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Symbolic Artificial Intelligence
In expert system, symbolic expert system (likewise referred to as classical artificial intelligence or logic-based expert system) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based on top-level symbolic (human-readable) representations of issues, reasoning and search. [3] Symbolic AI utilized tools such as reasoning programs, production rules, semantic internet and frames, and it established applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm caused influential ideas in search, symbolic shows languages, representatives, multi-agent systems, the semantic web, and the strengths and constraints of official understanding 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 producing a maker 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, led to impractical expectations and promises and was followed by the first AI Winter as moneying dried up. [5] [6] A 2nd boom (1969-1986) accompanied the rise of specialist systems, their guarantee of capturing business know-how, and an enthusiastic corporate embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later on disappointment. [8] Problems with difficulties in understanding acquisition, preserving large knowledge bases, and brittleness in dealing with out-of-domain issues emerged. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on resolving hidden issues in dealing with uncertainty and in knowledge acquisition. [10] Uncertainty was resolved with formal approaches such as covert Markov designs, Bayesian thinking, and analytical relational knowing. [11] [12] Symbolic machine discovering resolved the knowledge acquisition issue with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive logic shows to learn relations. [13]
Neural networks, a subsymbolic method, had been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not deemed successful till about 2012: “Until Big Data ended up being prevalent, the basic consensus in the Al community was that the so-called neural-network approach was helpless. Systems just didn’t work that well, compared to other methods. … A transformation was available in 2012, when a number of individuals, including a team of researchers dealing with Hinton, worked out a method to use the power of GPUs to enormously increase the power of neural networks.” [16] Over the next several years, deep knowing had incredible success in handling vision, speech recognition, speech synthesis, image generation, and device translation. However, because 2020, as fundamental troubles with predisposition, explanation, comprehensibility, and robustness ended up being more evident with deep learning approaches; an increasing number of AI researchers have actually called for combining the finest of both the symbolic and neural network methods [17] [18] and attending to locations that both techniques have difficulty with, such as sensible thinking. [16]
A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia short article on the History of AI, with dates and titles varying a little for increased clearness.
The first AI summer season: irrational exuberance, 1948-1966
Success at early efforts in AI occurred in 3 primary areas: synthetic neural networks, understanding representation, and heuristic search, adding to high expectations. This section summarizes Kautz’s reprise of early AI history.
Approaches inspired by human or animal cognition or habits
Cybernetic methods attempted to replicate the feedback loops between animals and their environments. A robotic turtle, with sensing units, motors for driving and guiding, and seven vacuum tubes for control, based on a preprogrammed neural net, was constructed as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and located robotics. [20]
An important early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to show 38 primary theorems from Whitehead and Russell’s Principia . Newell, Simon, and Shaw later generalized this work to develop a domain-independent problem solver, GPS (General Problem Solver). GPS fixed issues represented with formal operators via state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic methods achieved great success at replicating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own design of research study. Earlier approaches based on cybernetics or synthetic neural networks were deserted or pressed into the background.
Herbert Simon and Allen Newell studied human analytical skills and tried to formalize them, and their work laid the structures of the field of expert system, in addition to cognitive science, operations research and management science. Their research team utilized the outcomes of psychological experiments to establish programs that simulated the techniques that individuals utilized to solve problems. [22] [23] This custom, centered at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the middle 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific sort of knowledge that we will see later on utilized in specialist systems, early symbolic AI scientists discovered another more basic application of knowledge. These were called heuristics, guidelines that guide a search in promising instructions: “How can non-enumerative search be practical when the underlying issue is greatly hard? The approach promoted by Simon and Newell is to employ heuristics: fast algorithms that may stop working on some inputs or output suboptimal solutions.” [26] Another crucial advance was to find a method to use these heuristics that ensures an option will be discovered, if there is one, not withstanding the occasional fallibility of heuristics: “The A * algorithm offered a general frame for complete and optimal heuristically directed search. A * is utilized as a subroutine within practically every AI algorithm today but is still no magic bullet; its guarantee of efficiency is purchased at the expense of worst-case rapid time. [26]
Early deal with understanding representation and reasoning
Early work covered both applications of official thinking emphasizing first-order logic, together with attempts to manage sensible reasoning in a less official manner.
