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Symbolic Artificial Intelligence

In expert system, symbolic artificial intelligence (likewise referred to as classical expert system or logic-based expert system) [1] [2] is the term for the collection of all methods in expert system research that are based on top-level symbolic (human-readable) representations of problems, logic and search. [3] Symbolic AI utilized tools such as reasoning programs, production rules, semantic internet and frames, and it established applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programs languages, representatives, multi-agent systems, the semantic web, and the strengths and restrictions of formal understanding 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 encouraged that symbolic techniques would eventually be successful in creating a maker with artificial basic intelligence and considered this the supreme goal of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, led to unrealistic expectations and guarantees and was followed by the very first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) happened with the increase of professional systems, their promise of catching corporate knowledge, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later disappointment. [8] Problems with problems in knowledge acquisition, keeping large knowledge bases, and brittleness in dealing with out-of-domain issues occurred. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists focused on attending to hidden problems in managing uncertainty and in knowledge acquisition. [10] Uncertainty was resolved with official approaches such as concealed Markov models, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic machine discovering attended to the understanding acquisition problem with contributions including Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive logic programming to discover relations. [13]

Neural networks, a subsymbolic technique, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron learning 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 till about 2012: “Until Big Data became prevalent, the basic consensus in the Al community was that the so-called neural-network technique was helpless. Systems simply didn’t work that well, compared to other methods. … A revolution can be found in 2012, when a variety of people, consisting of a group of researchers dealing with Hinton, worked out a way to utilize the power of GPUs to immensely increase the power of neural networks.” [16] Over the next numerous years, deep learning had amazing success in dealing with vision, speech acknowledgment, speech synthesis, image generation, and machine translation. However, considering that 2020, as inherent problems with bias, description, coherence, and effectiveness became more apparent with deep learning methods; an increasing variety of AI scientists have actually called for combining the very best of both the symbolic and neural network methods [17] [18] and resolving areas that both approaches have difficulty with, such as sensible reasoning. [16]

A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia article on the History of AI, with dates and titles differing somewhat for increased clarity.

The very first AI summer: unreasonable enthusiasm, 1948-1966

Success at early attempts in AI happened in three main locations: synthetic neural networks, knowledge representation, and heuristic search, contributing to high expectations. This area summarizes Kautz’s reprise of early AI history.

Approaches inspired by human or animal cognition or habits

Cybernetic techniques tried to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensing units, motors for driving and guiding, and 7 vacuum tubes for control, based upon a preprogrammed neural net, was constructed as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, support learning, and located robotics. [20]

An important 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 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to produce a domain-independent problem solver, GPS (General Problem Solver). GPS fixed issues represented with official operators through state-space search utilizing means-ends analysis. [21]

During the 1960s, symbolic approaches achieved fantastic success at replicating smart behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in 4 organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one established its own style of research. Earlier techniques based upon cybernetics or artificial neural networks were abandoned or pressed into the background.

Herbert Simon and Allen Newell studied human analytical abilities and attempted to formalize them, and their work laid the structures of the field of synthetic intelligence, in addition to cognitive science, operations research study and management science. Their research group used the results of psychological experiments to establish programs that simulated the strategies that individuals utilized to fix issues. [22] [23] This custom, centered at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the middle 1980s. [24] [25]

Heuristic search

In addition to the extremely specialized domain-specific kinds of knowledge that we will see later on used in expert systems, early symbolic AI researchers discovered another more basic application of understanding. These were called heuristics, general rules that direct a search in appealing instructions: “How can non-enumerative search be practical when the underlying issue is exponentially difficult? The technique promoted by Simon and Newell is to employ heuristics: fast algorithms that may stop working on some inputs or output suboptimal services.” [26] Another essential advance was to discover a way to use these heuristics that ensures a service will be found, if there is one, not enduring the occasional fallibility of heuristics: “The A * algorithm supplied a general frame for complete and ideal heuristically guided search. A * is utilized as a subroutine within practically every AI algorithm today but is still no magic bullet; its warranty of completeness is bought at the expense of worst-case exponential time. [26]

Early deal with knowledge representation and thinking

Early work covered both applications of official reasoning highlighting first-order reasoning, along with attempts to deal with common-sense reasoning in a less formal manner.

