Symbolic Artificial Intelligence

In artificial intelligence, symbolic expert system (likewise referred to as classical artificial intelligence or logic-based synthetic intelligence) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based upon high-level symbolic (human-readable) representations of issues, reasoning and search. [3] Symbolic AI used tools such as logic programs, 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 preparation and scheduling systems. The Symbolic AI paradigm caused seminal concepts in search, symbolic programs 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 up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were convinced that symbolic techniques would ultimately be successful in developing a maker with artificial basic 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, led to unrealistic expectations and pledges and was followed by the very first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) happened with the increase of expert systems, their guarantee of catching business know-how, and a passionate business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later on dissatisfaction. [8] Problems with difficulties in knowledge acquisition, preserving big understanding bases, and brittleness in handling out-of-domain problems emerged. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists focused on attending to underlying problems in managing unpredictability and in understanding acquisition. [10] Uncertainty was addressed with formal methods such as surprise Markov models, Bayesian thinking, and analytical relational learning. [11] [12] Symbolic maker finding out attended to the understanding acquisition problem with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree learning, case-based knowing, and inductive logic programs to find out relations. [13]

Neural networks, a subsymbolic approach, had been pursued from early days and reemerged strongly 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 viewed as effective till about 2012: “Until Big Data ended up being prevalent, the basic agreement in the Al community was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other approaches. … A transformation came in 2012, when a number of individuals, including a group of researchers dealing with Hinton, worked out a method to utilize the power of GPUs to immensely increase the power of neural networks.” [16] Over the next a number of years, deep learning had amazing success in handling vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, considering that 2020, as fundamental problems with predisposition, explanation, coherence, and toughness became more evident with deep learning methods; an increasing number of AI researchers have required integrating the very best of both the symbolic and neural network approaches [17] [18] and addressing locations that both methods have trouble with, such as common-sense thinking. [16]

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

The first AI summertime: irrational liveliness, 1948-1966

Success at early attempts in AI took place in three main areas: synthetic neural networks, knowledge representation, and heuristic search, contributing to high expectations. This section sums up Kautz’s reprise of early AI history.

Approaches influenced by human or animal cognition or behavior

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

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

During the 1960s, symbolic techniques accomplished fantastic success at imitating 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. Every one established its own design of research. Earlier approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.

Herbert Simon and Allen Newell studied human problem-solving skills and tried to formalize them, and their work laid the foundations of the field of expert system, along with cognitive science, operations research study and management science. Their research study team utilized the outcomes of psychological experiments to establish programs that simulated the methods that people utilized to solve problems. [22] [23] This custom, focused at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the center 1980s. [24] [25]

Heuristic search

In addition to the highly specialized domain-specific kinds of knowledge that we will see later on used in professional systems, early symbolic AI scientists discovered another more basic application of understanding. These were called heuristics, general rules that guide a search in appealing directions: “How can non-enumerative search be useful when the underlying problem is tremendously hard? The approach advocated by Simon and Newell is to use heuristics: fast algorithms that might fail on some inputs or output suboptimal solutions.” [26] Another crucial advance was to discover a method to use these heuristics that ensures an option will be found, if there is one, not withstanding the occasional fallibility of heuristics: “The A * algorithm offered a general frame for total and optimal heuristically guided search. A * is used as a subroutine within almost every AI algorithm today but is still no magic bullet; its guarantee of completeness is bought at the expense of worst-case rapid time. [26]

Early deal with knowledge representation and reasoning

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

Modeling formal reasoning with logic: the “neats”

Unlike Simon and Newell, John McCarthy felt that machines did not require to replicate the exact mechanisms of human thought, however could rather attempt to discover the essence of abstract reasoning and problem-solving with reasoning, [27] regardless of whether people used the exact same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official reasoning to solve a variety of problems, including understanding 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 advancement of the programming language Prolog and the science of logic shows. [32] [33]

Modeling implicit sensible understanding with frames and scripts: the “scruffies”