Modeling official thinking with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that devices did not need to replicate the precise systems of human thought, but could instead look for the essence of abstract reasoning and analytical with logic, [27] regardless of whether people used the very same algorithms. [a] His laboratory at Stanford (SAIL) focused on utilizing official reasoning to solve a broad range of problems, consisting of 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 reasoning programming. [32] [33]
Modeling implicit sensible understanding with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that fixing difficult issues in vision and natural language processing needed ad hoc solutions-they argued that no basic and basic concept (like reasoning) would catch all the aspects of intelligent habits. Roger Schank described 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, since they need to be developed by hand, one complicated principle at a time. [38] [39] [40]
The very first AI winter: crushed dreams, 1967-1977
The very first AI winter was a shock:
During the first AI summertime, many individuals thought that device intelligence could be accomplished in simply a couple of years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to use AI to solve problems of national security; in specific, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battlefield. Researchers had actually begun to recognize that attaining AI was going to be much more difficult than was expected a decade earlier, but a mix of hubris and disingenuousness led lots of university and think-tank researchers to accept funding with promises of deliverables that they should have known they could not satisfy. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had been created, and a dramatic backlash embeded in. New DARPA leadership canceled existing AI funding programs.
Outside of the United States, the most fertile ground for AI research was the UK. The AI winter season in the UK was spurred on not so much by dissatisfied military leaders as by rival academics who saw AI scientists as charlatans and a drain on research study funding. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research study in the country. The report stated that all of the issues being dealt with in AI would be better handled by researchers from other disciplines-such as used mathematics. The report likewise declared that AI successes on toy problems might never ever scale to real-world applications due to combinatorial explosion. [41]
The 2nd AI summertime: knowledge is power, 1978-1987
Knowledge-based systems
As constraints with weak, domain-independent approaches became a growing number of evident, [42] scientists from all three customs began to build knowledge into AI applications. [43] [7] The knowledge transformation was driven by the awareness that understanding underlies high-performance, domain-specific AI applications.
Edward Feigenbaum stated:
– “In the understanding lies the power.” [44]
to explain that high performance in a specific domain requires both basic and extremely domain-specific understanding. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to perform a complicated task well, it must know a lot about the world in which it operates.
( 2) A possible extension of that concept, called the Breadth Hypothesis: there are two extra capabilities needed for intelligent habits in unforeseen circumstances: drawing on progressively general knowledge, and analogizing to specific but remote knowledge. [45]
Success with expert systems
This “understanding revolution” resulted in the advancement and release of professional systems (introduced by Edward Feigenbaum), the first commercially successful form of AI software application. [46] [47] [48]
Key expert systems were:
DENDRAL, which discovered the structure of organic particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and recommended additional lab tests, when necessary – by interpreting laboratory results, patient history, and physician observations. “With about 450 rules, MYCIN had the ability to perform as well as some experts, and substantially much better than junior doctors.” [49] INTERNIST and CADUCEUS which dealt with internal medication diagnosis. Internist attempted to capture the knowledge of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS could eventually diagnose as much as 1000 various illness.
– GUIDON, which demonstrated how an understanding base built for professional problem fixing could be repurposed for teaching. [50] XCON, to set up VAX computer systems, a then tiresome procedure that could take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is considered the very first specialist system that depend 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 designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I informed him I wanted an induction “sandbox”, he said, “I have just 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 techniques, and he had an algorithm that was proficient at generating the chemical problem area.
We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the contraceptive pill, and likewise among the world’s most appreciated mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We began to contribute to their understanding, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL increasingly more knowledge. The more you did that, the smarter the program ended up being. We had very excellent outcomes.