Modeling official thinking with reasoning: the “neats”

Unlike Simon and Newell, John McCarthy felt that devices did not need to replicate the precise mechanisms of human idea, but could rather look for the essence of abstract reasoning and analytical with logic, [27] despite whether individuals utilized the same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official logic to fix a wide range of problems, consisting of knowledge representation, preparation and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and elsewhere in Europe which caused the development of the shows language Prolog and the science of reasoning programs. [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] found that solving difficult issues in vision and natural language processing required ad hoc solutions-they argued that no basic and general principle (like logic) would record all the aspects of smart habits. Roger Schank described their “anti-logic” techniques as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, because they need to be constructed by hand, one complex principle at a time. [38] [39] [40]

The first AI winter season: crushed dreams, 1967-1977

The very first AI winter was a shock:

During the first AI summer season, many individuals thought that maker intelligence might be accomplished in just a couple of years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research to use AI to solve problems of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battlefield. Researchers had started to realize that achieving AI was going to be much more difficult than was expected a years previously, but a mix of hubris and disingenuousness led numerous university and think-tank scientists to accept funding with guarantees of deliverables that they should have known they might not fulfill. By the mid-1960s neither helpful natural language translation systems nor self-governing tanks had actually been produced, and a dramatic backlash embeded in. New DARPA leadership canceled existing AI financing programs.

Outside of the United States, the most fertile ground for AI research was the UK. The AI winter season in the UK was spurred on not so much by dissatisfied military leaders as by rival academics who viewed AI researchers as charlatans and a drain on research study funding. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research in the nation. The report stated that all of the problems being worked on in AI would be much better handled by scientists from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues might never scale to real-world applications due to combinatorial explosion. [41]

The 2nd AI summer: understanding is power, 1978-1987

Knowledge-based systems

As limitations with weak, domain-independent techniques became a growing number of apparent, [42] researchers from all 3 customs began to construct understanding into AI applications. [43] [7] The understanding revolution was driven by the awareness that knowledge underlies high-performance, domain-specific AI applications.

Edward Feigenbaum said:

– “In the understanding lies the power.” [44]
to explain that high efficiency in a specific domain needs both basic and extremely domain-specific understanding. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:

( 1) The Knowledge Principle: if a program is to carry out a complicated job well, it should understand a good deal about the world in which it operates.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are 2 additional abilities essential for intelligent behavior in unexpected scenarios: falling back on significantly basic understanding, and analogizing to specific but far-flung knowledge. [45]

Success with professional systems

This “understanding revolution” caused the advancement and release of professional systems (introduced by Edward Feigenbaum), the very first commercially successful form of AI software application. [46] [47] [48]

Key expert systems were:

DENDRAL, which discovered the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and recommended further lab tests, when essential – by analyzing lab results, patient history, and physician observations. “With about 450 rules, MYCIN was able to perform along with some specialists, and considerably better than junior physicians.” [49] INTERNIST and CADUCEUS which tackled internal medicine medical diagnosis. Internist attempted to record the proficiency of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS could ultimately detect approximately 1000 different illness.
– GUIDON, which showed how an understanding base developed for professional problem resolving might be repurposed for mentor. [50] XCON, to set up VAX computers, a then laborious procedure that could use up to 90 days. XCON minimized the time to about 90 minutes. [9]
DENDRAL is thought about the very first expert system that count on knowledge-intensive problem-solving. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

Among the people 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 just the one for you.” His laboratory was doing mass spectrometry of amino acids. The concern 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 began the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was great at creating the chemical problem space.

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 appreciated mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We started to contribute to their understanding, creating 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 excellent results.

