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Symbolic Artificial Intelligence

In expert system, symbolic artificial intelligence (likewise referred to as classical synthetic intelligence or logic-based expert system) [1] [2] is the term for the collection of all techniques in synthetic intelligence 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 shows, production guidelines, semantic webs and frames, and it established applications such as knowledge-based systems (in specific, professional systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and restrictions of formal knowledge and reasoning systems.

Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic techniques would ultimately prosper in producing a machine with artificial basic intelligence and considered this the supreme objective of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and guarantees and was followed by the first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) accompanied the increase of professional systems, their promise of catching corporate knowledge, and an enthusiastic business embrace. [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 difficulties in knowledge acquisition, maintaining big knowledge bases, and brittleness in handling out-of-domain problems emerged. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists focused on resolving underlying issues in dealing with uncertainty and in understanding acquisition. [10] Uncertainty was addressed with formal approaches such as concealed Markov models, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic maker finding out addressed the knowledge acquisition issue with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive logic shows to find out relations. [13]

Neural networks, a subsymbolic approach, 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 successful till about 2012: “Until Big Data ended up being prevalent, the general consensus in the Al community was that the so-called neural-network method was hopeless. Systems simply didn’t work that well, compared to other approaches. … A transformation came in 2012, when a variety of people, including a team of scientists working with Hinton, worked out a way to use the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next a number of years, deep knowing had spectacular success in with vision, speech recognition, speech synthesis, image generation, and maker translation. However, given that 2020, as inherent troubles with predisposition, description, coherence, and toughness became more evident with deep learning methods; an increasing number of AI scientists have called for integrating the very best of both the symbolic and neural network techniques [17] [18] and attending to areas that both approaches have problem 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 varying a little for increased clarity.

The very first AI summer season: illogical exuberance, 1948-1966

Success at early efforts in AI took place in three primary locations: synthetic neural networks, understanding representation, and heuristic search, adding to high expectations. This area sums up Kautz’s reprise of early AI history.

Approaches inspired by human or animal cognition or behavior

Cybernetic methods tried to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensing units, motors for driving and guiding, and seven vacuum tubes for control, based upon a preprogrammed neural internet, was built as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, support knowing, 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 was able to show 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to produce a domain-independent issue solver, GPS (General Problem Solver). GPS solved issues represented with official operators via state-space search using means-ends analysis. [21]

During the 1960s, symbolic approaches attained excellent success at replicating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in 4 institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Each one established its own design of research. Earlier approaches based on cybernetics or synthetic neural networks were deserted or pressed into the background.

Herbert Simon and Allen Newell studied human analytical abilities and tried to formalize them, and their work laid the foundations of the field of expert system, in addition to cognitive science, operations research study and management science. Their research group used the results of psychological experiments to develop programs that simulated the strategies that individuals utilized to solve problems. [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 type of knowledge that we will see later on utilized in expert systems, early symbolic AI researchers found another more basic application of knowledge. These were called heuristics, guidelines that direct a search in appealing directions: “How can non-enumerative search be useful when the underlying problem is tremendously tough? The method promoted by Simon and Newell is to utilize heuristics: quick algorithms that may fail on some inputs or output suboptimal options.” [26] Another essential advance was to discover a way to use these heuristics that ensures a service will be discovered, if there is one, not enduring the periodic fallibility of heuristics: “The A * algorithm supplied a general frame for total and optimal heuristically guided search. A * is used as a subroutine within practically every AI algorithm today however is still no magic bullet; its guarantee of efficiency is purchased the cost of worst-case rapid time. [26]

Early work on knowledge representation and thinking

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

Modeling official reasoning with logic: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not need to imitate the exact systems of human idea, however might rather look for the essence of abstract thinking and analytical with reasoning, [27] no matter whether individuals utilized the same algorithms. [a] His lab at Stanford (SAIL) concentrated on utilizing formal reasoning to resolve a wide array of issues, consisting of understanding representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and somewhere else in Europe which caused the advancement of the programs language Prolog and the science of logic shows. [32] [33]

Modeling implicit common-sense understanding with frames and scripts: the “scruffies”

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] discovered that solving difficult problems in vision and natural language processing needed ad hoc solutions-they argued that no easy and general concept (like logic) would capture all the aspects of intelligent habits. Roger Schank described their “anti-logic” methods as “scruffy” (rather than the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, considering that they must be developed by hand, one complicated concept at a time. [38] [39] [40]