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that fixing challenging problems in vision and natural language processing needed ad hoc solutions-they argued that no basic and basic principle (like reasoning) would capture all the elements of smart habits. Roger Schank explained their “anti-logic” approaches as “shabby” (as opposed to the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they should be developed by hand, one complex concept at a time. [38] [39] [40]

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

The very first AI winter was a shock:

During the very first AI summer, many individuals believed that machine intelligence might be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) released 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 produce autonomous tanks for the battleground. Researchers had actually begun to understand that achieving AI was going to be much more difficult than was supposed a decade earlier, but a mix of hubris and disingenuousness led many university and think-tank scientists to accept funding with guarantees of deliverables that they should have known they might not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had been produced, and a significant backlash embeded in. New DARPA management canceled existing AI funding programs.

Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the United Kingdom was stimulated on not a lot by dissatisfied military leaders as by competing 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 assess the state of AI research study in the country. The report stated that all of the problems being worked on in AI would be much better managed by researchers from other disciplines-such as applied mathematics. The report likewise declared that AI successes on toy problems might never scale to real-world applications due to combinatorial surge. [41]

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

Knowledge-based systems

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

Edward Feigenbaum stated:

– “In the knowledge lies the power.” [44]
to explain that high performance in a particular domain requires both general and highly 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 task well, it must know an excellent deal about the world in which it operates.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are two additional capabilities necessary for intelligent habits in unexpected situations: drawing on progressively general understanding, and analogizing to specific however remote understanding. [45]

Success with expert systems

This “understanding transformation” led to the development and release of specialist systems (introduced by Edward Feigenbaum), the first commercially successful form of AI software. [46] [47] [48]

Key professional systems were:

DENDRAL, which found the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and recommended more laboratory tests, when needed – by analyzing laboratory outcomes, client history, and doctor observations. “With about 450 guidelines, MYCIN was able to carry out as well as some specialists, and substantially much better than junior doctors.” [49] INTERNIST and CADUCEUS which took on internal medicine diagnosis. Internist tried to catch the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately diagnose as much as 1000 various diseases.
– GUIDON, which revealed how a knowledge base built for expert issue resolving might be repurposed for teaching. [50] XCON, to configure VAX computers, a then laborious process that might take up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is considered the very first specialist system that depend on knowledge-intensive analytical. It is explained below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

One of the individuals at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I wanted an induction “sandbox”, he stated, “I have simply the one for you.” His laboratory 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 proficient at heuristic search methods, and he had an algorithm that was proficient at producing the chemical problem space.

We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control tablet, and likewise among the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate experts in mass spectrometry. We started to add to their knowledge, 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 became. We had great outcomes.

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

The other specialist systems mentioned above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of rules coupled to a symbolic reasoning system, consisting of the usage of certainty aspects to manage uncertainty. GUIDON reveals how an explicit knowledge base can be repurposed for a 2nd application, tutoring, and is an example of an intelligent tutoring system, a specific type of knowledge-based application. Clancey showed that it was not enough simply to utilize MYCIN’s rules for instruction, but that he likewise required to include rules for discussion management and trainee modeling. [50] XCON is considerable due to the fact that of the millions of dollars it saved DEC, which triggered the professional system boom where most all significant corporations in the US had professional systems groups, to capture corporate knowledge, maintain it, and automate it:

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

Chess specialist knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the aid 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 professional systems is the knowledge base, which shops facts and guidelines for analytical. [53] The simplest method for an expert system understanding base is merely a collection or network of production rules. Production guidelines connect symbols in a relationship similar to an If-Then declaration. The expert system processes the rules to make deductions and to identify what additional details it needs, i.e. what concerns to ask, utilizing human-readable signs. For example, OPS5, CLIPS and their followers Jess and Drools run in this style.

Expert systems can operate in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and requirements – way. More advanced knowledge-based systems, such as Soar can likewise perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and keeping track of the success of analytical techniques.

