitchell presented version space learning which explains knowing as an explore an area of hypotheses, with upper, more general, and lower, more particular, limits including all feasible hypotheses constant with the examples seen up until now. [62] More formally, Valiant presented Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of device knowing. [63]

Symbolic machine learning included more than discovering by example. E.g., John Anderson provided a cognitive design of human knowing where skill practice leads to a collection of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a trainee may discover to use « Supplementary angles are two angles whose procedures sum 180 degrees » as a number of various procedural guidelines. E.g., one rule may say that if X and Y are supplementary and you know X, then Y will be 180 – X. He called his method « knowledge collection ». ACT-R has been utilized successfully to design aspects of human cognition, such as learning and retention. ACT-R is also utilized in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer programs, and algebra to school children. [64]

Inductive logic programming was another method to finding out that allowed reasoning programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might manufacture Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to produce genetic programs, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more basic approach to program synthesis that manufactures a practical program in the course of showing its requirements to be correct. [66]

As an option to logic, Roger Schank presented case-based reasoning (CBR). The CBR approach laid out in his book, Dynamic Memory, [67] focuses initially on remembering crucial problem-solving cases for future use and generalizing them where suitable. When confronted with a new problem, CBR recovers the most similar previous case and adjusts it to the specifics of the current issue. [68] Another option to reasoning, genetic algorithms and genetic shows are based upon an evolutionary model of knowing, where sets of guidelines are encoded into populations, the rules govern the habits of individuals, and selection of the fittest prunes out sets of unsuitable guidelines over lots of generations. [69]

Symbolic device knowing was used to learning concepts, rules, heuristics, and analytical. Approaches, besides those above, consist of:

1. Learning from guideline or advice-i.e., taking human guideline, postured as suggestions, and identifying how to operationalize it in particular situations. For example, in a video game of Hearts, discovering precisely how to play a hand to « avoid taking points. » [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter specialist (SME) feedback during training. When problem-solving fails, querying the expert to either learn a new prototype for problem-solving or to discover a brand-new explanation as to exactly why one prototype is more pertinent than another. For instance, the program Protos found out to diagnose ringing in the ears cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing problem services based on similar problems seen in the past, and after that customizing their options to fit a new situation or domain. [72] [73] 4. Apprentice learning systems-learning novel services to problems by observing human problem-solving. Domain understanding describes why unique options are correct and how the service can be generalized. LEAP discovered how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., producing jobs to perform experiments and after that discovering from the outcomes. Doug Lenat’s Eurisko, for example, found out heuristics to beat human gamers at the Traveller role-playing video game for two years in a row. [75] 6. Learning macro-operators-i.e., searching for helpful macro-operators to be gained from series of basic problem-solving actions. Good macro-operators streamline problem-solving by permitting problems to be solved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now

With the rise of deep knowing, the symbolic AI technique has been compared to deep learning as complementary « … with parallels having actually been drawn numerous times by AI researchers between Kahneman’s research on human reasoning and choice making – reflected in his book Thinking, Fast and Slow – and the so-called « AI systems 1 and 2″, which would in principle be designed by deep knowing and symbolic thinking, respectively. » In this view, symbolic thinking is more apt for deliberative reasoning, planning, and explanation while deep knowing is more apt for fast pattern recognition in affective applications with noisy information. [17] [18]

Neuro-symbolic AI: integrating neural and symbolic approaches

Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a manner that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI capable of thinking, learning, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the reliable building and construction of abundant computational cognitive designs demands the combination of sound symbolic reasoning and efficient (device) learning designs. Gary Marcus, similarly, argues that: « We can not construct rich cognitive designs in a sufficient, automated method without the set of three of hybrid architecture, rich anticipation, and advanced strategies for thinking. », [79] and in particular: « To develop a robust, knowledge-driven technique to AI we must have the machinery of symbol-manipulation in our toolkit. Excessive of useful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we understand of that can manipulate such abstract knowledge reliably is the apparatus of sign adjustment.  » [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a need to attend to the two sort of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two elements, System 1 and System 2. System 1 is quick, automated, user-friendly and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far much better matched for preparation, reduction, and deliberative thinking. In this view, deep learning finest designs the very first type of believing while symbolic thinking best models the second kind and both are needed.

Garcez and Lamb explain research study in this area as being ongoing for at least the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has been held every year given that 2005, see http://www.neural-symbolic.org/ for details.