The first AI winter: crushed dreams, 1967-1977

The very first AI winter was a shock:

During the very first AI summer, many people believed that machine intelligence could be accomplished in simply a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to use AI to resolve issues of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to create self-governing tanks for the battleground. Researchers had actually started to understand that accomplishing AI was going to be much more difficult than was supposed a decade earlier, but a combination of hubris and disingenuousness led numerous university and think-tank researchers to accept financing with promises of deliverables that they must have understood they might not satisfy. By the mid-1960s neither helpful natural language translation systems nor autonomous tanks had actually been created, and a dramatic backlash embeded in. New DARPA leadership 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 a lot by dissatisfied military leaders as by competing academics who saw AI scientists as charlatans and a drain on research study financing. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the nation. The report stated that all of the problems being worked on in AI would be better handled by researchers from other disciplines-such as used mathematics. The report likewise claimed that AI successes on toy problems could never ever scale to real-world applications due to combinatorial surge. [41]

The 2nd AI summertime: knowledge is power, 1978-1987

Knowledge-based systems

As limitations with weak, domain-independent approaches ended up being more and more evident, [42] scientists from all three traditions started to construct understanding into AI applications. [43] [7] The knowledge revolution was driven by the realization that understanding underlies high-performance, domain-specific AI applications.

Edward Feigenbaum said:

– “In the knowledge lies the power.” [44]
to describe that high efficiency in a specific domain needs both general 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 an intricate job well, it needs to understand a good deal about the world in which it operates.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are two extra capabilities required for intelligent behavior in unexpected situations: falling back on increasingly general knowledge, and analogizing to particular but remote knowledge. [45]

Success with specialist systems

This “knowledge transformation” caused the advancement and release of expert systems (introduced by Edward Feigenbaum), the first commercially successful kind of AI software. [46] [47] [48]

Key professional systems were:

DENDRAL, which found the structure of natural molecules from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested further lab tests, when necessary – by interpreting lab outcomes, patient history, and physician observations. “With about 450 guidelines, MYCIN had the ability to perform as well as some professionals, and significantly much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which tackled internal medicine diagnosis. Internist attempted to capture the know-how of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could eventually detect up to 1000 various diseases.
– GUIDON, which demonstrated how a knowledge base built for professional issue fixing might be repurposed for mentor. [50] XCON, to configure VAX computers, a then tiresome procedure that could take up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is thought about the first specialist system that relied on knowledge-intensive analytical. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

One of the people at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I desired an induction “sandbox”, he said, “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 good at producing the chemical problem area.

We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the contraceptive pill, and also among the world’s most respected mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We started to add to their understanding, creating understanding of engineering as we went along. These experiments totaled up to titrating DENDRAL more and more understanding. The more you did that, the smarter the program ended up being. We had very great outcomes.

The generalization was: in the knowledge lies the power. That was the huge concept. In my career that is the huge, “Ah ha!,” and it wasn’t the way AI was being done formerly. Sounds easy, but it’s probably AI’s most powerful generalization. [51]

The other professional systems mentioned above followed DENDRAL. MYCIN exemplifies the traditional professional system architecture of a knowledge-base of rules paired to a symbolic thinking system, consisting of making use of certainty elements to handle uncertainty. GUIDON demonstrates how an explicit understanding base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a specific type of knowledge-based application. Clancey showed that it was not enough merely to use MYCIN’s rules for direction, but that he likewise needed to include rules for dialogue management and trainee modeling. [50] XCON is considerable due to the fact that of the countless dollars it conserved DEC, which activated the specialist system boom where most all major corporations in the US had skilled systems groups, to catch corporate proficiency, protect it, and automate it:

By 1988, DEC’s AI group had 40 professional systems deployed, with more on the method. DuPont had 100 in usage and 500 in advancement. Nearly every major U.S. corporation had its own Al group and was either utilizing or examining expert systems. [49]

Chess specialist understanding was encoded in Deep Blue. In 1996, this enabled IBM’s Deep Blue, with the help of symbolic AI, to win in a video game of chess versus the world champ at that time, Garry Kasparov. [52]

Architecture of knowledge-based and expert systems

A key element of the system architecture for all expert systems is the understanding base, which stores facts and guidelines for analytical. [53] The most basic method for a skilled system understanding base is merely a collection or network of production rules. Production rules link symbols in a relationship comparable to an If-Then statement. The professional system processes the rules to make reductions and to identify what additional details it requires, i.e. what concerns to ask, utilizing human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools run in this style.