The generalization was: in the understanding lies the power. That was the huge concept. In my career that is the huge, “Ah ha!,” and it wasn’t the method AI was being done previously. Sounds simple, however it’s probably AI‘s most effective generalization. [51]

The other expert systems discussed above followed DENDRAL. MYCIN exhibits the classic professional system architecture of a knowledge-base of guidelines combined to a symbolic reasoning mechanism, including making use of certainty aspects to deal with unpredictability. GUIDON demonstrates how an explicit knowledge base can be repurposed for a 2nd application, tutoring, and is an example of an intelligent tutoring system, a specific type of knowledge-based application. Clancey showed that it was not sufficient simply to utilize MYCIN’s guidelines for direction, however that he likewise needed to add rules for dialogue management and trainee modeling. [50] XCON is considerable since of the millions of dollars it conserved DEC, which triggered the professional system boom where most all major corporations in the US had skilled systems groups, to catch corporate competence, preserve it, and automate it:

By 1988, DEC’s AI group had 40 expert systems released, with more on the way. DuPont had 100 in use and 500 in advancement. Nearly every major U.S. corporation had its own Al group and was either using or investigating expert systems. [49]

Chess professional knowledge was encoded in Deep Blue. In 1996, this allowed IBM’s Deep Blue, with the help of symbolic AI, to win in a video game of chess against the world champion at that time, Garry Kasparov. [52]

Architecture of knowledge-based and expert systems

An essential component of the system architecture for all specialist systems is the knowledge base, which stores truths and guidelines for problem-solving. [53] The simplest technique for a professional system understanding base is simply a collection or network of production guidelines. Production rules link signs in a relationship comparable to an If-Then declaration. The expert system processes the guidelines to make deductions and to identify what extra info it requires, i.e. what concerns to ask, utilizing human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this style.

Expert systems can operate in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to required information and requirements – manner. More innovative knowledge-based systems, such as Soar can likewise carry out meta-level thinking, that is thinking about their own reasoning in regards to choosing how to fix issues and keeping track of the success of problem-solving techniques.

Blackboard systems are a 2nd kind of knowledge-based or professional system architecture. They design a neighborhood of experts incrementally contributing, where they can, to resolve an issue. The issue is represented in several levels of abstraction or alternate views. The professionals (understanding sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is upgraded as the issue circumstance changes. A controller chooses how helpful each contribution is, and who need to make the next problem-solving action. One example, the BB1 blackboard architecture [54] was originally influenced by research studies of how human beings plan to carry out numerous tasks in a journey. [55] An innovation of BB1 was to apply the very same blackboard design to fixing its control problem, i.e., its controller carried out meta-level reasoning with knowledge sources that kept track of how well a plan or the problem-solving was continuing and could change from one strategy to another as conditions – such as objectives or times – altered. BB1 has actually been applied in multiple domains: construction website preparation, intelligent tutoring systems, and real-time client tracking.

The second AI winter season, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP makers particularly targeted to speed up the advancement of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling professional system shells, training, and consulting to corporations.

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

Many reasons can be offered for the arrival of the 2nd AI winter. The hardware companies failed when far more cost-effective basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the marketplace. Many commercial releases of specialist systems were discontinued when they showed too costly to maintain. Medical professional systems never captured on for a number of factors: the problem in keeping them as much as date; the obstacle for medical specialists to learn how to utilize an overwelming range of various expert systems for different medical conditions; and maybe most crucially, the reluctance of physicians to rely on a computer-made medical diagnosis over their gut instinct, even for particular domains where the specialist systems could outshine an average medical professional. Equity capital money deserted AI practically overnight. The world AI conference IJCAI hosted a huge and luxurious trade convention and countless nonacademic guests in 1987 in Vancouver; the primary AI conference the following year, AAAI 1988 in St. Paul, was a little and strictly scholastic affair. [9]

Adding in more strenuous structures, 1993-2011

Uncertain thinking

Both analytical approaches and extensions to reasoning were attempted.