In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:

The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a fairly little research neighborhood over the last 2 decades and has actually yielded several substantial results. Over the last decade, neural symbolic systems have actually been revealed capable of conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in action to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed capable of representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and pieces of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have actually been applied to a number of problems in the areas of bioinformatics, control engineering, software application verification and adaptation, visual intelligence, ontology knowing, and video game. [78]

Approaches for combination are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:

– Symbolic Neural symbolic-is the present technique of lots of neural designs in natural language processing, where words or subword tokens are both the ultimate input and output of big language models. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic strategies are used to call neural techniques. In this case the symbolic approach is Monte Carlo tree search and the neural methods learn how to examine game positions.
– Neural|Symbolic-uses a neural architecture to analyze perceptual information as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to produce or label training data that is consequently found out by a deep knowing model, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to create or identify examples.
– Neural _ Symbolic -uses a neural web that is created from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree generated from understanding base guidelines and terms. Logic Tensor Networks [86] likewise fall under this classification.
– Neural [Symbolic] -allows a neural design to directly call a symbolic thinking engine, e.g., to perform an action or assess a state.

Many key research study concerns remain, such as:

– What is the very best way to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible understanding be discovered and reasoned about?
– How can abstract understanding that is difficult to encode realistically be dealt with?

Techniques and contributions

This area provides an overview of methods and contributions in a total context leading to many other, more in-depth posts in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered previously in the history section.

AI programming languages

The key AI shows language in the US throughout the last symbolic AI boom duration was LISP. LISP is the second earliest programs language after FORTRAN and was developed in 1958 by John McCarthy. LISP offered the very first read-eval-print loop to support quick program advancement. Compiled functions could be freely combined with interpreted functions. Program tracing, stepping, and breakpoints were also offered, along with the ability to change worths or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally composed in LISP and after that ran interpretively to assemble the compiler code.

Other crucial innovations pioneered by LISP that have actually spread out to other programs languages include:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

Programs were themselves data structures that other programs might operate on, allowing the simple meaning of higher-level languages.

In contrast to the US, in Europe the crucial AI shows language throughout that very same period was Prolog. Prolog offered an integrated store of facts and clauses that could be queried by a read-eval-print loop. The shop could act as an understanding base and the provisions might serve as rules or a restricted kind of logic. As a subset of first-order reasoning Prolog was based upon Horn clauses with a closed-world assumption-any truths not understood were thought about false-and a distinct name assumption for primitive terms-e.g., the identifier barack_obama was thought about to refer to precisely one object. Backtracking and marriage are integrated to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of logic programs, which was created by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more detail see the section on the origins of Prolog in the PLANNER short article.

Prolog is likewise a kind of declarative programming. The reasoning provisions that describe programs are straight interpreted to run the programs specified. No specific series of actions is needed, as is the case with imperative shows languages.

Japan promoted Prolog for its Fifth Generation Project, planning to build special hardware for high efficiency. Similarly, LISP makers were built to run LISP, however as the second AI boom turned to bust these business might not take on new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history section for more information.

Smalltalk was another prominent AI shows language. For example, it introduced metaclasses and, along with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits numerous inheritance, in addition to incremental extensions to both classes and metaclasses, thus providing a run-time meta-object procedure. [88]

For other AI programs languages see this list of programming languages for expert system. Currently, Python, a multi-paradigm shows language, is the most popular programming language, partly due to its substantial bundle library that supports information science, natural language processing, and deep learning. Python consists of a read-eval-print loop, practical aspects such as higher-order functions, and object-oriented programming that consists of metaclasses.

Search

Search arises in numerous kinds of problem resolving, including planning, constraint fulfillment, and playing video games such as checkers, chess, and go. The very best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause knowing, and the DPLL algorithm. For adversarial search when playing games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and thinking

Multiple different approaches to represent knowledge and after that factor with those representations have actually been examined. Below is a fast summary of approaches to knowledge representation and automated thinking.

Knowledge representation

Semantic networks, conceptual charts, frames, and reasoning are all approaches to modeling knowledge such as domain understanding, analytical understanding, and the semantic meaning of language. Ontologies model essential concepts and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be deemed an ontology. YAGO incorporates WordNet as part of its ontology, to line up realities drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.

Description logic is a reasoning for automated category of ontologies and for identifying irregular classification data. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can check out in OWL ontologies and after that inspect consistency with deductive classifiers such as such as HermiT. [89]

First-order logic is more basic than description reasoning. The automated theorem provers gone over below can show theorems in first-order logic. Horn clause reasoning is more limited than first-order reasoning and is utilized in reasoning shows languages such as Prolog. Extensions to first-order logic include temporal logic, to deal with time; epistemic logic, to reason about agent understanding; modal logic, to manage possibility and requirement; and probabilistic reasonings to manage logic and likelihood together.