The generalization was: in the knowledge lies the power. That was the big idea. In my career that is the substantial, “Ah ha!,” and it wasn’t the method AI was being done formerly. Sounds simple, but it’s probably AI’s most powerful generalization. [51]
The other specialist systems discussed above followed DENDRAL. MYCIN exemplifies the timeless expert system architecture of a knowledge-base of guidelines paired to a symbolic reasoning mechanism, including making use of certainty aspects to deal with uncertainty. GUIDON demonstrates how a specific knowledge base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a specific type of knowledge-based application. Clancey showed that it was not adequate simply to utilize MYCIN’s rules for direction, however that he also needed to include guidelines for dialogue management and student modeling. [50] XCON is substantial since of the millions of dollars it conserved DEC, which activated the professional system boom where most all major corporations in the US had expert systems groups, to catch business expertise, preserve it, and automate it:
By 1988, DEC’s AI group had 40 expert systems released, with more en route. DuPont had 100 in use and 500 in advancement. Nearly every significant U.S. corporation had its own Al group and was either utilizing or examining professional systems. [49]
Chess specialist understanding was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the aid of symbolic AI, to win in a video game of chess versus the world champ at that time, Garry Kasparov. [52]
Architecture of knowledge-based and professional systems
A crucial element of the system architecture for all expert systems is the knowledge base, which shops realities and rules for problem-solving. [53] The most basic method for an expert system knowledge base is just a collection or network of production guidelines. Production rules connect symbols in a relationship comparable to an If-Then declaration. The professional system processes the rules to make deductions and to identify what extra info it requires, i.e. what concerns to ask, utilizing human-readable symbols. For instance, OPS5, CLIPS and their followers Jess and Drools run in this fashion.
Expert systems can run in either a forward chaining – from evidence to conclusions – or backwards chaining – from goals to needed information and prerequisites – way. Advanced knowledge-based systems, such as Soar can likewise carry out meta-level thinking, that is thinking about their own reasoning in terms of deciding how to fix problems and keeping track of the success of problem-solving strategies.
Blackboard systems are a second sort of knowledge-based or expert system architecture. They model a neighborhood of specialists incrementally contributing, where they can, to solve an issue. The issue is represented in several levels of abstraction or alternate views. The experts (understanding sources) offer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on a program that is upgraded as the problem situation changes. A controller chooses how useful each contribution is, and who should make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was initially inspired by research studies of how human beings prepare to carry out several tasks in a trip. [55] An innovation of BB1 was to use the exact same chalkboard model to fixing its control problem, i.e., its controller performed meta-level thinking with understanding sources that kept track of how well a plan or the problem-solving was continuing and could switch from one technique to another as conditions – such as goals or times – changed. BB1 has actually been applied in multiple domains: building website preparation, smart tutoring systems, and real-time patient monitoring.
The second AI winter season, 1988-1993
At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to speed up the development of AI applications and research. In addition, numerous synthetic intelligence companies, such as Teknowledge and Inference Corporation, were selling skilled system shells, training, and speaking with to corporations.
Unfortunately, the AI boom did not last and Kautz finest explains the second AI winter that followed:
Many factors can be used for the arrival of the 2nd AI winter. The hardware companies failed when a lot more economical general Unix workstations from Sun together with great compilers for LISP and Prolog came onto the market. Many commercial releases of specialist systems were stopped when they showed too costly to keep. Medical professional systems never captured on for a number of factors: the problem in keeping them as much as date; the challenge for medical specialists to find out how to use a bewildering range of different professional systems for different medical conditions; and possibly most crucially, the unwillingness of medical professionals to trust a computer-made diagnosis over their gut instinct, even for particular domains where the expert systems could surpass an average medical professional. Venture capital money deserted AI practically overnight. The world AI conference IJCAI hosted an enormous and luxurious trade convention and countless nonacademic participants 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]
Including more extensive foundations, 1993-2011
Uncertain reasoning
Both statistical techniques and extensions to reasoning were attempted.
