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Symbolic Expert System

In expert system, system (likewise called classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all methods in expert system research that are based upon high-level symbolic (human-readable) representations of problems, logic and search. [3] Symbolic AI used tools such as reasoning shows, production rules, semantic internet and frames, and it developed applications such as knowledge-based systems (in particular, skilled systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to influential concepts in search, symbolic programs languages, agents, multi-agent systems, the semantic web, and the strengths and restrictions of official knowledge and thinking systems.

Symbolic AI was the dominant paradigm of AI research study from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were convinced that symbolic techniques would eventually prosper in developing a machine with artificial general intelligence and considered this the ultimate objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in impractical expectations and pledges and was followed by the very first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) took place with the increase of expert systems, their pledge of capturing business knowledge, and an enthusiastic business accept. [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 understanding acquisition, keeping large understanding bases, and brittleness in handling out-of-domain issues emerged. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on resolving hidden problems in handling uncertainty and in understanding acquisition. [10] Uncertainty was attended to with formal techniques such as hidden Markov models, Bayesian reasoning, and analytical relational knowing. [11] [12] Symbolic device discovering attended to the knowledge acquisition issue with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree knowing, case-based learning, and inductive reasoning shows to find out relations. [13]

Neural networks, a subsymbolic method, had actually been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not viewed as successful until about 2012: “Until Big Data ended up being commonplace, the general consensus in the Al community was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other techniques. … A transformation was available in 2012, when a number of people, including a team of scientists dealing with Hinton, worked out a method to utilize the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next several years, deep learning had amazing success in handling vision, speech recognition, speech synthesis, image generation, and maker translation. However, because 2020, as intrinsic troubles with predisposition, explanation, coherence, and robustness ended up being more apparent with deep learning methods; an increasing number of AI researchers have called for combining the very best of both the symbolic and neural network methods [17] [18] and addressing locations that both methods have trouble with, such as common-sense reasoning. [16]

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

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

Success at early efforts in AI took place in three primary locations: synthetic neural networks, knowledge representation, and heuristic search, contributing to high expectations. This area sums up 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 steering, and seven vacuum tubes for control, based on a preprogrammed neural net, was developed as early as 1948. This work can be seen as an early precursor to later operate in neural networks, reinforcement learning, and positioned 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 prove 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to create a domain-independent issue solver, GPS (General Problem Solver). GPS solved problems represented with official operators by means of state-space search utilizing means-ends analysis. [21]

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

Herbert Simon and Allen Newell studied human analytical skills and tried to formalize them, and their work laid the foundations of the field of artificial intelligence, along with cognitive science, operations research and management science. Their research group utilized the outcomes of mental experiments to develop programs that simulated the methods that individuals utilized to resolve problems. [22] [23] This custom, focused at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the center 1980s. [24] [25]

Heuristic search

In addition to the extremely specialized domain-specific sort of understanding that we will see later on used in specialist systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, rules of thumb that direct a search in appealing directions: “How can non-enumerative search be useful when the underlying issue is significantly difficult? The method promoted by Simon and Newell is to employ heuristics: quick algorithms that may stop working on some inputs or output suboptimal solutions.” [26] Another crucial advance was to discover a method to use these heuristics that guarantees a service will be found, if there is one, not holding up against the periodic fallibility of heuristics: “The A * algorithm supplied a basic frame for total and ideal heuristically directed search. A * is utilized as a subroutine within virtually every AI algorithm today however is still no magic bullet; its assurance of efficiency is purchased the expense of worst-case exponential time. [26]

Early deal with knowledge representation and reasoning

Early work covered both applications of formal reasoning stressing first-order reasoning, along with efforts to handle sensible thinking in a less official way.