Automatic theorem showing

Examples of automated theorem provers for first-order reasoning are:

Prover9.
ACL2.
Vampire.

Prover9 can be used in conjunction with the Mace4 model checker. ACL2 is a theorem prover that can handle evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, also understood as Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have a specific knowledge base, usually of guidelines, to boost reusability throughout domains by separating procedural code and domain understanding. A different reasoning engine procedures guidelines and adds, deletes, or modifies a knowledge shop.

Forward chaining reasoning engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more minimal sensible representation is utilized, Horn Clauses. Pattern-matching, particularly unification, is used in Prolog.

A more flexible kind of problem-solving occurs when reasoning about what to do next takes place, instead of merely choosing one of the available actions. This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R might have additional capabilities, such as the capability to put together regularly used understanding into higher-level portions.

Commonsense thinking

Marvin Minsky initially proposed frames as a way of analyzing typical visual scenarios, such as an office, and Roger Schank extended this concept to scripts for common routines, such as eating in restaurants. Cyc has actually attempted to catch beneficial sensible knowledge and has « micro-theories » to manage particular sort of domain-specific reasoning.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about ignorant physics, such as what happens when we heat a liquid in a pot on the stove. We anticipate it to heat and potentially boil over, despite the fact that we may not understand its temperature level, its boiling point, or other details, such as air pressure.

Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be resolved with restraint solvers.

Constraints and constraint-based reasoning

Constraint solvers carry out a more minimal sort of inference than first-order reasoning. They can streamline sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, in addition to resolving other kinds of puzzle issues, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be utilized to resolve scheduling issues, for instance with constraint handling rules (CHR).

Automated planning

The General Problem Solver (GPS) cast preparation as analytical utilized means-ends analysis to produce plans. STRIPS took a different method, seeing planning as theorem proving. Graphplan takes a least-commitment technique to planning, rather than sequentially picking actions from an initial state, working forwards, or an objective state if working in reverse. Satplan is an approach to planning where a planning problem is minimized to a Boolean satisfiability issue.

Natural language processing

Natural language processing concentrates on dealing with language as information to carry out jobs such as determining topics without always comprehending the desired meaning. Natural language understanding, in contrast, constructs a meaning representation and utilizes that for additional processing, such as addressing questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all aspects of natural language processing long dealt with by symbolic AI, but since improved by deep knowing approaches. In symbolic AI, discourse representation theory and first-order logic have been utilized to represent sentence significances. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of files. In the latter case, vector components are interpretable as concepts named by Wikipedia articles.

New deep knowing techniques based on Transformer models have now eclipsed these earlier symbolic AI approaches and obtained state-of-the-art efficiency in natural language processing. However, Transformer designs are nontransparent and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector elements is nontransparent.

Agents and multi-agent systems

Agents are autonomous systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s standard book on synthetic intelligence is organized to show agent architectures of increasing sophistication. [91] The elegance of representatives varies from basic reactive agents, to those with a model of the world and automated preparation abilities, perhaps a BDI representative, i.e., one with beliefs, desires, and intents – or alternatively a reinforcement finding out design discovered in time to choose actions – as much as a combination of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep learning for understanding. [92]

On the other hand, a multi-agent system consists of numerous agents that interact amongst themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The agents require not all have the very same internal architecture. Advantages of multi-agent systems include the ability to divide work amongst the agents and to increase fault tolerance when representatives are lost. Research issues consist of how agents reach agreement, dispersed issue solving, multi-agent learning, multi-agent planning, and dispersed constraint optimization.

Controversies developed from early on in symbolic AI, both within the field-e.g., between logicists (the pro-logic « neats ») and non-logicists (the anti-logic « scruffies »)- and in between those who accepted AI however declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were mainly from philosophers, on intellectual premises, however likewise from financing firms, specifically throughout the 2 AI winter seasons.

The Frame Problem: understanding representation challenges for first-order reasoning

Limitations were discovered in using simple first-order logic to factor about vibrant domains. Problems were discovered both with regards to enumerating the prerequisites for an action to prosper and in offering axioms for what did not change after an action was performed.

McCarthy and Hayes presented the Frame Problem in 1969 in the paper, « Some Philosophical Problems from the Standpoint of Expert System. » [93] A simple example occurs in « showing that a person individual could enter into discussion with another », as an axiom asserting « if a person has a telephone he still has it after searching for a number in the telephone book » would be needed for the reduction to succeed. Similar axioms would be needed for other domain actions to specify what did not alter.