One analytical method, hidden Markov models, had currently been popularized in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl popularized the usage of Bayesian Networks as a sound but efficient way of dealing with unpredictable reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were used effectively in expert systems. [57] Even later on, in the 1990s, statistical relational knowing, an approach that integrates likelihood with rational solutions, enabled possibility to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order logic to support were also tried. For instance, non-monotonic thinking might be utilized with fact maintenance systems. A reality maintenance system tracked assumptions and justifications for all inferences. It permitted inferences to be withdrawn when presumptions were discovered out to be incorrect or a contradiction was derived. Explanations could be offered a reasoning by discussing which guidelines were applied to develop it and after that continuing through underlying reasonings and guidelines all the way back to root assumptions. [58] Lofti Zadeh had actually introduced a different sort of extension to manage the representation of vagueness. For instance, in choosing how “heavy” or “high” a male is, there is regularly no clear “yes” or “no” response, and a predicate for heavy or high would rather return values in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy reasoning further offered a means for propagating mixes of these worths through logical solutions. [59]
Machine learning
Symbolic maker discovering approaches were examined to deal with the understanding acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test strategy to create plausible rule hypotheses to check against spectra. Domain and job knowledge decreased the number of candidates tested to a manageable size. Feigenbaum described 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 knowledge to guide and prune the search. That understanding acted due to the fact that we interviewed individuals. But how did the people get the knowledge? By taking a look at countless spectra. So we wanted a program that would take a look at countless spectra and presume the understanding of mass spectrometry that DENDRAL could utilize to solve private hypothesis development problems. We did it. We were even able to publish brand-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 were able to do something that had actually 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 approach of Meta-DENDRAL, Ross Quinlan developed a domain-independent technique to analytical category, decision tree knowing, starting first with ID3 [60] and after that later extending its capabilities to C4.5. [61] The choice trees developed are glass box, interpretable classifiers, with human-interpretable category guidelines.
Advances were made in understanding machine knowing theory, too. Tom Mitchell introduced variation area knowing which describes knowing as a search through a space of hypotheses, with upper, more basic, and lower, more specific, borders incorporating all practical hypotheses consistent with the examples seen up until now. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]
Symbolic maker finding out encompassed more than discovering 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 instance, a trainee might find out to use “Supplementary angles are 2 angles whose procedures sum 180 degrees” as several various procedural rules. E.g., one guideline may say that if X and Y are extra and you understand X, then Y will be 180 – X. He called his technique “understanding compilation”. ACT-R has actually been used successfully to design aspects of human cognition, such as learning and retention. ACT-R is also utilized in intelligent tutoring systems, called cognitive tutors, to effectively teach geometry, computer system programs, and algebra to school kids. [64]
Inductive logic programming was another method to finding out that allowed reasoning programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could synthesize Prolog programs from examples. [65] John R. Koza used genetic algorithms to program synthesis to create hereditary shows, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic method to program synthesis that synthesizes a practical program in the course of showing its specs to be proper. [66]
As an option to logic, Roger Schank presented case-based reasoning (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses initially on remembering key problem-solving cases for future use and generalizing them where proper. When confronted with a brand-new issue, CBR retrieves the most similar previous case and adapts it to the specifics of the existing issue. [68] Another option to reasoning, genetic algorithms and genetic programs are based on an evolutionary design of learning, where sets of guidelines are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of inappropriate guidelines over lots of generations. [69]
Symbolic device learning was used to discovering principles, rules, heuristics, and analytical. Approaches, aside from those above, include:
1. Learning from guideline or advice-i.e., taking human guideline, impersonated suggestions, and figuring out how to operationalize it in particular scenarios. For example, 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 professional (SME) feedback during training. When problem-solving stops working, querying the professional to either find out a new prototype for problem-solving or to find out a new explanation regarding exactly why one exemplar is more relevant than another. For example, the program Protos discovered to identify tinnitus cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing problem solutions based upon similar problems seen in the past, and after that customizing their options to fit a brand-new scenario or domain. [72] [73] 4. Apprentice knowing systems-learning novel options to problems by observing human problem-solving. Domain knowledge discusses why unique services are correct and how the option can be generalized. LEAP found out how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., producing tasks to perform experiments and then gaining from the outcomes. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human players at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., searching for beneficial macro-operators to be found out from series of basic analytical actions. Good macro-operators simplify analytical by allowing problems to be solved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the increase of deep learning, the symbolic AI method has actually been compared to deep learning as complementary “… with parallels having been drawn lot of times by AI researchers between Kahneman’s research study 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 learning and symbolic reasoning, respectively.” In this view, symbolic reasoning is more apt for deliberative reasoning, planning, and description while deep knowing is more apt for fast pattern recognition in affective applications with loud data. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic techniques
Neuro-symbolic AI efforts 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, finding out, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the effective construction of rich computational cognitive designs requires the mix of sound symbolic reasoning and effective (device) knowing designs. Gary Marcus, likewise, argues that: “We can not build rich cognitive models in an appropriate, automated method without the triumvirate of hybrid architecture, abundant anticipation, and sophisticated techniques for reasoning.”, [79] and in specific: “To construct a robust, knowledge-driven technique to AI we must have the machinery of symbol-manipulation in our toolkit. Excessive of useful knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we understand of that can manipulate such abstract understanding reliably is the apparatus of symbol adjustment. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based upon a need to attend to the two sort of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two components, System 1 and System 2. System 1 is fast, automated, user-friendly and unconscious. System 2 is slower, step-by-step, and specific. System 1 is the kind used for pattern acknowledgment while System 2 is far better suited for planning, reduction, and deliberative thinking. In this view, deep knowing best models the very first kind of believing while symbolic reasoning best models the second kind and both are required.