One analytical approach, concealed Markov designs, had currently been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized the usage of Bayesian Networks as a sound but efficient way of dealing with unpredictable reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were applied successfully in specialist systems. [57] Even later on, in the 1990s, analytical relational knowing, a technique that integrates probability with sensible formulas, permitted probability to be combined 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 likewise tried. For example, non-monotonic reasoning could be used with reality upkeep systems. A fact maintenance system tracked presumptions and validations for all reasonings. It permitted inferences to be withdrawn when assumptions were found out to be incorrect or a contradiction was derived. Explanations might be attended to a reasoning by discussing which rules were applied to create it and then continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had actually introduced a different type of extension to deal with the representation of vagueness. For example, in choosing how “heavy” or “high” a man is, there is often no clear “yes” or “no” response, and a predicate for heavy or tall would instead return values between 0 and 1. Those values represented to what degree the predicates were true. His fuzzy reasoning even more supplied a way for propagating mixes of these worths through logical formulas. [59]

Machine learning

Symbolic maker finding out methods were examined to address the knowledge acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test strategy to produce possible guideline hypotheses to evaluate versus spectra. Domain and task knowledge reduced the number of prospects checked to a workable size. Feigenbaum described Meta-DENDRAL as

… the culmination of my imagine 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 utilized layers of knowledge to steer and prune the search. That knowledge got in there because we talked to individuals. But how did individuals get the understanding? By looking at countless spectra. So we desired a program that would look at countless spectra and infer the understanding of mass spectrometry that DENDRAL might use to resolve specific hypothesis formation issues. We did it. We were even able to publish new understanding of mass spectrometry in the Journal of the American Chemical Society, providing credit just in a footnote that a program, Meta-DENDRAL, in fact did it. We were able to do something that had actually been a dream: to have a computer system program developed a new and publishable piece of science. [51]

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

Advances were made in comprehending artificial intelligence theory, too. Tom Mitchell presented version space knowing which describes learning as an explore a space of hypotheses, with upper, more general, and lower, more particular, borders incorporating all viable hypotheses constant with the examples seen so far. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]

Symbolic machine discovering encompassed more than finding out by example. E.g., John Anderson provided a cognitive design of human learning where skill practice results in a collection of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a student may find out to use “Supplementary angles are 2 angles whose measures sum 180 degrees” as several different procedural guidelines. E.g., one guideline may state that if X and Y are extra and you understand X, then Y will be 180 – X. He called his method “understanding collection”. ACT-R has actually been used successfully to design elements of human cognition, such as learning and retention. ACT-R is also used in smart tutoring systems, called cognitive tutors, to successfully teach geometry, computer shows, and algebra to school children. [64]

Inductive logic programs was another technique to learning that permitted logic 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 used genetic algorithms to program synthesis to create genetic programs, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger supplied a more general method to program synthesis that synthesizes a functional program in the course of proving its requirements to be right. [66]

As an option to logic, Roger Schank introduced case-based thinking (CBR). The CBR approach laid out in his book, Dynamic Memory, [67] focuses initially on keeping in mind key problem-solving cases for future usage and generalizing them where suitable. When faced with a new problem, CBR retrieves the most similar previous case and adapts it to the specifics of the existing issue. [68] Another alternative to reasoning, genetic algorithms and genetic shows are based upon an evolutionary design of learning, where sets of guidelines are encoded into populations, the rules govern the behavior of people, and selection of the fittest prunes out sets of unsuitable guidelines over numerous generations. [69]

Symbolic maker knowing was applied to discovering concepts, rules, heuristics, and analytical. Approaches, besides those above, include:

1. Learning from guideline or advice-i.e., taking human instruction, impersonated recommendations, and identifying how to operationalize it in specific situations. For example, in a game of Hearts, learning precisely how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter specialist (SME) feedback throughout training. When problem-solving fails, querying the expert to either find out a brand-new prototype for problem-solving or to find out a brand-new explanation regarding exactly why one exemplar is more appropriate than another. For instance, the program Protos discovered to diagnose tinnitus cases by communicating with an audiologist. [71] 3. Learning by analogy-constructing problem services based upon comparable issues seen in the past, and then customizing their solutions to fit a brand-new scenario or domain. [72] [73] 4. Apprentice knowing systems-learning novel solutions to problems by observing human analytical. Domain understanding explains why novel services are appropriate and how the solution can be generalized. LEAP learned how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing tasks to bring out experiments and then learning from the outcomes. Doug Lenat’s Eurisko, for instance, learned heuristics to beat human players at the Traveller role-playing video game for two years in a row. [75] 6. Learning macro-operators-i.e., looking for useful macro-operators to be discovered from series of standard analytical actions. Good macro-operators simplify problem-solving by enabling problems to be resolved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the increase of deep learning, the symbolic AI approach has been compared to deep knowing as complementary “… with parallels having actually been drawn sometimes by AI researchers between Kahneman’s research study on human thinking 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 reasoning is more apt for deliberative thinking, planning, and description while deep learning is more apt for quick pattern recognition in affective applications with loud information. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic approaches