Modeling formal reasoning with reasoning: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not need to imitate the specific systems of human thought, however could instead search for the essence of abstract reasoning and problem-solving with logic, [27] despite whether individuals used the same algorithms. [a] His lab at Stanford (SAIL) concentrated on using official logic to solve a wide range of problems, including knowledge representation, planning and learning. [31] Logic was also the focus of the work at the University of Edinburgh and in other places in Europe which resulted in the development of the shows language Prolog and the science of logic programming. [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 issues in vision and natural language processing required advertisement hoc solutions-they argued that no simple and general principle (like logic) would record all the elements of intelligent behavior. Roger Schank explained their “anti-logic” approaches as “scruffy” (instead of the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, considering that they need to be developed by hand, one complex idea at a time. [38] [39] [40]

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

The first AI winter was a shock:

During the very first AI summertime, many individuals thought that maker intelligence could be accomplished in simply a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research study to utilize AI to solve issues of national security; in particular, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battleground. Researchers had actually begun to recognize that achieving AI was going to be much harder than was expected a years previously, however a mix of hubris and disingenuousness led lots of university and think-tank researchers to accept funding with promises of deliverables that they must have understood they could not meet. By the mid-1960s neither useful natural language translation systems nor self-governing tanks had been created, and a significant reaction set in. New DARPA management canceled existing AI financing programs.

Outside of the United States, the most fertile ground for AI research was the United Kingdom. The AI winter season in the UK was spurred on not a lot by dissatisfied military leaders as by rival academics who saw AI researchers as charlatans and a drain on research study financing. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research study in the country. The report mentioned that all of the problems being dealt with in AI would be better managed by researchers from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues could never ever scale to real-world applications due to combinatorial explosion. [41]

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

Knowledge-based systems

As restrictions with weak, domain-independent techniques ended up being a growing number of evident, [42] scientists from all three traditions started to build understanding into AI applications. [43] [7] The knowledge transformation was driven by the awareness that understanding underlies high-performance, domain-specific AI applications.

Edward Feigenbaum said:

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

( 1) The Knowledge Principle: if a program is to perform a complex job well, it needs to understand a great deal about the world in which it runs.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are two extra capabilities needed for smart habits in unforeseen scenarios: falling back on increasingly general knowledge, and analogizing to specific but distant understanding. [45]

Success with professional systems

This “understanding revolution” resulted in the development and deployment of expert systems (introduced by Edward Feigenbaum), the very first commercially effective form of AI software. [46] [47] [48]

Key expert systems were:

DENDRAL, which discovered the structure of organic molecules from their chemical formula and mass spectrometer readings.
MYCIN, which detected bacteremia – and recommended more laboratory tests, when necessary – by translating lab results, client history, and doctor observations. “With about 450 guidelines, MYCIN had the ability to carry out in addition to some specialists, and significantly better than junior doctors.” [49] INTERNIST and CADUCEUS which took on internal medicine medical diagnosis. Internist tried to catch the proficiency of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately diagnose as much as 1000 different illness.
– GUIDON, which demonstrated how a knowledge base developed for professional issue resolving might be repurposed for mentor. [50] XCON, to configure VAX computers, a then laborious procedure that could take up to 90 days. XCON minimized the time to about 90 minutes. [9]
DENDRAL is considered the first specialist system that relied on knowledge-intensive analytical. It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

Among the individuals at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. 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 great at producing the chemical problem area.

We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, creator of the chemical behind the birth control pill, and likewise one of the world’s most respected mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We started to include to their understanding, inventing understanding of engineering as we went along. These experiments amounted to titrating DENDRAL a growing number of understanding. The more you did that, the smarter the program ended up being. We had really great outcomes.

The generalization was: in the understanding lies the power. That was the big concept. In my profession that is the big, “Ah ha!,” and it wasn’t the way AI was being done previously. Sounds basic, but it’s most likely AI’s most effective generalization. [51]

The other professional systems pointed out above came after DENDRAL. MYCIN exhibits the classic professional system architecture of a knowledge-base of rules coupled to a symbolic reasoning mechanism, consisting of making use of certainty aspects to manage 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 kind of knowledge-based application. Clancey revealed that it was not enough just to utilize MYCIN’s guidelines for direction, however that he likewise required to add rules for discussion management and trainee modeling. [50] XCON is significant because of the countless dollars it saved DEC, which activated the professional system boom where most all significant corporations in the US had expert systems groups, to record business competence, preserve it, and automate it:

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

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

Architecture of knowledge-based and expert systems

An essential component of the system architecture for all expert systems is the understanding base, which stores realities and rules for problem-solving. [53] The easiest technique for a skilled system understanding base is just a collection or network of production guidelines. Production rules connect symbols in a relationship similar to an If-Then declaration. The expert system processes the rules to make deductions and to determine what additional details it requires, i.e. what concerns to ask, using human-readable symbols. For example, OPS5, CLIPS and their followers Jess and Drools run in this style.