A similar issue, called the Qualification Problem, happens in attempting to specify the preconditions for an action to succeed. A limitless number of pathological conditions can be envisioned, e.g., a banana in a tailpipe might prevent an automobile from running correctly.

McCarthy’s technique to fix the frame problem was circumscription, a sort of non-monotonic logic where reductions might be made from actions that require just define what would change while not having to explicitly specify whatever that would not change. Other non-monotonic logics supplied reality upkeep systems that revised beliefs resulting in contradictions.

Other ways of managing more open-ended domains included probabilistic reasoning systems and artificial intelligence to discover brand-new principles and rules. McCarthy’s Advice Taker can be considered as a motivation here, as it could incorporate brand-new understanding offered by a human in the kind of assertions or rules. For example, speculative symbolic maker learning systems checked out the capability to take top-level natural language advice and to analyze it into domain-specific actionable guidelines.

Similar to the issues in handling dynamic domains, sensible reasoning is likewise tough to catch in official reasoning. Examples of sensible thinking consist of implicit thinking about how people think or general knowledge of daily occasions, items, and living animals. This type of knowledge is taken for given and not seen as noteworthy. Common-sense thinking is an open location of research study and challenging both for symbolic systems (e.g., Cyc has tried to capture key parts of this knowledge over more than a decade) and neural systems (e.g., self-driving automobiles that do not understand not to drive into cones or not to strike pedestrians strolling a bike).

McCarthy viewed his Advice Taker as having sensible, but his meaning of common-sense was various than the one above. [94] He specified a program as having typical sense « if it automatically deduces for itself an adequately broad class of instant effects of anything it is informed and what it currently understands. « 

Connectionist AI: philosophical challenges and sociological disputes

Connectionist approaches consist of earlier work on neural networks, [95] such as perceptrons; work in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s advanced approaches, such as Transformers, GANs, and other work in deep learning.

Three philosophical positions [96] have actually been described among connectionists:

1. Implementationism-where connectionist architectures execute the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined absolutely, and connectionist architectures underlie intelligence and are totally adequate to describe it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are viewed as complementary and both are required for intelligence

Olazaran, in his sociological history of the debates within the neural network community, explained the moderate connectionism consider as basically suitable with present research in neuro-symbolic hybrids:

The 3rd and last position I would like to take a look at here is what I call the moderate connectionist view, a more diverse view of the existing dispute in between connectionism and symbolic AI. Among the researchers who has elaborated this position most clearly is Andy Clark, a thinker from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partially symbolic, partly connectionist) systems. He claimed that (at least) 2 kinds of theories are needed in order to study and model cognition. On the one hand, for some information-processing tasks (such as pattern recognition) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive reasoning, and generative symbol adjustment processes) the symbolic paradigm offers sufficient designs, and not only « approximations » (contrary to what extreme connectionists would claim). [97]

Gary Marcus has declared that the animus in the deep knowing neighborhood against symbolic techniques now might be more sociological than philosophical:

To think that we can just desert symbol-manipulation is to suspend disbelief.

And yet, for the most part, that’s how most current AI profits. Hinton and many others have attempted tough to eliminate signs completely. The deep knowing hope-seemingly grounded not so much in science, but in a sort of historic grudge-is that smart behavior will emerge purely from the confluence of enormous information and deep learning. Where classical computers and software application fix jobs by defining sets of symbol-manipulating rules committed to particular jobs, such as editing a line in a word processor or carrying out an estimation in a spreadsheet, neural networks usually try to solve jobs by statistical approximation and discovering from examples.

According to Marcus, Geoffrey Hinton and his colleagues have actually been vehemently « anti-symbolic »:

When deep learning reemerged in 2012, it was with a kind of take-no-prisoners mindset that has actually identified the majority of the last years. By 2015, his hostility toward all things signs had totally taken shape. He offered a talk at an AI workshop at Stanford comparing symbols to aether, among science’s greatest errors.