Neuro-symbolic AI efforts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI capable of reasoning, finding out, and cognitive modeling. As argued by Valiant [77] and many others, [78] the efficient construction of rich computational cognitive models demands the mix of sound symbolic reasoning and effective (device) learning designs. Gary Marcus, similarly, argues that: “We can not construct rich cognitive models in an appropriate, automated method without the triumvirate of hybrid architecture, abundant prior understanding, and advanced methods 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 knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we understand of that can control such abstract knowledge dependably is the device of symbol control. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based upon a need to address the two sort of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 elements, System 1 and System 2. System 1 is quickly, automatic, instinctive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern acknowledgment while System 2 is far much better fit for planning, deduction, and deliberative thinking. In this view, deep learning finest designs the very first kind of thinking while symbolic thinking finest models the 2nd kind and both are required.

Garcez and Lamb describe research study in this area as being continuous 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 actually been held every year because 2005, see http://www.neural-symbolic.org/ for information.

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

The combination of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively little research study neighborhood over the last 2 decades and has actually yielded several significant outcomes. Over the last decade, neural symbolic systems have actually been revealed capable of getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see likewise (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and fragments of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of problems in the areas of bioinformatics, control engineering, software verification and adjustment, visual intelligence, ontology learning, and computer system games. [78]

Approaches for integration are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, along with some examples, follows:

– Symbolic Neural symbolic-is the current method of many neural models in natural language processing, where words or subword tokens are both the supreme input and output of big language models. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic methods are used to call neural techniques. In this case the symbolic technique is Monte Carlo tree search and the neural techniques discover how to assess game positions.
– Neural|Symbolic-uses a neural architecture to translate perceptual data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to generate or identify training data that is consequently found out by a deep learning model, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to produce or label examples.
– Neural _ Symbolic -uses a neural net that is created from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree produced from understanding base guidelines and terms. Logic Tensor Networks [86] likewise fall under this category.
– Neural [Symbolic] -permits a neural model to directly call a symbolic reasoning engine, e.g., to perform an action or assess a state.

Many essential research concerns remain, such as:

– What is the best method to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should common-sense understanding be learned and reasoned about?
– How can abstract knowledge that is hard to encode rationally be dealt with?

Techniques and contributions

This area offers an introduction of techniques and contributions in a total context leading to lots of other, more detailed articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history area.

AI shows languages

The essential AI programs language in the US during the last symbolic AI boom period was LISP. LISP is the second earliest shows language after FORTRAN and was created in 1958 by John McCarthy. LISP supplied the first read-eval-print loop to support rapid program development. Compiled functions might be easily combined with analyzed functions. Program tracing, stepping, and breakpoints were likewise supplied, together with the capability to change values or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, indicating that the compiler itself was originally composed in LISP and then ran interpretively to put together the compiler code.

Other essential developments originated by LISP that have infected other programs languages include:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

Programs were themselves data structures that other programs might run on, enabling the easy definition of higher-level languages.

In contrast to the US, in Europe the crucial AI shows language during that same period was Prolog. Prolog offered a built-in shop of realities and stipulations that might be queried by a read-eval-print loop. The store might function as a knowledge base and the clauses might act as guidelines or a restricted form of logic. As a subset of first-order logic Prolog was based upon Horn clauses with a closed-world assumption-any facts not known were considered false-and an unique name presumption for primitive terms-e.g., the identifier barack_obama was thought about to describe exactly one things. Backtracking and marriage are built-in to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of logic programs, which was invented 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 area on the origins of Prolog in the PLANNER article.