Expert systems can run in either a forward chaining – from evidence to conclusions – or backward chaining – from objectives to required data and requirements – way. Advanced knowledge-based systems, such as Soar can also perform meta-level thinking, that is thinking about their own thinking in regards to deciding how to solve problems and keeping an eye on the success of problem-solving techniques.

Blackboard systems are a 2nd kind of knowledge-based or skilled system architecture. They design a neighborhood of specialists incrementally contributing, where they can, to resolve a problem. The problem 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 updated as the issue situation changes. A controller chooses how helpful each contribution is, and who must make the next analytical action. One example, the BB1 chalkboard architecture [54] was originally influenced by research studies of how human beings plan to perform several tasks in a journey. [55] A development of BB1 was to apply the same chalkboard model to resolving its control issue, i.e., its controller carried out meta-level thinking with knowledge sources that monitored how well a strategy or the analytical was proceeding and might change from one method to another as conditions – such as goals or times – changed. BB1 has been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time patient tracking.

The 2nd AI winter season, 1988-1993

At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were offering LISP machines specifically targeted to accelerate the advancement of AI applications and research. In addition, a number of synthetic intelligence companies, 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 finest describes the 2nd AI winter that followed:

Many reasons can be used for the arrival of the 2nd AI winter. The hardware companies failed when much more cost-effective basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the market. Many commercial releases of professional systems were ceased when they proved too pricey to keep. Medical expert systems never caught on for a number of reasons: the trouble in keeping them as much as date; the obstacle for medical experts to find out how to utilize a bewildering range of various expert systems for various medical conditions; and possibly most crucially, the hesitation of medical professionals to rely on a computer-made medical diagnosis over their gut instinct, even for particular domains where the professional systems might exceed an average medical professional. Venture capital cash deserted AI almost over night. The world AI conference IJCAI hosted a huge and extravagant trade convention and thousands of nonacademic attendees in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]

Adding in more extensive foundations, 1993-2011

Uncertain thinking

Both statistical techniques and extensions to reasoning were tried.

One analytical technique, concealed Markov models, had actually already been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a sound but efficient way of managing uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were used effectively in professional systems. [57] Even later on, in the 1990s, statistical relational knowing, a method that combines likelihood with logical solutions, enabled 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 assistance were also tried. For instance, non-monotonic thinking might be utilized with fact maintenance systems. A fact maintenance system tracked presumptions and justifications for all inferences. It enabled reasonings to be withdrawn when presumptions were learnt to be inaccurate or a contradiction was derived. Explanations could be offered for an inference by explaining which rules were applied to develop it and then continuing through underlying reasonings and guidelines all the way back to root assumptions. [58] Lofti Zadeh had actually introduced a different kind of extension to manage the representation of ambiguity. For instance, in choosing how “heavy” or “tall” a male is, there is regularly no clear “yes” or “no” response, and a predicate for heavy or high would rather return worths between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy logic even more supplied a means for propagating combinations of these values through logical formulas. [59]

Artificial intelligence

Symbolic machine discovering techniques were examined to attend to the understanding acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to generate plausible rule hypotheses to test versus spectra. Domain and task knowledge lowered the variety of candidates evaluated to a workable size. Feigenbaum explained Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s relating to theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it utilized layers of understanding to guide and prune the search. That understanding acted due to the fact that we talked to individuals. But how did the individuals get the understanding? By looking at countless spectra. So we wanted a program that would take a look at thousands of spectra and presume the knowledge of mass spectrometry that DENDRAL might utilize to resolve individual hypothesis development issues. We did it. We were even able to release brand-new knowledge of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, really did it. We had the ability to do something that had been a dream: to have a computer system program come up with a new and publishable piece of science. [51]

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

Advances were made in comprehending artificial intelligence theory, too. Tom Mitchell introduced version space learning which explains learning as an explore a space of hypotheses, with upper, more basic, and lower, more particular, borders incorporating all viable hypotheses constant with the examples seen up until now. [62] More officially, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of device knowing. [63]