Expert systems can run in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to required data and requirements – way. More innovative knowledge-based systems, such as Soar can also perform meta-level thinking, that is thinking about their own thinking in terms of choosing how to solve issues and monitoring the success of problem-solving methods.

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 resolve a problem. The issue is represented in several levels of abstraction or alternate views. The experts (understanding sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is upgraded as the problem situation modifications. A controller decides how helpful each contribution is, and who must make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was originally motivated by research studies of how humans plan to carry out numerous tasks in a journey. [55] A development of BB1 was to use the exact same blackboard model to resolving its control issue, i.e., its controller carried out meta-level reasoning with knowledge sources that kept track of how well a strategy or the problem-solving was proceeding and could switch from one method to another as conditions – such as objectives or times – changed. BB1 has been applied in numerous domains: construction website planning, smart tutoring systems, and real-time client monitoring.

The 2nd AI winter season, 1988-1993

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

Unfortunately, the AI boom did not last and Kautz best explains the 2nd AI winter season that followed:

Many factors can be used for the arrival of the second AI winter. The hardware business stopped working when much more cost-effective general Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the marketplace. Many commercial implementations of specialist systems were stopped when they showed too costly to keep. Medical specialist systems never ever captured on for numerous factors: the problem in keeping them approximately date; the obstacle for medical experts to learn how to use an overwelming variety of different professional systems for different medical conditions; and perhaps most crucially, the reluctance of medical professionals to trust a computer-made medical diagnosis over their gut instinct, even for particular domains where the specialist systems might outperform an average medical professional. Equity capital cash deserted AI practically overnight. The world AI conference IJCAI hosted a massive and luxurious trade program and countless nonacademic attendees in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]

Adding in more extensive structures, 1993-2011

Uncertain reasoning

Both analytical approaches and extensions to reasoning were tried.

One analytical approach, concealed Markov models, had actually already been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl promoted the usage of Bayesian Networks as a noise however efficient way of dealing with uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were applied effectively in expert systems. [57] Even later, in the 1990s, statistical relational knowing, a method that combines likelihood with logical formulas, enabled probability 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 attempted. For example, non-monotonic reasoning might be utilized with fact maintenance systems. A reality upkeep system tracked assumptions and validations for all inferences. It enabled reasonings to be withdrawn when assumptions were discovered to be inaccurate or a contradiction was obtained. Explanations might be attended to an inference by discussing which guidelines were applied to create it and after that continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had introduced a different sort of extension to handle the representation of ambiguity. For example, in choosing how “heavy” or “high” a man is, there is regularly no clear “yes” or “no” answer, and a predicate for heavy or high would instead return values between 0 and 1. Those worths represented to what degree the predicates were real. His fuzzy reasoning further offered a way for propagating combinations of these values through rational formulas. [59]

Artificial intelligence

Symbolic device discovering techniques were investigated to deal with the understanding acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test method to create plausible rule hypotheses to evaluate against spectra. Domain and task understanding minimized the number of prospects evaluated to a manageable size. Feigenbaum explained Meta-DENDRAL as

… the conclusion of my dream of the early to mid-1960s involving theory development. The conception was that you had a problem solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of understanding to steer and prune the search. That understanding got in there due to the fact that we talked to individuals. But how did the people get the knowledge? By looking at thousands of spectra. So we wanted a program that would look at countless spectra and presume the understanding of mass spectrometry that DENDRAL could utilize to resolve specific hypothesis development problems. We did it. We were even able to publish new knowledge of mass spectrometry in the Journal of the American Chemical Society, providing credit just in a footnote that a program, Meta-DENDRAL, really did it. We were able to do something that had been a dream: to have a computer program developed a new and publishable piece of science. [51]

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

Advances were made in understanding artificial intelligence theory, too. Tom Mitchell presented version area knowing which explains learning as an explore a space of hypotheses, with upper, more general, and lower, more specific, limits including all practical hypotheses consistent with the examples seen up until now. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]

Symbolic device discovering incorporated more than discovering by example. E.g., John Anderson supplied a cognitive model of human knowing where skill practice results in a compilation of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a student may learn to apply “Supplementary angles are two angles whose procedures sum 180 degrees” as numerous different procedural guidelines. E.g., one rule may state that if X and Y are extra and you understand X, then Y will be 180 – X. He called his technique “knowledge compilation”. ACT-R has actually been utilized effectively to model elements of human cognition, such as learning and retention. ACT-R is likewise used in smart tutoring systems, called cognitive tutors, to successfully teach geometry, computer programming, and algebra to school kids. [64]