Prolog is likewise a type of declarative programming. The reasoning clauses that explain programs are straight analyzed to run the programs defined. No explicit series of actions is needed, as is the case with necessary shows languages.

Japan championed Prolog for its Fifth Generation Project, meaning to construct special hardware for high performance. Similarly, LISP makers were constructed to run LISP, but 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 similar speeds. See the history area for more information.

Smalltalk was another influential AI programming language. For example, it presented metaclasses and, together with Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the current standard Lisp dialect. CLOS is a Lisp-based object-oriented system that allows multiple inheritance, in addition to incremental extensions to both classes and metaclasses, therefore offering a run-time meta-object protocol. [88]

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

Search

Search emerges in many type of problem solving, consisting of preparation, constraint satisfaction, and playing games such as checkers, chess, and go. The very best known 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 provision learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and thinking

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

Knowledge representation

Semantic networks, conceptual charts, frames, and logic are all methods to modeling knowledge such as domain knowledge, problem-solving understanding, and the semantic meaning of language. Ontologies design key principles and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can likewise be deemed an ontology. YAGO includes WordNet as part of its ontology, to line up realities extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being utilized.

Description logic is a logic for automated category of ontologies and for spotting inconsistent classification data. OWL is a language utilized to represent ontologies with description logic. Protégé is an ontology editor that can read in OWL ontologies and after that check consistency with deductive classifiers such as such as HermiT. [89]

First-order logic is more general than description logic. The automated theorem provers gone over below can prove theorems in first-order reasoning. Horn clause logic is more limited than first-order reasoning and is utilized in reasoning programming languages such as Prolog. Extensions to first-order reasoning include temporal logic, to manage time; epistemic logic, to reason about representative knowledge; modal logic, to handle possibility and need; and probabilistic reasonings to deal with reasoning and possibility together.

Automatic theorem proving

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 manage proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, also known as Nqthm.

Reasoning in knowledge-based systems

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

Forward chaining reasoning engines are the most common, and are seen in CLIPS and OPS5. Backward chaining takes place in Prolog, where a more minimal sensible representation is utilized, Horn Clauses. Pattern-matching, specifically marriage, is used in Prolog.

A more versatile kind of analytical occurs when thinking about what to do next happens, rather than simply selecting one of the readily available actions. This sort of meta-level thinking is used in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R may have extra capabilities, such as the capability to compile regularly utilized understanding into higher-level chunks.

Commonsense reasoning

Marvin Minsky first proposed frames as a way of interpreting common visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for typical regimens, such as eating in restaurants. Cyc has actually tried to capture beneficial common-sense understanding and has “micro-theories” to handle particular type of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human reasoning about ignorant physics, such as what takes place when we warm a liquid in a pot on the stove. We expect it to heat and potentially boil over, even though we might not know its temperature, its boiling point, or other information, such as atmospheric pressure.

Similarly, Allen’s temporal period algebra is a simplification of thinking about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be fixed with restriction solvers.

Constraints and constraint-based thinking

Constraint solvers carry out a more limited sort of reasoning than first-order reasoning. They can streamline sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, together with resolving other sort of puzzle issues, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint logic programming can be used to resolve scheduling issues, for instance with constraint handling guidelines (CHR).

Automated planning

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

Natural language processing

Natural language processing concentrates on treating language as data to carry out tasks such as determining subjects without always comprehending the intended significance. Natural language understanding, on the other hand, constructs a meaning representation and uses that for more processing, such as answering concerns.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long managed by symbolic AI, however since enhanced by deep learning techniques. In symbolic AI, discourse representation theory and first-order reasoning have actually been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also offered vector representations of files. In the latter case, vector elements are interpretable as principles called by Wikipedia posts.