Symbolic machine finding out included more than finding out by example. E.g., John Anderson offered a cognitive model of human knowing where ability practice results in a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a trainee might find out to use “Supplementary angles are 2 angles whose steps sum 180 degrees” as several different procedural rules. E.g., one guideline might say that if X and Y are additional and you know X, then Y will be 180 – X. He called his technique “knowledge collection”. ACT-R has actually been used effectively to design aspects of human cognition, such as discovering and retention. ACT-R is also used in smart tutoring systems, called cognitive tutors, to successfully teach geometry, computer system programs, and algebra to school kids. [64]

Inductive reasoning programming was another method to discovering that enabled 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 develop genetic programming, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic approach to program synthesis that manufactures a functional program in the course of proving its specs to be appropriate. [66]

As an option to reasoning, Roger Schank presented case-based thinking (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses initially on keeping in mind essential analytical cases for future use and generalizing them where suitable. When faced with a brand-new problem, CBR recovers the most comparable previous case and adapts it to the specifics of the current problem. [68] Another option to reasoning, hereditary algorithms and genetic shows are based on an evolutionary model of learning, where sets of guidelines are encoded into populations, the rules govern the behavior of individuals, and choice of the fittest prunes out sets of inappropriate rules over lots of generations. [69]

Symbolic artificial intelligence was used to finding out ideas, guidelines, heuristics, and analytical. Approaches, other than those above, include:

1. Learning from instruction or advice-i.e., taking human guideline, impersonated suggestions, and identifying how to operationalize it in specific circumstances. For example, in a game of Hearts, learning precisely how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter professional (SME) feedback during training. When problem-solving fails, querying the expert to either discover a brand-new exemplar for analytical or to discover a new explanation as to precisely why one exemplar is more pertinent than another. For example, the program Protos found out to diagnose tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing issue options based on similar issues seen in the past, and after that customizing their solutions to fit a new scenario or domain. [72] [73] 4. Apprentice learning systems-learning unique services to problems by observing human analytical. Domain understanding explains why novel solutions are proper and how the service can be generalized. LEAP learned how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating jobs to bring out experiments and then gaining from the results. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human gamers at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., browsing for useful macro-operators to be found out from series of standard problem-solving actions. Good macro-operators simplify analytical by allowing problems to be resolved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

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

Neuro-symbolic AI: integrating neural and symbolic approaches

Neuro-symbolic AI attempts to integrate 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 reasoning, discovering, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the effective building of rich computational cognitive models requires the mix of sound symbolic reasoning and effective (maker) knowing designs. Gary Marcus, similarly, argues that: “We can not build abundant cognitive models in a sufficient, automated method without the triune of hybrid architecture, rich prior knowledge, and advanced strategies for reasoning.”, [79] and in specific: “To develop a robust, knowledge-driven method to AI we should have the machinery of symbol-manipulation in our toolkit. Too much of beneficial knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only equipment that we know of that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based on a need to resolve the two kinds of believing gone over 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 quick, automatic, instinctive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind utilized for pattern recognition while System 2 is far better fit for planning, reduction, and deliberative thinking. In this view, deep knowing best designs the very first kind of thinking while symbolic reasoning best designs the second kind and both are needed.

Garcez and Lamb describe research in this location as being ongoing for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic learning systems. [84] A series of workshops on neuro-symbolic thinking has been held every year because 2005, see http://www.neural-symbolic.org/ for details.

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

The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively small research study neighborhood over the last twenty years and has yielded a number of considerable outcomes. Over the last years, neural symbolic systems have been shown capable of getting rid of 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 pieces of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of problems in the locations of bioinformatics, control engineering, software confirmation and adjustment, visual intelligence, ontology knowing, and computer system games. [78]

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

– Symbolic Neural symbolic-is the existing approach of numerous neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language designs. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic methods are utilized to call neural techniques. In this case the symbolic approach is Monte Carlo tree search and the neural strategies find out how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to analyze perceptual information as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to generate or identify training data that is consequently discovered by a deep knowing design, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to produce 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 produced from knowledge base guidelines and terms. Logic Tensor Networks [86] likewise fall into this category.
– Neural [Symbolic] -permits a neural model to straight call a symbolic reasoning engine, e.g., to carry out an action or evaluate a state.