…

Ever since, his anti-symbolic campaign has actually just increased in strength. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in one of science’s essential journals, Nature. It closed with a direct attack on sign manipulation, calling not for reconciliation but for straight-out replacement. Later, Hinton told an event of European Union leaders that investing any more money in symbol-manipulating approaches was « a substantial mistake, » likening it to buying internal combustion engines in the era of electrical vehicles. [98]

Part of these disagreements may be because of uncertain terms:

Turing award winner Judea Pearl provides a critique of maker knowing which, unfortunately, conflates the terms machine knowing and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of specialist systems dispossessed of any capability to learn. Using the terms needs explanation. Artificial intelligence is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational learning (the distinctions to deep knowing being the option of representation, localist rational rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production guidelines composed by hand. An appropriate definition of AI concerns knowledge representation and reasoning, autonomous multi-agent systems, planning and argumentation, in addition to knowing. [99]

Situated robotics: the world as a model

Another review of symbolic AI is the embodied cognition technique:

The embodied cognition method declares that it makes no sense to consider the brain separately: cognition occurs within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s functioning exploits consistencies in its environment, including the rest of its body. Under the embodied cognition technique, robotics, vision, and other sensing units become main, not peripheral. [100]

Rodney Brooks invented behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this technique, is deemed an alternative to both symbolic AI and connectionist AI. His technique declined representations, either symbolic or distributed, as not just unnecessary, but as damaging. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer accomplishes a different purpose and needs to work in the genuine world. For instance, the very first robot he explains in Intelligence Without Representation, has three layers. The bottom layer analyzes sonar sensing units to avoid things. The middle layer causes the robotic to roam around when there are no obstacles. The top layer triggers the robotic to go to more distant places for additional expedition. Each layer can momentarily prevent or reduce a lower-level layer. He criticized AI scientists for specifying AI problems for their systems, when: « There is no clean division in between understanding (abstraction) and thinking in the real life. » [101] He called his robotics « Creatures » and each layer was « composed of a fixed-topology network of basic limited state devices. » [102] In the Nouvelle AI method, « First, it is essential to test the Creatures we build in the genuine world; i.e., in the exact same world that we humans inhabit. It is devastating to fall under the temptation of testing them in a simplified world initially, even with the very best intents of later moving activity to an unsimplified world. » [103] His focus on real-world testing was in contrast to « Early operate in AI focused on video games, geometrical issues, symbolic algebra, theorem proving, and other formal systems » [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, however has actually been slammed by the other methods. Symbolic AI has been criticized as disembodied, accountable to the credentials issue, and bad in dealing with the affective issues where deep learning excels. In turn, connectionist AI has actually been criticized as poorly fit for deliberative detailed problem resolving, integrating knowledge, and dealing with planning. Finally, Nouvelle AI masters reactive and real-world robotics domains however has actually been criticized for difficulties in integrating knowing and knowledge.

Hybrid AIs incorporating several of these methods are currently considered as the path forward. [19] [81] [82] Russell and Norvig conclude that:

Overall, Dreyfus saw locations where AI did not have complete responses and stated that Al is therefore difficult; we now see many of these same areas undergoing continued research study and development resulting in increased ability, not impossibility. [100]

Expert system.
Automated planning and scheduling
Automated theorem proving
Belief modification
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep learning
First-order logic
GOFAI
History of artificial intelligence
Inductive logic shows
Knowledge-based systems
Knowledge representation and reasoning
Logic programming
Artificial intelligence
Model monitoring
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of synthetic intelligence
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet

Notes

^ McCarthy when stated: « This is AI, so we do not care if it’s emotionally real ». [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he stated « Artificial intelligence is not, by meaning, simulation of human intelligence ». [28] Pamela McCorduck writes that there are « 2 major branches of artificial intelligence: one intended at producing smart habits regardless of how it was accomplished, and the other targeted at modeling smart procedures discovered in nature, especially human ones. », [29] Stuart Russell and Peter Norvig wrote « Aeronautical engineering texts do not specify the goal of their field as making ‘machines that fly so precisely like pigeons that they can trick even other pigeons.' » [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). « Reconciling deep knowing with symbolic synthetic intelligence: representing things and relations ». Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). « Logic-Based Artificial Intelligence ». In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). « Reconciling deep learning with symbolic expert system: representing objects and relations ». Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). « Learning representations by back-propagating errors ». Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). « Backpropagation Applied to Handwritten Postal Code Recognition ». Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. « Thinking Fast and Slow in AI ». AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. « AAAI Presidential Address: The State of AI ». AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). « An interview with Ed Feigenbaum ». Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). « On the limits of knowledge ». Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications: 291-300. doi:10.1109/ AIIA.1988.13308. S2CID 11778085.
^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). « An interview with Ed Feigenbaum ». Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
^ « The fascination with AI: what is artificial intelligence? ». IONOS Digitalguide. Retrieved 2021-12-02.
^ Hayes-Roth, Murray & Adelman 2015.
^ Hayes-Roth, Barbara (1985 ). « A chalkboard architecture for control ». Expert system. 26 (3 ): 251-321. doi:10.1016/ 0004-3702( 85 )90063-3.
^ Hayes-Roth, Barbara (1980 ). Human Planning Processes. RAND.
^ Pearl 1988.
^ Spiegelhalter et al. 1993.
^ Russell & Norvig 2021, pp. 335-337.
^ Russell & Norvig 2021, p. 459.
^ Quinlan, J. Ross. « Chapter 15: Learning Efficient Classification Procedures and their Application to Chess End Games ». In Michalski, Carbonell & Mitchell (1983 ).
^ Quinlan, J. Ross (1992-10-15). C4.5: Programs for Machine Learning (1st ed.). San Mateo, Calif: Morgan Kaufmann. ISBN 978-1-55860-238-0.
^ Mitchell, Tom M.; Utgoff, Paul E.; Banerji, Ranan. « Chapter 6: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics ». In Michalski, Carbonell & Mitchell (1983 ).
^ Valiant, L. G. (1984-11-05). « A theory of the learnable ». Communications of the ACM. 27 (11 ): 1134-1142. doi:10.1145/ 1968.1972. ISSN 0001-0782. S2CID 12837541.
^ Koedinger, K. R.; Anderson, J. R.; Hadley, W. H.; Mark, M. A.; others (1997 ). « Intelligent tutoring goes to school in the big city ». International Journal of Artificial Intelligence in Education (IJAIED). 8: 30-43. Retrieved 2012-08-18.
^ Shapiro, Ehud Y (1981 ). « The Model Inference System ». Proceedings of the 7th international joint conference on Expert system. IJCAI. Vol. 2. p. 1064.