Inductive reasoning programs was another technique to discovering that enabled reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could synthesize Prolog programs from examples. [65] John R. Koza applied hereditary algorithms to program synthesis to develop hereditary programming, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more basic approach to program synthesis that synthesizes a functional program in the course of showing its requirements to be correct. [66]

As an option to logic, Roger Schank introduced case-based thinking (CBR). The CBR method described in his book, Dynamic Memory, [67] focuses first on remembering crucial analytical cases for future use and generalizing them where appropriate. When faced with a new problem, CBR obtains the most similar previous case and adapts it to the specifics of the present issue. [68] Another option to reasoning, hereditary algorithms and genetic shows are based on an evolutionary design of learning, where sets of guidelines are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of inappropriate guidelines over many generations. [69]

Symbolic artificial intelligence was applied to learning concepts, guidelines, heuristics, and problem-solving. Approaches, aside from those above, consist of:

1. Learning from guideline or advice-i.e., taking human direction, posed as advice, 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 during training. When analytical stops working, querying the specialist to either discover a brand-new prototype for problem-solving or to discover a new description as to exactly why one prototype is more pertinent than another. For example, the program Protos learned to identify tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing problem services based on comparable issues seen in the past, and then modifying their solutions to fit a brand-new scenario or domain. [72] [73] 4. Apprentice knowing systems-learning novel services to issues by observing human problem-solving. Domain knowledge discusses why unique options are correct and how the option can be generalized. LEAP discovered how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to carry out experiments and after that gaining from the outcomes. Doug Lenat’s Eurisko, for instance, discovered heuristics to beat human gamers at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., searching for useful macro-operators to be gained from series of basic analytical actions. Good macro-operators streamline problem-solving by enabling problems to be resolved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now

With the rise of deep learning, the symbolic AI technique has been compared to deep learning as complementary “… with parallels having been drawn sometimes by AI scientists in between Kahneman’s research study on human reasoning and decision making – reflected in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in concept be modelled by deep learning and symbolic thinking, respectively.” In this view, symbolic reasoning is more apt for deliberative thinking, planning, and explanation while deep learning is more apt for quick pattern recognition in perceptual applications with loud data. [17] [18]

Neuro-symbolic AI: integrating neural and symbolic techniques

Neuro-symbolic AI attempts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of thinking, finding out, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the efficient building of abundant computational cognitive designs demands the mix of sound symbolic thinking and efficient (machine) learning models. Gary Marcus, likewise, argues that: “We can not build rich cognitive models in an adequate, automatic way without the set of three of hybrid architecture, abundant prior understanding, and advanced techniques for thinking.”, [79] and in specific: “To develop a robust, knowledge-driven technique to AI we must have the equipment of symbol-manipulation in our toolkit. Excessive of helpful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only equipment that we know of that can manipulate such abstract knowledge reliably is the device of sign adjustment. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based upon a need to resolve the 2 type of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having 2 components, System 1 and System 2. System 1 is quickly, automated, instinctive and unconscious. System 2 is slower, detailed, and specific. System 1 is the kind used for pattern acknowledgment while System 2 is far better fit for preparation, deduction, and deliberative thinking. In this view, deep knowing best designs the very first type of believing while symbolic thinking finest designs the second kind and both are required.

Garcez and Lamb explain research study in this area as being continuous for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking has actually been held every year considering 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 been pursued by a reasonably small research community over the last 2 years and has actually yielded numerous considerable outcomes. 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 reaction to Smolensky (1988 ); see likewise (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and fragments of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of issues in the locations of bioinformatics, control engineering, software application confirmation and adaptation, visual intelligence, ontology knowing, and computer system games. [78]

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

– Symbolic Neural symbolic-is the current technique 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 consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic techniques are used to call neural strategies. In this case the symbolic technique is Monte Carlo tree search and the neural techniques learn how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to translate affective data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to create or label training information that is consequently discovered by a deep learning model, e.g., to train a neural model for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to create or identify examples.
– Neural _ Symbolic -uses a neural web that is produced from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree generated from understanding base rules and terms. Logic Tensor Networks [86] also fall under this classification.
– Neural [Symbolic] -enables a neural model to straight call a symbolic reasoning engine, e.g., to perform an action or assess a state.

