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Symbolic Artificial Intelligence
In expert system, symbolic artificial intelligence (also referred to as classical synthetic intelligence or logic-based expert system) [1] [2] is the term for the collection of all techniques in expert system research study that are based upon top-level symbolic (human-readable) representations of problems, reasoning and search. [3] Symbolic AI used tools such as logic shows, production guidelines, semantic nets and frames, and it developed applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in seminal ideas in search, symbolic shows languages, agents, 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 from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were persuaded that symbolic techniques would eventually succeed in creating a maker with artificial basic intelligence and considered this the supreme goal 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 guarantees and was followed by the very first AI Winter as moneying dried up. [5] [6] A 2nd boom (1969-1986) took place with the rise of specialist systems, their promise of recording business competence, and a passionate corporate accept. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later dissatisfaction. [8] Problems with difficulties in understanding acquisition, keeping big knowledge bases, and brittleness in dealing with out-of-domain issues arose. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists focused on dealing with hidden issues in handling unpredictability and in understanding acquisition. [10] Uncertainty was attended to with official techniques such as hidden Markov designs, Bayesian thinking, and analytical relational learning. [11] [12] Symbolic device discovering attended to the knowledge acquisition issue with contributions including 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 technique, had been pursued from early days and reemerged highly 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 considered as effective till about 2012: “Until Big Data ended up being commonplace, the general agreement in the Al neighborhood was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other methods. … A transformation can be found in 2012, when a number of individuals, including a group of scientists dealing with Hinton, exercised a way to utilize the power of GPUs to enormously increase the power of neural networks.” [16] Over the next several years, deep knowing had amazing success in dealing with vision, speech recognition, speech synthesis, image generation, and maker translation. However, given that 2020, as intrinsic problems with predisposition, explanation, comprehensibility, and robustness became more obvious with deep knowing methods; an increasing number of AI have actually called for integrating the very best of both the symbolic and neural network techniques [17] [18] and resolving areas that both methods have problem with, such as sensible thinking. [16]
A brief history of symbolic AI to today day follows listed below. Time durations and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia article on the History of AI, with dates and titles varying slightly for increased clarity.
The very first AI summertime: unreasonable spirit, 1948-1966
Success at early attempts in AI happened in 3 primary locations: synthetic neural networks, knowledge representation, and heuristic search, adding to high expectations. This area sums up Kautz’s reprise of early AI history.
Approaches inspired by human or animal cognition or habits
Cybernetic techniques tried to reproduce the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based on a preprogrammed neural internet, was built 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, written by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it had the ability to prove 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to create a domain-independent problem solver, GPS (General Problem Solver). GPS resolved problems represented with official operators by means of state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic techniques achieved great success at simulating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one established its own design of research study. Earlier techniques based upon cybernetics or synthetic neural networks were deserted or pressed 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 synthetic intelligence, in addition to cognitive science, operations research study and management science. Their research study team used the results of mental experiments to establish programs that simulated the techniques that people utilized to fix issues. [22] [23] This tradition, centered 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 understanding that we will see later on utilized in specialist systems, early symbolic AI scientists discovered another more basic application of understanding. These were called heuristics, guidelines of thumb that assist a search in promising directions: “How can non-enumerative search be useful when the underlying issue is significantly tough? The approach promoted by Simon and Newell is to employ heuristics: quick algorithms that may stop working on some inputs or output suboptimal options.” [26] Another crucial advance was to discover a way to apply these heuristics that guarantees an option will be found, if there is one, not enduring the periodic fallibility of heuristics: “The A * algorithm offered a general frame for complete and optimum heuristically guided search. A * is used as a subroutine within virtually 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 formal thinking highlighting first-order reasoning, in addition to efforts to deal with 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 imitate the specific systems of human thought, but might rather search for the essence of abstract reasoning and problem-solving with reasoning, [27] despite whether individuals utilized the very same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official reasoning to resolve a wide range of issues, consisting of understanding representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and somewhere else in Europe which caused the advancement of the programs language Prolog and the science of reasoning shows. [32] [33]
Modeling implicit common-sense knowledge with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that solving challenging problems in vision and natural language processing needed advertisement hoc solutions-they argued that no easy and basic principle (like reasoning) would catch all the aspects of smart behavior. Roger Schank explained their “anti-logic” methods as “scruffy” (rather than the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, given that they must be built by hand, one complex principle at a time. [38] [39] [40]
The first AI winter season: crushed dreams, 1967-1977
The very first AI winter season was a shock:
During the first AI summer season, many individuals thought that maker intelligence might be achieved in just a couple of years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research to use AI to fix problems of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to produce self-governing tanks for the battlefield. Researchers had actually started to recognize that accomplishing AI was going to be much harder than was supposed a decade earlier, however a combination of hubris and disingenuousness led many university and think-tank scientists to accept financing with promises of deliverables that they ought to have understood they might not satisfy. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been produced, and a remarkable reaction embeded in. New DARPA management canceled existing AI funding programs.