Many essential research study questions stay, such as:

– What is the finest method to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and drawn out from them?
– How should common-sense understanding be found out and reasoned about?
– How can abstract understanding that is hard to encode realistically be handled?

Techniques and contributions

This area offers an overview of techniques and contributions in a general context resulting in many other, more in-depth articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section.

AI shows languages

The essential AI shows language in the US during the last symbolic AI boom period was LISP. LISP is the 2nd earliest programming language after FORTRAN and was developed in 1958 by John McCarthy. LISP provided the very first read-eval-print loop to support rapid program advancement. Compiled functions could be easily blended with translated functions. Program tracing, stepping, and breakpoints were likewise supplied, along with the ability to alter values or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, suggesting that the compiler itself was initially composed in LISP and then ran interpretively to put together the compiler code.

Other essential developments originated by LISP that have spread to other shows languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the essential AI shows language during that very same period was Prolog. Prolog supplied a built-in store of facts and provisions that might be queried by a read-eval-print loop. The shop might act as an understanding base and the stipulations could function as rules or a restricted form of logic. As a subset of first-order reasoning Prolog was based upon Horn stipulations with a closed-world assumption-any facts not understood were thought about false-and a distinct name presumption for primitive terms-e.g., the identifier barack_obama was considered to refer to exactly one item. Backtracking and marriage are built-in to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a kind of logic shows, which was created by Robert Kowalski. Its history was likewise influenced 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 also a sort of declarative shows. The logic clauses that explain programs are straight analyzed to run the programs specified. No explicit series of actions is required, as holds true with crucial programs languages.

Japan championed Prolog for its Fifth Generation Project, planning to build unique hardware for high efficiency. Similarly, LISP devices were developed to run LISP, however as the 2nd AI boom turned to bust these companies could not take on brand-new workstations that could now run LISP or Prolog natively at similar speeds. See the history area for more detail.

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

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

Search

Search develops in many sort of problem resolving, including planning, restriction 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 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 understanding and then reason with those representations have been investigated. Below is a quick overview of methods to knowledge representation and automated thinking.

Knowledge representation

Semantic networks, conceptual graphs, frames, and reasoning are all methods to modeling knowledge such as domain understanding, problem-solving knowledge, and the semantic significance of language. Ontologies design essential 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 also be viewed as an ontology. YAGO includes WordNet as part of its ontology, to align realities drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.

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

First-order reasoning is more general than description reasoning. The automated theorem provers talked about below can prove theorems in first-order reasoning. Horn stipulation logic is more restricted than first-order reasoning and is used in reasoning programming languages such as Prolog. Extensions to first-order logic include temporal reasoning, to handle time; epistemic reasoning, to factor about agent understanding; modal reasoning, to deal with possibility and need; and probabilistic reasonings to handle reasoning and possibility together.

Automatic theorem proving

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

Prover9.
ACL2.
Vampire.

Prover9 can be used in conjunction with the Mace4 design checker. ACL2 is a theorem prover that can deal with proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise referred to as Nqthm.

Reasoning in knowledge-based systems

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

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

A more versatile type of analytical takes place when thinking about what to do next takes place, instead of simply selecting one of the offered actions. This sort of meta-level reasoning is utilized in Soar and in the BB1 chalkboard architecture.

Cognitive architectures such as ACT-R might have additional abilities, such as the capability to compile regularly used understanding into higher-level chunks.

Commonsense reasoning

Marvin Minsky first proposed frames as a way of interpreting typical visual scenarios, such as an office, and Roger Schank extended this concept to scripts for typical routines, such as eating in restaurants. Cyc has attempted to record beneficial sensible understanding and has “micro-theories” to manage particular type of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human reasoning about naive physics, such as what occurs when we heat a liquid in a pot on the stove. We expect it to heat and perhaps boil over, even though we may not understand its temperature, its boiling point, or other information, such as climatic pressure.

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

Constraints and constraint-based thinking

Constraint solvers perform a more limited sort of inference than first-order logic. They can streamline sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, in addition to resolving other sort of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programs can be utilized to solve scheduling issues, for instance with restraint handling guidelines (CHR).