^ Manna, Zohar; Waldinger, Richard (1980-01-01). « A Deductive Approach to Program Synthesis ». ACM Trans. Program. Lang. Syst. 2 (1 ): 90-121. doi:10.1145/ 357084.357090. S2CID 14770735.
^ Schank, Roger C. (1983-01-28). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge Cambridgeshire: New York: Cambridge University Press. ISBN 978-0-521-27029-8.
^ Hammond, Kristian J. (1989-04-11). Case-Based Planning: Viewing Planning as a Memory Task. Boston: Academic Press. ISBN 978-0-12-322060-8.
^ Koza, John R. (1992-12-11). Genetic Programming: On the Programming of Computers by Means of Natural Selection (1st ed.). Cambridge, Mass: A Bradford Book. ISBN 978-0-262-11170-6.
^ Mostow, David Jack. « Chapter 12: Machine Transformation of Advice into a Heuristic Search Procedure ». In Michalski, Carbonell & Mitchell (1983 ).
^ Bareiss, Ray; Porter, Bruce; Wier, Craig. « Chapter 4: Protos: An Exemplar-Based Learning Apprentice ». In Michalski, Carbonell & Mitchell (1986 ), pp. 112-139.
^ Carbonell, Jaime. « Chapter 5: Learning by Analogy: Formulating and Generalizing Plans from Past Experience ». In Michalski, Carbonell & Mitchell (1983 ), pp. 137-162.
^ Carbonell, Jaime. « Chapter 14: Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition ». In Michalski, Carbonell & Mitchell (1986 ), pp. 371-392.
^ Mitchell, Tom; Mabadevan, Sridbar; Steinberg, Louis. « Chapter 10: LEAP: A Learning Apprentice for VLSI Design ». In Kodratoff & Michalski (1990 ), pp. 271-289.
^ Lenat, Douglas. « Chapter 9: The Role of Heuristics in Learning by Discovery: Three Case Studies ». In Michalski, Carbonell & Mitchell (1983 ), pp. 243-306.
^ Korf, Richard E. (1985 ). Learning to Solve Problems by Searching for Macro-Operators. Research Notes in Expert System. Pitman Publishing. ISBN 0-273-08690-1.
^ Valiant 2008.
^ a b Garcez et al. 2015.
^ Marcus 2020, p. 44.
^ Marcus 2020, p. 17.
^ a b Rossi 2022.
^ a b Selman 2022.
^ Garcez & Lamb 2020, p. 2.
^ Garcez et al. 2002.
^ Rocktäschel, Tim; Riedel, Sebastian (2016 ). « Learning Knowledge Base Inference with Neural Theorem Provers ». Proceedings of the 5th Workshop on Automated Knowledge Base Construction. San Diego, CA: Association for Computational Linguistics. pp. 45-50. doi:10.18653/ v1/W16 -1309. Retrieved 2022-08-06.
^ Serafini, Luciano; Garcez, Artur d’Avila (2016 ), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
^ a b Garcez, Artur d’Avila; Lamb, Luis C.; Gabbay, Dov M. (2009 ). Neural-Symbolic Cognitive Reasoning (1st ed.). Berlin-Heidelberg: Springer. Bibcode:2009 nscr.book … D. doi:10.1007/ 978-3-540-73246-4. ISBN 978-3-540-73245-7. S2CID 14002173.
^ Kiczales, Gregor; Rivieres, Jim des; Bobrow, Daniel G. (1991-07-30). The Art of the Metaobject Protocol (1st ed.). Cambridge, Mass: The MIT Press. ISBN 978-0-262-61074-2.
^ Motik, Boris; Shearer, Rob; Horrocks, Ian (2009-10-28). « Hypertableau Reasoning for Description Logics ». Journal of Expert System Research. 36: 165-228. arXiv:1401.3485. doi:10.1613/ jair.2811. ISSN 1076-9757. S2CID 190609.
^ Kuipers, Benjamin (1994 ). Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press. ISBN 978-0-262-51540-5.
^ Russell & Norvig 2021.
^ Leo de Penning, Artur S. d’Avila Garcez, Luís C. Lamb, John-Jules Ch. Meyer: « A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. » IJCAI 2011: 1653-1658.
^ McCarthy & Hayes 1969.
^ McCarthy 1959.
^ Nilsson 1998, p. 7.
^ Olazaran 1993, pp. 411-416.
^ Olazaran 1993, pp. 415-416.
^ Marcus 2020, p. 20.
^ Garcez & Lamb 2020, p. 8.
^ a b Russell & Norvig 2021, p. 982.
^ Brooks 1991, p. 143.
^ Brooks 1991, p. 151.
^ Brooks 1991, p. 150.
^ Brooks 1991, p. 142.
References