Garcez and Lamb describe research study in this area as being continuous for at least the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic 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 been pursued by a relatively little research community over the last 2 years and has yielded a number of substantial outcomes. Over the last years, neural symbolic systems have been shown efficient in getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in action to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and fragments of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been applied to a number of problems in the locations of bioinformatics, control engineering, software confirmation and adjustment, visual intelligence, ontology knowing, and video game. [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 method of numerous neural designs in natural language processing, where words or subword tokens are both the supreme input and output of large language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic strategies are utilized to call neural techniques. In this case the symbolic technique is Monte Carlo tree search and the neural techniques find out how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to analyze affective data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to generate or identify training information that is subsequently learned by a deep learning design, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to produce or label examples.
– Neural _ Symbolic -utilizes a neural net that is generated from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree created from knowledge base rules and terms. Logic Tensor Networks [86] likewise fall under this classification.
– Neural [Symbolic] -permits a neural design to directly call a symbolic reasoning engine, e.g., to perform an action or assess a state.
Many essential research study questions remain, 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 logically be dealt with?
Techniques and contributions
This area offers an introduction of methods and contributions in a general context causing numerous other, more in-depth posts in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history section.
AI programs languages
The key AI programming language in the US throughout the last symbolic AI boom period was LISP. LISP is the 2nd oldest programs language after FORTRAN and was produced in 1958 by John McCarthy. LISP supplied the very first read-eval-print loop to support fast program advancement. Compiled functions could be freely blended with translated functions. Program tracing, stepping, and breakpoints were also supplied, in addition to the ability to change worths 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 after that ran interpretively to put together the compiler code.
Other key innovations pioneered by LISP that have actually spread to other shows languages consist of:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves information structures that other programs could run on, allowing the easy meaning of higher-level languages.
In contrast to the US, in Europe the essential AI programs language during that very same period was Prolog. Prolog offered a built-in store of facts and clauses that could be queried by a read-eval-print loop. The shop might serve as an understanding base and the provisions could act as rules or a limited form of reasoning. As a subset of first-order logic Prolog was based on Horn stipulations with a closed-world assumption-any facts not understood were thought about false-and an unique name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe exactly one things. Backtracking and marriage are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a kind of reasoning programming, which was developed by Robert Kowalski. Its history was also affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more detail see the area on the origins of Prolog in the PLANNER short article.
Prolog is likewise a sort of declarative programs. The logic clauses that describe programs are directly translated to run the programs defined. No explicit series of actions is needed, as is the case with essential programming languages.
Japan championed Prolog for its Fifth Generation Project, intending to construct unique hardware for high performance. Similarly, LISP makers were constructed to run LISP, however as the 2nd AI boom turned to bust these business might not complete with brand-new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history area for more detail.