Automated planning

The General Problem Solver (GPS) cast preparation as problem-solving utilized means-ends analysis to develop strategies. STRIPS took a different method, seeing preparation as theorem proving. Graphplan takes a least-commitment method to planning, instead of sequentially picking actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is an approach to planning where a planning issue is decreased to a Boolean satisfiability issue.

Natural language processing

Natural language processing concentrates on dealing with language as data to carry out jobs such as determining topics without always understanding the desired significance. Natural language understanding, in contrast, constructs a significance representation and uses that for further processing, such as answering questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long dealt with by symbolic AI, but considering that improved by deep knowing methods. In symbolic AI, discourse representation theory and first-order reasoning have been utilized to represent sentence significances. Latent semantic analysis (LSA) and explicit semantic analysis likewise supplied vector representations of files. In the latter case, vector parts are interpretable as concepts called by Wikipedia articles.

New deep knowing approaches based upon Transformer designs have actually now eclipsed these earlier symbolic AI methods and obtained advanced efficiency in natural language processing. However, Transformer designs 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 components is opaque.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s basic textbook on artificial intelligence is organized to show agent architectures of increasing sophistication. [91] The elegance of representatives varies from simple reactive agents, to those with a model of the world and automated preparation capabilities, perhaps a BDI representative, i.e., one with beliefs, desires, and intents – or additionally a support finding out model learned in time to choose actions – as much as a combination of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep learning for understanding. [92]

In contrast, a multi-agent system consists of multiple 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 exact same internal architecture. Advantages of multi-agent systems consist of the capability to divide work among the agents and to increase fault tolerance when agents are lost. Research issues consist of how representatives reach agreement, dispersed problem fixing, multi-agent learning, multi-agent preparation, and distributed constraint optimization.

Controversies arose 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 embraced AI but turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mostly from thinkers, on intellectual premises, but likewise from financing agencies, specifically throughout the two AI winters.

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

Limitations were discovered in utilizing simple first-order reasoning to reason about dynamic domains. Problems were found both with regards to specifying the prerequisites for an action to be successful and in providing axioms for what did not alter after an action was performed.

McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] An easy example happens in “proving that one person might enter into conversation with another”, as an axiom asserting “if an individual has a telephone he still has it after looking up a number in the telephone directory” would be required for the reduction to prosper. Similar axioms would be needed for other domain actions to define what did not change.

A similar issue, called the Qualification Problem, happens in trying to mention the preconditions for an action to prosper. An infinite number of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a car from running properly.

McCarthy’s approach to repair the frame issue was circumscription, a kind of non-monotonic reasoning where reductions could be made from actions that need only specify what would change while not having to explicitly specify whatever that would not alter. Other non-monotonic reasonings supplied reality maintenance systems that modified beliefs leading to contradictions.

Other methods of handling more open-ended domains included probabilistic thinking systems and artificial intelligence to find out brand-new principles and guidelines. McCarthy’s Advice Taker can be considered as a motivation here, as it could incorporate new knowledge offered by a human in the type of assertions or guidelines. For instance, speculative symbolic machine discovering systems checked out the capability to take high-level natural language suggestions and to interpret it into domain-specific actionable guidelines.

Similar to the issues in handling dynamic domains, sensible reasoning is likewise difficult to record in formal reasoning. Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of daily occasions, things, and living creatures. This kind of knowledge is taken for approved and not considered as noteworthy. Common-sense thinking is an open area of research and challenging both for symbolic systems (e.g., Cyc has attempted to record key parts of this knowledge over more than a years) and neural systems (e.g., self-driving automobiles that do not understand not to drive into cones or not to hit pedestrians walking a bike).