Blackboard systems are a second sort of knowledge-based or skilled system architecture. They model a neighborhood of professionals incrementally contributing, where they can, to fix an issue. The issue is represented in several levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they acknowledge they can contribute. Potential problem-solving actions are represented on a program that is updated as the issue scenario modifications. A controller chooses how beneficial each contribution is, and who need to make the next analytical action. One example, the BB1 chalkboard architecture [54] was originally motivated by studies of how human beings plan to carry out several jobs in a journey. [55] A development of BB1 was to apply the very same blackboard model to solving its control problem, i.e., its controller carried out meta-level reasoning with understanding sources that monitored how well a strategy or the analytical was proceeding and could change from one strategy to another as conditions – such as objectives or times – altered. BB1 has actually been used in several domains: building and construction website preparation, intelligent tutoring systems, and real-time patient tracking.

The second AI winter, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP devices particularly targeted to speed up the advancement of AI applications and research study. In addition, several synthetic intelligence companies, such as Teknowledge and Inference Corporation, were offering expert system shells, training, and speaking with to corporations.

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

Many factors can be offered for the arrival of the second AI winter season. The hardware business stopped working when much more cost-effective basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the marketplace. Many business deployments of specialist systems were stopped when they showed too pricey to preserve. Medical specialist systems never captured on for several factors: the difficulty in keeping them up to date; the obstacle for medical experts to discover how to use a bewildering variety of different professional systems for various medical conditions; and perhaps most crucially, the unwillingness of physicians to rely on a computer-made diagnosis over their gut impulse, even for particular domains where the professional systems could exceed an average physician. Venture capital cash deserted AI virtually overnight. The world AI conference IJCAI hosted an enormous and extravagant trade show and countless nonacademic guests in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a little and strictly scholastic affair. [9]

Including more rigorous foundations, 1993-2011

Uncertain reasoning

Both analytical methods and extensions to reasoning were attempted.

One analytical technique, hidden Markov designs, had actually already been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl promoted using Bayesian Networks as a sound however efficient method of handling uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were used successfully in professional systems. [57] Even later, in the 1990s, analytical relational learning, a method that combines possibility with rational solutions, enabled likelihood to be integrated with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order reasoning to support were likewise tried. For example, non-monotonic reasoning could be used with fact maintenance systems. A truth maintenance system tracked assumptions and justifications for all reasonings. It allowed inferences to be withdrawn when presumptions were discovered to be inaccurate or a contradiction was obtained. Explanations could be offered a reasoning by discussing which guidelines were used to create it and then continuing through underlying inferences and guidelines all the method back to root presumptions. [58] Lofti Zadeh had introduced a different sort of extension to deal with the representation of vagueness. For instance, in deciding how “heavy” or “high” a guy is, there is often no clear “yes” or “no” answer, and a predicate for heavy or tall would rather return worths in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy reasoning further offered a way for propagating mixes of these values through rational formulas. [59]

Artificial intelligence

Symbolic maker discovering techniques were investigated to resolve the understanding acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to produce possible guideline hypotheses to test against spectra. Domain and task knowledge minimized the number of candidates checked to a manageable size. Feigenbaum explained Meta-DENDRAL as

… the conclusion of my imagine the early to mid-1960s relating to theory formation. 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 knowledge to steer and prune the search. That understanding acted since we spoke with individuals. But how did the people get the understanding? By taking a look at thousands of spectra. So we desired a program that would look at countless spectra and infer the understanding of mass spectrometry that DENDRAL might utilize to fix private hypothesis formation issues. We did it. We were even able to release new knowledge of mass spectrometry in the Journal of the American Chemical Society, giving credit just in a footnote that a program, Meta-DENDRAL, actually did it. We had the ability to do something that had been a dream: to have a computer program created a new and publishable piece of science. [51]

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

Advances were made in understanding device knowing theory, too. Tom Mitchell introduced variation space knowing which describes knowing as an explore a space of hypotheses, with upper, more general, and lower, more particular, boundaries including all feasible hypotheses consistent 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 artificial intelligence. [63]

Symbolic machine discovering encompassed more than learning by example. E.g., John Anderson supplied a cognitive model of human knowing where skill practice leads to a compilation of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a student may discover to apply “Supplementary angles are 2 angles whose steps sum 180 degrees” as numerous various procedural guidelines. E.g., one rule might state that if X and Y are extra and you know X, then Y will be 180 – X. He called his method “understanding compilation”. ACT-R has been utilized effectively to model elements of human cognition, such as finding out and retention. ACT-R is likewise utilized in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer system programming, and algebra to school kids. [64]