Smalltalk was another prominent AI programming language. For instance, it presented metaclasses and, in addition to Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the current basic Lisp dialect. CLOS is a Lisp-based object-oriented system that enables several inheritance, in addition to incremental extensions to both classes and metaclasses, thus providing a run-time meta-object protocol. [88]
For other AI programs languages see this list of programs languages for synthetic intelligence. Currently, Python, a multi-paradigm shows language, is the most popular programs language, partly due to its comprehensive bundle library that supports data science, natural language processing, and deep knowing. Python includes a read-eval-print loop, functional aspects such as higher-order functions, and object-oriented shows that includes metaclasses.
Search
Search emerges in lots of kinds of issue fixing, including planning, constraint fulfillment, and playing video 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 clause learning, 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 approaches to represent understanding and after that reason with those representations have been examined. Below is a quick overview of approaches to understanding representation and automated thinking.
Knowledge representation
Semantic networks, conceptual graphs, frames, and reasoning are all methods to modeling knowledge such as domain knowledge, analytical knowledge, and the semantic meaning of language. Ontologies model key principles and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can likewise be considered as an ontology. YAGO includes WordNet as part of its ontology, to align facts drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being used.
Description reasoning is a reasoning for automated classification of ontologies and for identifying 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 then inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order logic is more basic than description logic. The automated theorem provers gone over listed below can prove theorems in first-order reasoning. Horn clause reasoning is more restricted than first-order reasoning and is used in logic shows languages such as Prolog. Extensions to first-order reasoning consist of temporal reasoning, to deal with time; epistemic logic, to reason about agent understanding; modal reasoning, to handle possibility and need; and probabilistic logics to deal with logic and likelihood together.
Automatic theorem showing
Examples of automated theorem provers for first-order logic are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in combination with the Mace4 design checker. ACL2 is a theorem prover that can handle proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, also known as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit knowledge base, typically of rules, to improve reusability throughout domains by separating procedural code and domain understanding. A different inference engine processes rules and includes, deletes, or customizes an understanding shop.
Forward chaining reasoning engines are the most common, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more restricted logical representation is utilized, Horn Clauses. Pattern-matching, particularly unification, is utilized in Prolog.
A more versatile sort of problem-solving happens when reasoning about what to do next happens, rather than merely picking among the readily available actions. This type of meta-level thinking is utilized in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R may have extra capabilities, such as the capability to compile often utilized understanding into higher-level pieces.
Commonsense thinking
Marvin Minsky initially proposed frames as a way of analyzing common visual situations, such as an office, and Roger Schank extended this idea to scripts for typical routines, such as eating in restaurants. Cyc has actually attempted to record helpful common-sense understanding and has “micro-theories” to handle specific type of domain-specific reasoning.
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 range. We expect it to heat and possibly boil over, although we may not understand its temperature level, its boiling point, or other information, such as atmospheric pressure.
Similarly, Allen’s temporal period algebra is a simplification of thinking about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be resolved with constraint solvers.
Constraints and constraint-based reasoning
Constraint solvers perform a more minimal sort of inference than first-order reasoning. They can simplify sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, along with resolving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programs can be utilized to resolve scheduling problems, for instance with constraint managing rules (CHR).
Automated planning
The General Problem Solver (GPS) cast preparation as problem-solving used means-ends analysis to produce strategies. STRIPS took a different method, viewing preparation as theorem proving. Graphplan takes a least-commitment technique to planning, rather than sequentially picking actions from a preliminary state, working forwards, or a goal state if working backwards. Satplan is an approach to planning where a planning problem is decreased to a Boolean satisfiability issue.
Natural language processing
Natural language processing concentrates on dealing with language as data to carry out tasks such as recognizing topics without necessarily comprehending the designated meaning. Natural language understanding, in contrast, constructs a significance representation and utilizes that for further processing, such as addressing questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long managed by symbolic AI, but since enhanced by deep knowing methods. In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings. Latent semantic analysis (LSA) and specific semantic analysis likewise provided vector representations of documents. In the latter case, vector parts are interpretable as concepts named by Wikipedia articles.
New deep learning techniques based on Transformer models have now eclipsed these earlier symbolic AI approaches and obtained modern performance 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 elements is nontransparent.