Many essential research questions 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 sensible understanding be discovered and reasoned about?
– How can abstract understanding that is hard to encode rationally be managed?

Techniques and contributions

This section supplies a summary of techniques and contributions in an overall context causing lots of other, more comprehensive articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section.

AI shows languages

The key AI shows 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 quick program advancement. Compiled functions could be easily mixed with translated functions. Program tracing, stepping, and breakpoints were also offered, in addition to the ability to change values or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, indicating that the compiler itself was originally written in LISP and after that ran interpretively to assemble the compiler code.

Other crucial innovations originated by LISP that have actually infected other shows languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

Programs were themselves information structures that other programs could run on, permitting the easy definition of higher-level languages.

In contrast to the US, in Europe the essential AI programming language during that same duration was Prolog. Prolog supplied an integrated shop of realities and clauses that might be queried by a read-eval-print loop. The store might function as an understanding base and the clauses might serve as guidelines or a restricted kind of reasoning. As a subset of first-order logic Prolog was based on Horn clauses with a closed-world assumption-any truths not understood were thought about false-and a distinct name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe 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 type of reasoning programming, which was created by Robert Kowalski. Its history was also affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more information see the section on the origins of Prolog in the PLANNER article.

Prolog is also a sort of declarative programs. The reasoning clauses that explain programs are straight translated to run the programs defined. No specific series of actions is required, as holds true with necessary programming languages.

Japan championed Prolog for its Fifth Generation Project, planning to construct special hardware for high efficiency. Similarly, LISP devices were constructed to run LISP, however as the 2nd AI boom turned to bust these companies could not complete with new workstations that could now run LISP or Prolog natively at similar speeds. See the history area for more information.

Smalltalk was another influential AI programs language. For instance, it introduced metaclasses and, along with Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present basic Lisp dialect. CLOS is a Lisp-based object-oriented system that permits multiple inheritance, in addition to incremental extensions to both classes and metaclasses, therefore offering 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 programs language, partly due to its extensive package library that supports data science, natural language processing, and deep learning. Python consists of a read-eval-print loop, practical elements such as higher-order functions, and object-oriented shows that includes metaclasses.

Search

Search arises in lots of kinds of problem fixing, consisting of planning, restriction fulfillment, 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 stipulation 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 reasoning

Multiple various methods to represent knowledge and after that reason with those representations have actually been investigated. Below is a fast introduction of methods to knowledge representation and automated thinking.

Knowledge representation

Semantic networks, conceptual charts, frames, and logic are all methods to modeling knowledge such as domain knowledge, analytical understanding, and the semantic significance of language. Ontologies model crucial ideas 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 likewise be viewed as an ontology. YAGO integrates WordNet as part of its ontology, to line up truths drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.

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

First-order reasoning is more general than description logic. The automated theorem provers gone over below can show theorems in first-order logic. Horn clause reasoning is more restricted than first-order logic and is utilized in reasoning programming languages such as Prolog. Extensions to first-order reasoning consist of temporal logic, to handle time; epistemic logic, to factor about agent understanding; modal logic, to manage possibility and necessity; and probabilistic logics to handle logic and probability together.

Automatic theorem showing

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

Prover9.
ACL2.
Vampire.

Prover9 can be utilized in conjunction with the Mace4 model checker. ACL2 is a theorem prover that can handle 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 an explicit understanding base, normally of rules, to improve reusability throughout domains by separating procedural code and domain understanding. A separate inference engine processes guidelines and adds, deletes, or customizes a knowledge shop.

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

A more versatile sort of problem-solving occurs when thinking about what to do next happens, instead of simply choosing among the available actions. This kind of meta-level thinking is used in Soar and in the BB1 chalkboard architecture.

Cognitive architectures such as ACT-R might have extra abilities, such as the capability to put together often utilized knowledge into higher-level pieces.

Commonsense reasoning

Marvin Minsky first proposed frames as a method of translating common visual situations, 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 record useful sensible knowledge and has “micro-theories” to manage particular kinds of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about naive physics, such as what happens when we heat up a liquid in a pot on the stove. We anticipate it to heat and potentially boil over, even though we may not know its temperature, its boiling point, or other information, such as climatic pressure.

Similarly, Allen’s temporal period algebra is a simplification of thinking about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be solved with constraint 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 solving other sort of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programs can be utilized to resolve scheduling problems, for instance with restraint handling guidelines (CHR).