Inductive logic shows was another method to discovering that permitted reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might manufacture Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to produce hereditary programming, which he utilized to synthesize 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 showing its specs to be appropriate. [66]

As an alternative to logic, Roger Schank presented case-based reasoning (CBR). The CBR method outlined in his book, Dynamic Memory, [67] focuses first on remembering essential problem-solving cases for future usage and generalizing them where appropriate. When faced with a new problem, CBR recovers the most comparable previous case and adjusts it to the specifics of the present problem. [68] Another option to reasoning, hereditary algorithms and hereditary programming are based upon an evolutionary model of knowing, where sets of rules are encoded into populations, the guidelines govern the habits of individuals, and selection of the fittest prunes out sets of unsuitable guidelines over numerous generations. [69]

Symbolic maker learning was applied to learning principles, guidelines, heuristics, and problem-solving. Approaches, other than those above, include:

1. Learning from direction or advice-i.e., taking human direction, impersonated recommendations, and determining how to operationalize it in particular situations. For instance, in a game of Hearts, finding out exactly how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter specialist (SME) feedback throughout training. When problem-solving stops working, querying the expert to either find out a new exemplar for analytical or to learn a new description as to precisely why one exemplar is more pertinent than another. For instance, the program Protos discovered to identify tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing issue solutions based on comparable problems seen in the past, and after that modifying their options to fit a new scenario or domain. [72] [73] 4. Apprentice learning systems-learning unique options to problems by observing human analytical. Domain knowledge discusses why unique options are right 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 perform experiments and after that discovering from the outcomes. Doug Lenat’s Eurisko, for example, found out heuristics to beat human gamers at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., looking for beneficial macro-operators to be learned from sequences of fundamental problem-solving actions. Good macro-operators simplify analytical by enabling issues to be solved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the rise of deep knowing, the symbolic AI approach has been compared to deep knowing as complementary “… with parallels having actually been drawn lot of times by AI researchers in 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 modelled by deep learning and symbolic reasoning, respectively.” In this view, symbolic thinking is more apt for deliberative thinking, planning, and explanation while deep knowing is more apt for fast pattern acknowledgment in affective applications with loud data. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic approaches

Neuro-symbolic AI efforts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of thinking, discovering, and cognitive modeling. As argued by Valiant [77] and many others, [78] the effective building and construction of abundant computational cognitive designs demands the combination of sound symbolic thinking and efficient (device) learning designs. Gary Marcus, similarly, argues that: “We can not construct abundant cognitive models in an adequate, automated method without the triumvirate of hybrid architecture, rich prior understanding, and advanced methods for reasoning.”, [79] and in particular: “To build a robust, knowledge-driven approach to AI we should have the equipment of symbol-manipulation in our toolkit. Excessive of beneficial knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we understand of that can manipulate such abstract understanding reliably is the apparatus of symbol manipulation. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a requirement to resolve the two sort of believing talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 parts, System 1 and System 2. System 1 is quick, automatic, instinctive and unconscious. System 2 is slower, step-by-step, and specific. System 1 is the kind utilized for pattern recognition while System 2 is far much better matched for preparation, deduction, and deliberative thinking. In this view, deep knowing best models the first type of thinking while symbolic thinking best designs the 2nd kind and both are needed.

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

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

The combination of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively small research neighborhood over the last 2 decades and has actually yielded several substantial outcomes. Over the last decade, neural symbolic systems have actually been shown capable of overcoming the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were shown capable of representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and pieces of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of issues in the areas of bioinformatics, control engineering, software application confirmation and adjustment, visual intelligence, ontology knowing, and computer games. [78]

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

– Symbolic Neural symbolic-is the current approach of numerous neural designs in natural language processing, where words or subword tokens are both the supreme input and output of large language designs. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic strategies are used to call neural strategies. In this case the symbolic approach is Monte Carlo tree search and the neural methods find out how to examine video game positions.
– Neural|Symbolic-uses a neural architecture to interpret affective information as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to generate or identify training data that is consequently learned by a deep knowing design, e.g., to train a neural model for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to develop or identify examples.
– Neural _ Symbolic -utilizes 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 understanding base guidelines and terms. Logic Tensor Networks [86] also fall into this classification.
– Neural [Symbolic] -enables a neural design to directly call a symbolic reasoning engine, e.g., to perform an action or examine a state.