New deep learning approaches based upon Transformer designs have now eclipsed these earlier symbolic AI techniques and achieved state-of-the-art efficiency in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the significance of the vector parts is opaque.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s standard book on expert system is organized to show representative architectures of increasing sophistication. [91] The elegance of representatives differs from easy reactive agents, to those with a model of the world and automated planning abilities, perhaps a BDI agent, i.e., one with beliefs, desires, and intents – or alternatively a reinforcement finding out design found out with time to pick actions – approximately 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 representatives that interact amongst themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The representatives need not all have the same internal architecture. Advantages of multi-agent systems include the ability to divide work among the representatives and to increase fault tolerance when agents are lost. Research issues consist of how representatives reach agreement, distributed problem fixing, multi-agent learning, multi-agent preparation, and dispersed constraint optimization.

Controversies occurred from at an early stage in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and in between those who welcomed AI but turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mainly from theorists, on intellectual grounds, but likewise from funding agencies, specifically during the two AI winters.

The Frame Problem: knowledge representation obstacles for first-order reasoning

Limitations were found in utilizing basic first-order reasoning to factor about vibrant domains. Problems were found both with concerns to enumerating the prerequisites for an action to be successful and in providing axioms for what did not alter after an action was carried out.

McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A basic example takes place in “showing that one person 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 deduction to succeed. Similar axioms would be required for other domain actions to define what did not alter.

A comparable issue, called the Qualification Problem, happens in attempting to identify the prerequisites for an action to prosper. An unlimited number of pathological conditions can be pictured, e.g., a banana in a tailpipe might prevent an automobile from operating correctly.

McCarthy’s method to repair the frame problem was circumscription, a kind of non-monotonic logic where deductions might be made from actions that require just define what would change while not needing to clearly specify whatever that would not alter. Other non-monotonic reasonings provided reality maintenance systems that modified beliefs causing contradictions.

Other methods of dealing with more open-ended domains included probabilistic reasoning systems and machine knowing to learn brand-new concepts and guidelines. McCarthy’s Advice Taker can be viewed as a motivation here, as it might integrate new knowledge offered by a human in the kind of assertions or guidelines. For instance, speculative symbolic machine finding out systems explored the capability to take top-level natural language guidance and to analyze it into domain-specific actionable guidelines.

Similar to the issues in dealing with vibrant domains, sensible thinking is also challenging to catch in formal thinking. Examples of sensible reasoning consist of implicit thinking about how people believe or general understanding of day-to-day events, things, and living creatures. This sort of knowledge is considered approved and not seen as noteworthy. Common-sense reasoning is an open location of research study and challenging both for symbolic systems (e.g., Cyc has actually attempted to catch crucial 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 hit pedestrians walking a bicycle).

McCarthy saw his Advice Taker as having common-sense, but his definition of sensible was different than the one above. [94] He specified a program as having common sense “if it automatically deduces for itself a sufficiently wide class of immediate repercussions of anything it is told and what it currently knows. “

Connectionist AI: philosophical obstacles and sociological conflicts

Connectionist methods consist of earlier work on neural networks, [95] such as perceptrons; operate 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 methods, such as Transformers, GANs, and other work in deep knowing.

Three philosophical positions [96] have actually been outlined amongst connectionists:

1. Implementationism-where connectionist architectures carry out the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down absolutely, and connectionist architectures underlie intelligence and are completely sufficient to describe it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are viewed as complementary and both are needed for intelligence

Olazaran, in his sociological history of the debates within the neural network neighborhood, described the moderate connectionism consider as basically compatible with existing research study in neuro-symbolic hybrids:

The third and last position I would like to analyze here is what I call the moderate connectionist view, a more diverse view of the current dispute between connectionism and symbolic AI. One of the scientists who has elaborated this position most clearly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark safeguarded hybrid (partly symbolic, partially connectionist) systems. He declared that (at least) two kinds of theories are needed in order to study and design cognition. On the one hand, for some information-processing tasks (such as pattern acknowledgment) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative symbol manipulation processes) the symbolic paradigm offers sufficient models, and not only “approximations” (contrary to what radical connectionists would claim). [97]

Gary Marcus has claimed that the animus in the deep knowing community against symbolic methods now may be more sociological than philosophical:

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

And yet, for the a lot of part, that’s how most present AI proceeds. Hinton and numerous others have actually striven to get rid of signs entirely. The deep learning hope-seemingly grounded not so much in science, but in a sort of historical grudge-is that intelligent behavior will emerge simply from the confluence of huge information and deep knowing. Where classical computers and software application solve tasks by defining sets of symbol-manipulating guidelines committed to particular jobs, such as modifying a line in a word processor or performing a computation in a spreadsheet, neural networks generally try to fix jobs by statistical approximation and learning from examples.