Brooks, Rodney A. (1991 ). « Intelligence without representation ». Expert system. 47 (1 ): 139-159. doi:10.1016/ 0004-3702( 91 )90053-M. ISSN 0004-3702. S2CID 207507849. Retrieved 2022-09-13.
Clancey, William (1987 ). Knowledge-Based Tutoring: The GUIDON Program (MIT Press Series in Artificial Intelligence) (Hardcover ed.).
Crevier, Daniel (1993 ). AI: The Tumultuous Search for Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3.
Dreyfus, Hubert L (1981 ). « From micro-worlds to understanding representation: AI at a deadlock » (PDF). Mind Design. MIT Press, Cambridge, MA: 161-204.
Garcez, Artur S. d’Avila; Broda, Krysia; Gabbay, Dov M.; Gabbay, Augustus de Morgan Professor of Logic Dov M. (2002 ). Neural-Symbolic Learning Systems: Foundations and Applications. Springer Science & Business Media. ISBN 978-1-85233-512-0.
Garcez, Artur; Besold, Tarek; De Raedt, Luc; Földiák, Peter; Hitzler, Pascal; Icard, Thomas; Kühnberger, Kai-Uwe; Lamb, Luís; Miikkulainen, Risto; Silver, Daniel (2015 ). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. AAI Spring Symposium – Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches. Stanford, CA: AAAI Press. doi:10.13140/ 2.1.1779.4243.
Garcez, Artur d’Avila; Gori, Marco; Lamb, Luis C.; Serafini, Luciano; Spranger, Michael; Tran, Son N. (2019 ), Neural-Symbolic Computing: An Efficient Methodology for Principled Integration of Artificial Intelligence and Reasoning, arXiv:1905.06088.
Garcez, Artur d’Avila; Lamb, Luis C. (2020 ), Neurosymbolic AI: The 3rd Wave, arXiv:2012.05876.
Haugeland, John (1985 ), Expert System: The Very Idea, Cambridge, Mass: MIT Press, ISBN 0-262-08153-9.
Hayes-Roth, Frederick; Murray, William; Adelman, Leonard (2015 ). « Expert systems ». AccessScience. doi:10.1036/ 1097-8542.248550.
Honavar, Vasant; Uhr, Leonard (1994 ). Symbolic Artificial Intelligence, Connectionist Networks & Beyond (Technical report). Iowa State University Digital Repository, Computer Technology Technical Reports. 76. p. 6.
Honavar, Vasant (1995 ). Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy. The Springer International Series In Engineering and Computer Technology. Springer US. pp. 351-388. doi:10.1007/ 978-0-585-29599-2_11.
Howe, J. (November 1994). « Expert System at Edinburgh University: a Viewpoint ». Archived from the initial on 15 May 2007. Retrieved 30 August 2007.
Kautz, Henry (2020-02-11). The Third AI Summer, Henry Kautz, AAAI 2020 Robert S. Engelmore Memorial Award Lecture. Retrieved 2022-07-06.
Kautz, Henry (2022 ). « The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture ». AI Magazine. 43 (1 ): 93-104. doi:10.1609/ aimag.v43i1.19122. ISSN 2371-9621. S2CID 248213051. Retrieved 2022-07-12.
Kodratoff, Yves; Michalski, Ryszard, eds. (1990 ). Machine Learning: an Artificial Intelligence Approach. Vol. III. San Mateo, Calif.: Morgan Kaufman. ISBN 0-934613-09-5. OCLC 893488404.
Kolata, G. (1982 ). « How can computers get good sense? ». Science. 217 (4566 ): 1237-1238. Bibcode:1982 Sci … 217.1237 K. doi:10.1126/ science.217.4566.1237. PMID 17837639.
Maker, Meg Houston (2006 ). « AI@50: AI Past, Present, Future ». Dartmouth College. Archived from the initial on 3 January 2007. Retrieved 16 October 2008.
Marcus, Gary; Davis, Ernest (2019 ). Rebooting AI: Building Expert System We Can Trust. New York: Pantheon Books. ISBN 9781524748258. OCLC 1083223029.
Marcus, Gary (2020 ), The Next Decade in AI: Four Steps Towards Robust Expert system, arXiv:2002.06177.
McCarthy, John (1959 ). PROGRAMS WITH GOOD SENSE. Symposium on Mechanization of Thought Processes. NATIONAL PHYSICAL LABORATORY, TEDDINGTON, UK. p. 8.
McCarthy, John; Hayes, Patrick (1969 ). « Some Philosophical Problems From the Standpoint of Artificial Intelligence ». Machine Intelligence 4. B. Meltzer, Donald Michie (eds.): 463-502.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1983 ). Artificial intelligence: an Expert System Approach. Vol. I. Palo Alto, Calif.: Tioga Publishing Company. ISBN 0-935382-05-4. OCLC 9262069.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1986 ). Artificial intelligence: an Expert System Approach. Vol. II. Los Altos, Calif.: Morgan Kaufman. ISBN 0-934613-00-1.
Newell, Allen; Simon, Herbert A. (1972 ). Human Problem Solving (1st ed.). Englewood Cliffs, New Jersey: Prentice Hall. ISBN 0-13-445403-0.
Newell, Allen; Simon, H. A. (1976 ). « Computer Technology as Empirical Inquiry: Symbols and Search ». Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.
Olazaran, Mikel (1993-01-01), « A Sociological History of the Neural Network Controversy », in Yovits, Marshall C. (ed.), Advances in Computers Volume 37, vol. 37, Elsevier, pp. 335-425, doi:10.1016/ S0065-2458( 08 )60408-8, ISBN 9780120121373, recovered 2023-10-31.
Pearl, J. (1988 ). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California: Morgan Kaufmann. ISBN 978-1-55860-479-7. OCLC 249625842.
Russell, Stuart J.; Norvig, Peter (2021 ). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-13-461099-3. LCCN 20190474.
Rossi, Francesca (2022-07-06). « AAAI2022: Thinking Fast and Slow in AI (AAAI 2022 Invited Talk) ». Retrieved 2022-07-06.
Selman, Bart (2022-07-06). « AAAI2022: Presidential Address: The State of AI« . Retrieved 2022-07-06.
Serafini, Luciano; Garcez, Artur d’Avila (2016-07-07), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
Spiegelhalter, David J.; Dawid, A. Philip; Lauritzen, Steffen; Cowell, Robert G. (1993 ). « Bayesian analysis in professional systems ». Statistical Science. 8 (3 ).
Turing, A. M. (1950 ). « I.-Computing Machinery and Intelligence ». Mind. LIX (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 0026-4423. Retrieved 2022-09-14.
Valiant, Leslie G (2008 ). « Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence ». In Hariharan, R.; Mukund, M.; Vinay, V. (eds.). Foundations of Software Technology and Theoretical Computer Technology (Bangalore). pp. 415-422.
Xifan Yao; Jiajun Zhou; Jiangming Zhang; Claudio R. Boer (2017 ). From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Artificial Intelligence and Further On.