Many crucial research study concerns stay, such as:

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

Techniques and contributions

This section offers a summary of strategies and contributions in a total context resulting in many other, more in-depth articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history area.

AI shows languages

The key AI programming language in the US throughout the last symbolic AI boom duration was LISP. LISP is the second oldest programs language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the very first read-eval-print loop to support quick program development. Compiled functions might be freely blended with translated functions. Program tracing, stepping, and breakpoints were likewise provided, together with the ability to alter values or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, meaning that the compiler itself was initially composed in LISP and after that ran interpretively to assemble the compiler code.

Other key developments originated by LISP that have spread to other programs languages include:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the essential AI shows language throughout that same period was Prolog. Prolog provided a built-in shop of facts and clauses that could be queried by a read-eval-print loop. The store might act as a knowledge base and the stipulations could act as rules or a restricted type 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 special name assumption for primitive terms-e.g., the identifier barack_obama was thought about to refer to precisely one item. Backtracking and unification are built-in to Prolog.

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

Prolog is also a type of declarative programs. The reasoning clauses that describe programs are directly interpreted to run the programs defined. No specific series of actions is needed, as is the case with crucial shows languages.

Japan championed Prolog for its Fifth Generation Project, planning to construct unique hardware for high efficiency. Similarly, LISP makers were constructed to run LISP, but as the second AI boom turned to bust these business could not take on brand-new workstations that could now run LISP or Prolog natively at equivalent speeds. See the history section for more information.

Smalltalk was another prominent AI programming language. For instance, it presented 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 allows multiple inheritance, in addition to incremental extensions to both classes and metaclasses, thus providing a run-time meta-object procedure. [88]

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

Search

Search occurs in many sort of problem solving, consisting of planning, restraint satisfaction, and playing games such as checkers, chess, and go. The best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven stipulation knowing, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and reasoning

Multiple different techniques to represent understanding and then factor with those representations have been investigated. Below is a quick summary of approaches to understanding representation and automated reasoning.

Knowledge representation

Semantic networks, conceptual charts, frames, and reasoning are all approaches to modeling knowledge such as domain knowledge, problem-solving understanding, and the semantic meaning of language. Ontologies model key 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 deemed an ontology. YAGO integrates WordNet as part of its ontology, to line up facts drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.

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

First-order logic is more basic than description reasoning. The automated theorem provers discussed listed below can prove theorems in first-order reasoning. Horn stipulation logic is more restricted than first-order logic and is utilized in reasoning programs languages such as Prolog. Extensions to first-order logic include temporal reasoning, to handle time; epistemic logic, to factor about agent understanding; modal logic, to handle possibility and necessity; and probabilistic logics to handle logic and possibility together.

Automatic theorem showing

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

Prover9.
ACL2.
Vampire.

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

Reasoning in knowledge-based systems

Knowledge-based systems have a specific understanding base, normally of guidelines, to enhance reusability across domains by separating procedural code and domain understanding. A different reasoning engine processes rules and includes, deletes, or customizes a knowledge shop.

Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more minimal logical representation is used, Horn Clauses. Pattern-matching, particularly unification, is utilized in Prolog.

A more versatile sort of problem-solving occurs when reasoning about what to do next takes place, instead of just picking among 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 extra abilities, such as the capability to assemble frequently utilized understanding into higher-level portions.

Commonsense reasoning

Marvin Minsky initially proposed frames as a method of analyzing typical visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for typical regimens, such as eating in restaurants. Cyc has tried to catch useful common-sense understanding and has “micro-theories” to manage particular kinds of domain-specific thinking.