Automated planning

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

Natural language processing

Natural language processing focuses on treating language as information to carry out tasks such as identifying topics without always understanding the desired meaning. Natural language understanding, on the other hand, constructs a meaning representation and utilizes that for more 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 managed by symbolic AI, however since improved by deep knowing methods. In symbolic AI, discourse representation theory and first-order reasoning have actually been utilized to represent sentence meanings. Latent semantic analysis (LSA) and specific semantic analysis likewise offered vector representations of documents. In the latter case, vector elements are interpretable as principles called by Wikipedia articles.

New deep learning methods based on Transformer models have actually now eclipsed these earlier symbolic AI techniques and obtained modern efficiency in natural language processing. However, Transformer models 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 components is nontransparent.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig’s basic book on artificial intelligence is organized to reflect agent architectures of increasing sophistication. [91] The elegance of representatives differs from simple reactive agents, to those with a model of the world and automated planning abilities, possibly a BDI representative, i.e., one with beliefs, desires, and intents – or alternatively a reinforcement learning model discovered gradually to pick actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep knowing for perception. [92]

In contrast, a multi-agent system consists of numerous agents that interact among themselves with some inter-agent communication 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 amongst the representatives and to increase fault tolerance when representatives are lost. Research issues include how representatives reach consensus, distributed problem solving, multi-agent knowing, multi-agent planning, and dispersed restraint optimization.

Controversies occurred from early 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 however turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were primarily from thinkers, on intellectual grounds, but also from funding companies, specifically throughout the 2 AI winter seasons.

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

Limitations were found in utilizing simple first-order logic to factor about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in offering axioms for what did not alter 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] An easy example takes place in “showing that one person could enter conversation with another”, as an axiom asserting “if an individual has a telephone he still has it after searching for a number in the telephone directory” would be required for the deduction to succeed. Similar axioms would be needed for other domain actions to define what did not alter.

A comparable problem, called the Qualification Problem, takes place in trying to identify the prerequisites for an action to succeed. An unlimited number of pathological conditions can be thought of, e.g., a banana in a tailpipe could avoid a car from operating correctly.

McCarthy’s technique to repair the frame problem was circumscription, a sort of non-monotonic reasoning where deductions could be made from actions that need only define what would alter while not having to clearly define everything that would not alter. Other non-monotonic logics offered truth upkeep systems that modified beliefs resulting in contradictions.

Other methods of handling more open-ended domains included probabilistic reasoning systems and artificial intelligence to find out new principles and rules. McCarthy’s Advice Taker can be considered as an inspiration here, as it could incorporate new understanding supplied by a human in the type of assertions or rules. For instance, experimental symbolic device discovering systems explored the ability to take top-level natural language advice and to interpret it into domain-specific actionable guidelines.

Similar to the issues in dealing with vibrant domains, sensible reasoning is also tough to capture in formal thinking. Examples of sensible reasoning consist of implicit thinking about how people believe or general knowledge of daily events, items, and living animals. This sort of knowledge is considered given and not considered as noteworthy. Common-sense reasoning is an open location of research and challenging both for symbolic systems (e.g., Cyc has actually attempted to capture key parts of this knowledge over more than a decade) and neural systems (e.g., self-driving cars and trucks that do not know not to drive into cones or not to strike pedestrians strolling a bicycle).

McCarthy viewed his Advice Taker as having sensible, however his definition of sensible was different than the one above. [94] He specified a program as having common sense “if it instantly deduces for itself a sufficiently wide class of immediate consequences of anything it is informed and what it currently understands. “

Connectionist AI: philosophical challenges and sociological disputes

Connectionist approaches include 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 operate in deep learning.

Three philosophical positions [96] have been detailed among connectionists:

1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down totally, and connectionist architectures underlie intelligence and are completely enough to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are seen as complementary and both are required for intelligence

Olazaran, in his sociological history of the debates within the neural network community, described the moderate connectionism deem essentially suitable with current research study in neuro-symbolic hybrids:

The 3rd and last position I want to examine here is what I call the moderate connectionist view, a more diverse view of the existing debate in between connectionism and symbolic AI. Among the scientists 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 safeguarded hybrid (partially symbolic, partially connectionist) systems. He claimed that (at least) two sort 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 designs. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative symbol adjustment procedures) the symbolic paradigm provides adequate designs, and not just “approximations” (contrary to what extreme connectionists would claim). [97]

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

To believe that we can merely desert symbol-manipulation is to suspend shock.