McCarthy viewed his Advice Taker as having sensible, but his definition of sensible was different than the one above. [94] He specified a program as having typical sense “if it instantly deduces for itself an adequately wide class of instant effects of anything it is informed and what it already knows. “

Connectionist AI: philosophical challenges and sociological conflicts

Connectionist techniques include 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 implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected totally, and connectionist architectures underlie intelligence and are fully sufficient to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are seen as complementary and both are needed for intelligence

Olazaran, in his sociological history of the controversies within the neural network community, explained the moderate connectionism view as essentially suitable with existing research in neuro-symbolic hybrids:

The third and last position I wish to examine here is what I call the moderate connectionist view, a more diverse view of the existing argument in between connectionism and symbolic AI. Among the scientists who has elaborated this position most explicitly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partially symbolic, partially connectionist) systems. He declared 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 benefits over symbolic designs. But on the other hand, for other cognitive processes (such as serial, deductive reasoning, and generative sign adjustment procedures) the symbolic paradigm uses appropriate models, and not just “approximations” (contrary to what extreme connectionists would claim). [97]

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

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

And yet, for the a lot of part, that’s how most existing AI earnings. Hinton and numerous others have attempted difficult to eradicate symbols altogether. The deep knowing hope-seemingly grounded not a lot in science, however in a sort of historic grudge-is that intelligent habits will emerge purely from the confluence of huge information and deep knowing. Where classical computer systems and software application resolve jobs by defining sets of symbol-manipulating rules dedicated to particular tasks, such as editing a line in a word processor or carrying out an estimation in a spreadsheet, neural networks generally try to solve tasks by statistical approximation and gaining from examples.

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

When deep knowing reemerged in 2012, it was with a sort of take-no-prisoners mindset that has defined most of the last decade. By 2015, his hostility toward all things signs had actually completely crystallized. He offered a talk at an AI workshop at Stanford comparing signs to aether, one of science’s greatest errors.

…

Since then, his anti-symbolic campaign has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s crucial journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation but for straight-out replacement. Later, Hinton told a gathering of European Union leaders that investing any additional cash in symbol-manipulating methods was “a big mistake,” likening it to buying internal combustion engines in the period of electrical vehicles. [98]

Part of these disagreements might be because of uncertain terminology:

Turing award winner Judea Pearl uses a critique of artificial intelligence which, unfortunately, 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 professional systems dispossessed of any capability to find out. Making use of the terms is in need of information. Machine learning is not restricted to association rule mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep knowing being the option 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 written by hand. A correct meaning of AI concerns knowledge representation and reasoning, autonomous multi-agent systems, preparation and argumentation, along with knowing. [99]

Situated robotics: the world as a model

Another critique of symbolic AI is the embodied cognition approach:

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

Rodney Brooks invented behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this approach, is viewed as an alternative to both symbolic AI and connectionist AI. His technique rejected representations, either symbolic or distributed, as not just unneeded, but as detrimental. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer achieves a different purpose and needs to work in the real world. For example, the first robotic he describes in Intelligence Without Representation, has 3 layers. The bottom layer translates finder sensing units to prevent things. The middle layer causes the robot to wander around when there are no barriers. The leading layer triggers the robot to go to more remote places for further exploration. Each layer can temporarily hinder or suppress a lower-level layer. He criticized AI scientists for specifying AI problems for their systems, when: “There is no clean division in between perception (abstraction) and reasoning in the real life.” [101] He called his robots “Creatures” and each layer was “made up of a fixed-topology network of easy limited state machines.” [102] In the Nouvelle AI method, “First, it is extremely essential to check the Creatures we integrate in the real life; i.e., in the exact same world that we human beings populate. It is devastating to fall under the temptation of testing them in a simplified world initially, even with the very best intents of later transferring activity to an unsimplified world.” [103] His emphasis on real-world screening was in contrast to “Early operate in AI concentrated on games, geometrical issues, symbolic algebra, theorem proving, and other official systems” [104] and using the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has advantages, but has actually been criticized by the other techniques. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and bad in managing the affective issues where deep finding out excels. In turn, connectionist AI has been slammed as inadequately fit for deliberative detailed problem resolving, incorporating understanding, and managing planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains however has actually been criticized for troubles in incorporating knowing and understanding.

Hybrid AIs including several of these methods are presently considered as 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 for that reason impossible; we now see much of these same areas going through continued research and development causing increased capability, not impossibility. [100]

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

Notes

^ McCarthy when stated: “This is AI, so we don’t care if it’s mentally genuine”. [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he said “Expert system is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 major branches of artificial intelligence: one focused on producing intelligent habits regardless of how it was achieved, and the other aimed at modeling smart processes discovered in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the goal 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 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.
^ 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 knowing with symbolic synthetic intelligence: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. 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.
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^ 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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