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

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

Constraints and constraint-based reasoning

Constraint solvers carry out a more limited kind of inference than first-order reasoning. They can simplify sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, along with fixing other type of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning shows can be utilized to fix scheduling issues, for example with restraint handling rules (CHR).

Automated planning

The General Problem Solver (GPS) cast preparation as analytical used means-ends analysis to create strategies. STRIPS took a different technique, seeing planning as theorem proving. Graphplan takes a least-commitment method to preparation, instead of sequentially picking actions from an initial state, working forwards, or a goal state if working backwards. Satplan is a technique to planning where a preparation problem is reduced to a Boolean satisfiability problem.

Natural language processing

Natural language processing concentrates on treating language as data to perform jobs such as recognizing topics without necessarily comprehending the intended significance. Natural language understanding, in contrast, constructs a significance representation and utilizes that for further processing, such as answering questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long dealt with by symbolic AI, but since enhanced 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 explicit semantic analysis likewise offered vector representations of files. In the latter case, vector elements are interpretable as ideas called by Wikipedia articles.

New deep knowing approaches based on Transformer designs have now eclipsed these earlier symbolic AI techniques and attained cutting edge 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 meaning of the vector parts is opaque.

Agents and multi-agent systems

Agents are autonomous systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig’s basic book on expert system is organized to show agent architectures of increasing sophistication. [91] The elegance of representatives differs from basic reactive agents, to those with a model of the world and automated preparation abilities, perhaps a BDI representative, i.e., one with beliefs, desires, and intents – or alternatively a support discovering model found out with time 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 includes multiple agents that communicate amongst themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The agents require not all have the very same internal architecture. Advantages of multi-agent systems include the capability to divide work among the representatives and to increase fault tolerance when agents are lost. Research problems consist of how representatives reach agreement, dispersed issue solving, multi-agent learning, multi-agent preparation, and dispersed constraint optimization.

Controversies occurred from at an early stage in symbolic AI, both within the field-e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who accepted AI but declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were mostly from thinkers, on intellectual grounds, but likewise from funding firms, specifically during the 2 AI winters.

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

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

McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A simple example occurs in “proving that a person person might enter 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 be successful. 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 preconditions for an action to succeed. An unlimited number of pathological conditions can be pictured, e.g., a banana in a tailpipe could prevent a car from operating correctly.

McCarthy’s technique to repair the frame issue was circumscription, a kind of non-monotonic logic where reductions could be made from actions that require only specify what would alter while not having to clearly define everything that would not change. Other non-monotonic reasonings provided reality upkeep systems that modified beliefs causing contradictions.

Other ways of dealing with more open-ended domains included probabilistic reasoning systems and artificial intelligence to find out new principles and rules. McCarthy’s Advice Taker can be deemed a motivation here, as it could integrate new understanding offered by a human in the type of assertions or rules. For example, experimental symbolic maker learning systems checked out the ability to take top-level natural language advice and to translate it into domain-specific actionable guidelines.

Similar to the problems in managing dynamic domains, common-sense reasoning is also hard to catch in official reasoning. Examples of sensible thinking include implicit thinking about how people believe or general understanding of daily occasions, objects, and living creatures. This sort of knowledge is considered approved and not deemed noteworthy. Common-sense thinking is an open location of research and challenging both for symbolic systems (e.g., Cyc has tried to catch key parts of this understanding over more than a decade) and neural systems (e.g., self-driving cars that do not understand not to drive into cones or not to strike pedestrians walking a bicycle).

McCarthy viewed his Advice Taker as having common-sense, however his meaning of common-sense was various than the one above. [94] He specified a program as having good sense “if it immediately deduces for itself a sufficiently broad class of immediate effects of anything it is informed and what it already understands. “

Connectionist AI: philosophical difficulties and sociological disputes

Connectionist techniques include earlier deal with 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 more advanced methods, such as Transformers, GANs, and other operate in deep knowing.