And yet, for the many part, that’s how most present AI proceeds. Hinton and lots of others have actually striven to eradicate symbols entirely. The deep knowing hope-seemingly grounded not a lot in science, but in a sort of historic grudge-is that intelligent habits will emerge simply from the confluence of massive data and deep learning. Where classical computers and software resolve tasks by defining sets of symbol-manipulating rules dedicated to specific tasks, such as editing a line in a word processor or performing an estimation in a spreadsheet, neural networks typically try to resolve tasks by statistical approximation and finding out from examples.

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

When deep learning reemerged in 2012, it was with a sort of take-no-prisoners attitude that has identified the majority of the last decade. By 2015, his hostility toward all things symbols had completely crystallized. He lectured at an AI workshop at Stanford comparing symbols to aether, among science’s biggest mistakes.

Since then, his anti-symbolic campaign has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in one of science’s essential journals, Nature. It closed with a direct attack on symbol manipulation, calling not for reconciliation but for outright replacement. Later, Hinton informed a gathering of European Union leaders that investing any further cash in symbol-manipulating approaches was “a substantial mistake,” likening it to purchasing internal combustion engines in the age of electrical vehicles. [98]

Part of these conflicts might be because of unclear terms:

Turing award winner Judea Pearl uses a review of machine learning which, sadly, conflates the terms device learning and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the undertone of the term tends to be that of expert systems dispossessed of any capability to discover. Making use of the terms requires information. Artificial intelligence is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the distinctions to deep knowing being the choice of representation, localist sensible instead of dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not just about production rules composed by hand. A proper meaning of AI concerns knowledge representation and reasoning, self-governing multi-agent systems, preparation and argumentation, as well as knowing. [99]

Situated robotics: the world as a design

Another review of symbolic AI is the embodied cognition method:

The embodied cognition approach claims that it makes no sense to consider the brain independently: 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, consisting of the rest of its body. Under the embodied cognition method, robotics, vision, and other sensors become central, not peripheral. [100]

Rodney Brooks developed behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this approach, is considered as an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or dispersed, as not only unnecessary, but as destructive. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer accomplishes a various function and should function in the real life. For instance, the very first robotic he explains in Intelligence Without Representation, has three layers. The bottom layer translates sonar sensing units to prevent things. The middle layer causes the robotic to wander around when there are no challenges. The top layer triggers the robotic to go to more far-off places for additional exploration. Each layer can momentarily prevent or reduce a lower-level layer. He slammed AI researchers for defining AI problems for their systems, when: “There is no clean division in between perception (abstraction) and thinking in the genuine world.” [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 extremely essential to evaluate the Creatures we develop in the real life; i.e., in the very same world that we humans populate. It is dreadful to fall into the temptation of testing them in a streamlined world initially, even with the best intents of later moving activity to an unsimplified world.” [103] His focus on real-world screening remained in contrast to “Early work in AI concentrated on games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and the usage 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 criticized by the other techniques. Symbolic AI has been criticized as disembodied, responsible to the certification issue, and bad in dealing with the affective problems where deep learning excels. In turn, connectionist AI has actually been slammed as inadequately matched for deliberative step-by-step issue fixing, incorporating knowledge, and dealing with preparation. Finally, Nouvelle AI stands out in reactive and real-world robotics domains however has been criticized for difficulties in including knowing and understanding.

Hybrid AIs including one or more of these methods are currently deemed the course forward. [19] [81] [82] Russell and Norvig conclude that:

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

Expert system.
Automated planning and scheduling
Automated theorem proving
Belief modification
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint programming
Deep learning
First-order reasoning
GOFAI
History of artificial intelligence
Inductive reasoning programming
Knowledge-based systems
Knowledge representation and reasoning
Logic programming
Machine knowing
Model monitoring
Model-based reasoning
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 once stated: “This is AI, so we do not care if it’s mentally real”. [4] McCarthy restated his position in 2006 at the AI@50 conference where he said “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of expert system: one targeted at producing smart habits regardless of how it was achieved, and the other targeted at modeling smart procedures found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig wrote “Aeronautical engineering texts do not specify the objective of their field as making ‘machines that fly so exactly like pigeons that they can trick even other pigeons.'” [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing 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.
^ 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 Zip 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 thresholds of knowledge”. Proceedings of the International Workshop on Expert System 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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