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 book on artificial intelligence is organized to reflect agent architectures of increasing elegance. [91] The elegance of representatives differs from easy reactive representatives, to those with a model of the world and automated preparation abilities, possibly a BDI agent, i.e., one with beliefs, desires, and objectives – or additionally a support learning design discovered with time to select actions – approximately a combination of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep learning for perception. [92]
In contrast, a multi-agent system consists of several representatives that interact amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The agents need not all have the exact same internal architecture. Advantages of multi-agent systems consist of the capability to divide work amongst the representatives and to increase fault tolerance when representatives are lost. Research issues consist of how representatives reach consensus, distributed issue resolving, multi-agent knowing, multi-agent planning, and dispersed constraint optimization.
Controversies developed from early on in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who embraced AI however declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from outside of the field were mostly from thinkers, on intellectual premises, however likewise from funding firms, especially throughout the 2 AI winter seasons.
The Frame Problem: knowledge representation challenges for first-order reasoning
Limitations were found in utilizing simple first-order logic to reason about vibrant domains. Problems were found both with concerns to enumerating the preconditions for an action to be successful and in providing 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 basic example occurs in “showing that one individual could enter into conversation with another”, as an axiom asserting “if a person has a telephone he still has it after looking up a number in the telephone book” would be needed for the deduction to succeed. Similar axioms would be needed for other domain actions to define what did not change.
A similar problem, called the Qualification Problem, happens in trying to specify the preconditions for an action to succeed. A limitless number of pathological conditions can be pictured, e.g., a banana in a tailpipe could avoid a vehicle from operating correctly.
McCarthy’s approach to fix the frame problem was circumscription, a kind of non-monotonic logic where deductions might be made from actions that need only specify what would change while not having to explicitly specify whatever that would not change. Other non-monotonic logics supplied fact upkeep systems that modified beliefs resulting in contradictions.
Other methods of managing more open-ended domains included probabilistic reasoning systems and machine learning to learn brand-new concepts and guidelines. McCarthy’s Advice Taker can be deemed a motivation here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, speculative symbolic device discovering systems explored the capability to take high-level natural language recommendations and to translate it into domain-specific actionable guidelines.
Similar to the problems in handling vibrant domains, common-sense reasoning is likewise challenging to catch in formal thinking. Examples of common-sense reasoning include implicit reasoning about how people think or basic knowledge of daily occasions, things, and living animals. This kind of knowledge is considered granted and not deemed noteworthy. Common-sense reasoning is an open location of research study and challenging both for symbolic systems (e.g., Cyc has attempted to capture crucial parts of this knowledge over more than a decade) and neural systems (e.g., self-driving automobiles that do not understand not to drive into cones or not to hit pedestrians walking a bicycle).
McCarthy saw his Advice Taker as having common-sense, but his meaning of sensible was different than the one above. [94] He defined a program as having good sense “if it instantly deduces for itself an adequately broad class of immediate repercussions of anything it is informed and what it currently understands. “
Connectionist AI: philosophical obstacles and sociological disputes
Connectionist methods include earlier work on neural networks, [95] such as perceptrons; work in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s advanced methods, such as Transformers, GANs, and other operate in deep learning.
Three philosophical positions [96] have been outlined among connectionists:
1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down completely, and connectionist architectures underlie intelligence and are totally sufficient to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are required for intelligence
Olazaran, in his sociological history of the controversies within the neural network community, described the moderate connectionism consider as basically suitable with existing research in neuro-symbolic hybrids:
The 3rd and last position I wish to examine here is what I call the moderate connectionist view, a more eclectic view of the existing debate in between connectionism and symbolic AI. One of the researchers who has actually elaborated this position most explicitly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark safeguarded hybrid (partly symbolic, partially connectionist) systems. He claimed that (a minimum of) 2 type of theories are required in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern acknowledgment) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive reasoning, and generative sign control procedures) the symbolic paradigm uses appropriate designs, and not just “approximations” (contrary to what extreme connectionists would claim). [97]
Gary Marcus has declared that the animus in the deep learning neighborhood versus symbolic approaches now may be more sociological than philosophical:
To believe that we can just desert symbol-manipulation is to suspend shock.