According to Marcus, Geoffrey Hinton and his associates have actually been vehemently “anti-symbolic”:

When deep knowing reemerged in 2012, it was with a type of take-no-prisoners attitude that has characterized most of the last years. By 2015, his hostility towards all things signs had fully crystallized. He lectured at an AI workshop at Stanford comparing symbols to aether, among science’s biggest errors.

Ever since, his anti-symbolic campaign has actually only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in one of science’s crucial journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation however for straight-out replacement. Later, Hinton informed a gathering of European Union leaders that investing any more money in symbol-manipulating approaches was “a substantial mistake,” comparing it to buying internal combustion engines in the period of electrical automobiles. [98]

Part of these conflicts may be because of uncertain terminology:

Turing award winner Judea Pearl uses a review of artificial intelligence which, regrettably, conflates the terms machine learning and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of specialist systems dispossessed of any capability to learn. Using the terminology is in need of clarification. Artificial intelligence is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep knowing being the choice of representation, localist logical rather than dispersed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not almost production guidelines composed by hand. A correct meaning of AI concerns knowledge representation and thinking, autonomous multi-agent systems, planning and argumentation, in addition to learning. [99]

Situated robotics: the world as a model

Another critique of symbolic AI is the embodied cognition technique:

The embodied cognition technique declares that it makes no sense to think about the brain individually: cognition happens within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s operating exploits regularities in its environment, consisting of the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensors become main, not peripheral. [100]

Rodney Brooks created behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this method, is viewed as an alternative to both symbolic AI and connectionist AI. His approach declined representations, either symbolic or distributed, as not just unneeded, but as harmful. Instead, he created the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a various function and needs to function in the real world. For example, the first robotic he explains in Intelligence Without Representation, has 3 layers. The bottom layer analyzes sonar sensors to avoid things. The middle layer triggers the robot to wander around when there are no challenges. The leading layer causes the robot to go to more far-off places for further exploration. Each layer can briefly prevent or suppress a lower-level layer. He criticized AI scientists for specifying AI issues for their systems, when: “There is no clean division 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 easy limited state makers.” [102] In the Nouvelle AI technique, “First, it is critically important to evaluate the Creatures we build in the real life; i.e., in the exact same world that we people occupy. It is dreadful to fall under the temptation of checking them in a streamlined world initially, even with the very best intentions of later moving activity to an unsimplified world.” [103] His emphasis on real-world screening remained 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, but has been slammed by the other approaches. Symbolic AI has been slammed as disembodied, accountable to the qualification issue, and poor in handling the affective problems where deep discovering excels. In turn, connectionist AI has been slammed as poorly fit for deliberative detailed issue resolving, including understanding, and dealing with preparation. Finally, Nouvelle AI stands out in reactive and real-world robotics domains but has actually been slammed for problems in integrating knowing and knowledge.

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

Overall, Dreyfus saw areas where AI did not have total answers and said that Al is therefore impossible; we now see a lot of these very same locations going through 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 programs
Deep knowing
First-order logic
GOFAI
History of expert system
Inductive reasoning shows
Knowledge-based systems
Knowledge representation and thinking
Logic programming
Machine knowing
Model checking
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of artificial intelligence
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet

Notes

^ McCarthy as soon as stated: “This is AI, so we don’t care if it’s psychologically genuine”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he stated “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck composes that there are “2 significant branches of expert system: one targeted at producing intelligent behavior no matter how it was achieved, and the other targeted at modeling intelligent processes found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig wrote “Aeronautical engineering texts do not define the goal of their field as making ‘machines that fly so exactly like pigeons that they can fool even other pigeons.'” [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing 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.
^ 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 items 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 understanding”. 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.
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