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

1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected totally, and connectionist architectures underlie intelligence and are completely adequate to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are needed for intelligence

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

The third and last position I want to analyze here is what I call the moderate connectionist view, a more eclectic view of the existing dispute in between connectionism and symbolic AI. One of 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 (partly symbolic, partially connectionist) systems. He declared that (a minimum of) 2 sort of theories are needed in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern acknowledgment) connectionism has benefits over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive thinking, and generative symbol manipulation procedures) the symbolic paradigm uses adequate 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 neighborhood against symbolic approaches now might be more sociological than philosophical:

To believe that we can just abandon symbol-manipulation is to suspend shock.

And yet, for the most part, that’s how most current AI profits. Hinton and numerous others have actually attempted difficult to banish symbols entirely. The deep knowing hope-seemingly grounded not a lot in science, however in a sort of historic grudge-is that smart habits will emerge purely from the confluence of massive data and deep learning. Where classical computer systems and software solve tasks by defining sets of symbol-manipulating guidelines dedicated to specific tasks, such as editing a line in a word processor or carrying out a calculation in a spreadsheet, neural networks normally try to solve jobs by statistical approximation and gaining from examples.

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

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

…

Since then, his anti-symbolic project has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in one of science’s crucial journals, Nature. It closed with a direct attack on symbol adjustment, calling not for reconciliation however for straight-out replacement. Later, Hinton told a gathering of European Union leaders that investing any additional cash in symbol-manipulating approaches was “a substantial error,” likening it to purchasing internal combustion engines in the period of electric cars. [98]

Part of these conflicts might be because of unclear terminology:

Turing award winner Judea Pearl provides a critique of artificial intelligence which, sadly, conflates the terms machine learning and deep learning. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of expert systems dispossessed of any ability to learn. The usage of the terms needs information. Machine learning is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the option of representation, localist logical rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production rules composed by hand. An appropriate meaning of AI concerns understanding representation and thinking, autonomous multi-agent systems, planning and argumentation, in addition to learning. [99]

Situated robotics: the world as a model

Another critique of symbolic AI is the embodied cognition technique:

The embodied cognition technique claims that it makes no sense to consider the brain independently: cognition occurs within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s working exploits consistencies in its environment, consisting of the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units end up being central, not peripheral. [100]

Rodney Brooks developed behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this method, is deemed an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or dispersed, as not only unneeded, however as harmful. Instead, he produced the subsumption architecture, a layered architecture for embodied agents. Each layer attains a different 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 avoid objects. The middle layer triggers the robot to roam around when there are no challenges. The top layer causes the robot to go to more remote locations for additional expedition. Each layer can briefly hinder or reduce a lower-level layer. He criticized AI researchers for specifying AI problems for their systems, when: “There is no clean department in between perception (abstraction) and reasoning in the genuine world.” [101] He called his robots “Creatures” and each layer was “made up of a fixed-topology network of basic finite state machines.” [102] In the Nouvelle AI method, “First, it is extremely crucial to test the Creatures we develop in the real world; i.e., in the exact same world that we people populate. It is disastrous to fall under the temptation of testing them in a streamlined world initially, even with the very best intentions of later moving activity to an unsimplified world.” [103] His focus on real-world testing was in contrast to “Early operate in AI focused on video games, geometrical problems, symbolic algebra, theorem proving, and other formal 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 methods. Symbolic AI has actually been criticized as disembodied, responsible to the credentials issue, and bad in handling the perceptual issues where deep learning excels. In turn, connectionist AI has actually been criticized as improperly suited for deliberative detailed problem solving, integrating knowledge, and handling planning. Finally, Nouvelle AI masters reactive and real-world robotics domains however has been slammed for difficulties in integrating knowing and knowledge.

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

Overall, Dreyfus saw locations where AI did not have total answers and stated that Al is therefore difficult; we now see much of these exact same areas undergoing continued research and advancement causing increased ability, not impossibility. [100]

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

Notes

^ McCarthy as soon as stated: “This is AI, so we don’t care if it’s emotionally real”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he stated “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of expert system: one targeted at producing intelligent behavior no matter how it was achieved, and the other focused on modeling intelligent procedures discovered in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not specify 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 knowing with symbolic expert system: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “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. 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.
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^ 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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