And yet, for the most part, that’s how most present AI profits. Hinton and many others have actually tried tough to eradicate symbols altogether. The deep knowing hope-seemingly grounded not so much in science, however in a sort of historic grudge-is that smart habits will emerge simply from the confluence of massive data and deep learning. Where classical computers and software solve jobs by specifying 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 analytical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his colleagues have been vehemently “anti-symbolic”:
When deep knowing reemerged in 2012, it was with a sort of take-no-prisoners mindset that has actually identified the majority of the last decade. By 2015, his hostility toward all things signs had fully taken shape. He offered a talk at an AI workshop at Stanford comparing signs to aether, one of science’s greatest errors.
…
Ever since, his anti-symbolic project has just increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in among science’s most important journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation but for straight-out replacement. Later, Hinton told an event of European Union leaders that investing any additional money in symbol-manipulating techniques was “a big mistake,” likening it to investing in internal combustion engines in the period of electrical automobiles. [98]
Part of these disagreements may be due to unclear terms:
Turing award winner Judea Pearl provides a review of artificial intelligence which, unfortunately, conflates the terms maker learning and deep learning. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of professional systems dispossessed of any capability to learn. Using the terminology needs clarification. Artificial intelligence is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the differences to deep knowing being the option of representation, localist logical instead of dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not practically production rules written by hand. An appropriate meaning of AI issues knowledge representation and thinking, self-governing multi-agent systems, preparation and argumentation, as well as learning. [99]
Situated robotics: the world as a model
Another review of symbolic AI is the embodied cognition approach:
The embodied cognition method declares that it makes no sense to think about the brain independently: cognition occurs within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s functioning exploits regularities in its environment, including the rest of its body. Under the embodied cognition technique, robotics, vision, and other sensing units become main, not peripheral. [100]
Rodney Brooks created behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this approach, is seen as an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or distributed, as not just unneeded, but as harmful. Instead, he developed the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a various function and needs to function in the genuine world. For example, the first robot he explains in Intelligence Without Representation, has three layers. The bottom layer analyzes finder sensing units to prevent items. The middle layer causes the robotic to wander around when there are no challenges. The top layer triggers the robot to go to more far-off locations for additional expedition. Each layer can momentarily hinder or reduce a lower-level layer. He criticized AI scientists for defining AI problems for their systems, when: “There is no clean department in between perception (abstraction) and thinking in the genuine world.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of simple finite state machines.” [102] In the Nouvelle AI technique, “First, it is critically important to check the Creatures we develop in the real world; i.e., in the very same world that we people occupy. It is dreadful to fall into the temptation of evaluating them in a simplified world initially, even with the finest intentions of later moving activity to an unsimplified world.” [103] His emphasis on real-world screening remained in contrast to “Early work in AI focused on games, geometrical issues, symbolic algebra, theorem proving, and other formal systems” [104] and the usage of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has advantages, however has actually been criticized by the other methods. Symbolic AI has been criticized as disembodied, responsible to the qualification issue, and bad in dealing with the affective issues where deep finding out excels. In turn, connectionist AI has actually been slammed as poorly fit for deliberative step-by-step problem fixing, integrating understanding, and handling planning. Finally, Nouvelle AI stands out in reactive and real-world robotics domains but has actually been slammed for problems in including learning and understanding.
Hybrid AIs including several of these techniques 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 responses and said that Al is for that reason impossible; we now see a lot of these exact same areas going through ongoing research and advancement leading to increased capability, not impossibility. [100]
Expert system.
Automated planning and scheduling
Automated theorem proving
Belief revision
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint programming
Deep knowing
First-order logic
GOFAI
History of artificial intelligence
Inductive logic programming
Knowledge-based systems
Knowledge representation and thinking
Logic programming
Artificial intelligence
Model checking
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as said: “This is AI, so we don’t care if it’s mentally genuine”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he said “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck composes that there are “2 significant branches of expert system: one targeted at producing intelligent behavior regardless of how it was achieved, and the other focused on modeling intelligent processes found 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 precisely like pigeons that they can fool even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep learning 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 Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep learning 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 mistakes”. 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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