Outside of the United States, the most fertile ground for AI research was the United Kingdom. The AI winter season in the UK was stimulated on not so much by disappointed military leaders as by competing academics who saw AI researchers as charlatans and a drain on research study financing. A teacher of used mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research in the country. The report specified that all of the issues being dealt with in AI would be much better handled by scientists from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues might never scale to real-world applications due to combinatorial explosion. [41]
The 2nd AI summertime: knowledge is power, 1978-1987
Knowledge-based systems
As constraints with weak, domain-independent methods became increasingly more apparent, [42] researchers from all three customs began to build understanding into AI applications. [43] [7] The understanding transformation was driven by the realization that understanding underlies high-performance, domain-specific AI applications.
Edward Feigenbaum stated:
– “In the understanding lies the power.” [44]
to explain that high efficiency in a specific domain requires both basic 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 understand a good deal about the world in which it runs.
( 2) A possible extension of that concept, called the Breadth Hypothesis: there are two additional capabilities required for intelligent behavior in unforeseen scenarios: falling back on progressively basic understanding, and analogizing to specific but distant knowledge. [45]
Success with specialist systems
This “understanding transformation” led to the development and implementation of expert systems (introduced by Edward Feigenbaum), the first commercially successful type of AI software application. [46] [47] [48]
Key expert systems were:
DENDRAL, which found the structure of organic particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested additional laboratory tests, when essential – by translating laboratory results, patient history, and medical professional observations. “With about 450 rules, MYCIN had the ability to perform as well as some specialists, and considerably much better than junior physicians.” [49] INTERNIST and CADUCEUS which tackled internal medicine diagnosis. Internist attempted to capture the competence of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might eventually detect up to 1000 different illness.
– GUIDON, which revealed how a knowledge base built for specialist problem solving could be repurposed for teaching. [50] XCON, to set up VAX computer systems, a then laborious process that might take up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is thought about the first specialist system that relied on knowledge-intensive problem-solving. It is explained listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
Among individuals at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I informed him I wanted an induction “sandbox”, he said, “I have just the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we started the DENDRAL Project: I was good at heuristic search techniques, and he had an algorithm that was excellent at producing the chemical issue space.
We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, creator of the chemical behind the contraceptive pill, and also one of the world’s most appreciated mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We started to include to their understanding, developing knowledge of engineering as we went along. These experiments amounted 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 understanding lies the power. That was the big idea. In my profession that is the huge, “Ah ha!,” and it wasn’t the way AI was being done formerly. Sounds easy, but it’s most likely AI’s most effective generalization. [51]
The other professional systems discussed above followed DENDRAL. MYCIN exhibits the classic professional system architecture of a knowledge-base of guidelines paired to a symbolic thinking system, including making use of certainty aspects to deal with uncertainty. GUIDON demonstrates how an explicit knowledge base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a specific kind of knowledge-based application. Clancey revealed that it was not enough just to utilize MYCIN’s guidelines for instruction, but that he likewise required to add guidelines for discussion management and student modeling. [50] XCON is significant due to the fact that of the millions of dollars it saved DEC, which triggered the specialist system boom where most all major corporations in the US had professional systems groups, to capture corporate expertise, preserve it, and automate it:
By 1988, DEC’s AI group had 40 specialist systems released, with more on the way. DuPont had 100 in usage and 500 in development. Nearly every significant U.S. corporation had its own Al group and was either using or examining specialist systems. [49]
Chess specialist knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the aid of symbolic AI, to win in a game of chess versus the world champion at that time, Garry Kasparov. [52]
Architecture of knowledge-based and skilled systems
A crucial element of the system architecture for all professional systems is the understanding base, which stores realities and rules for analytical. [53] The easiest technique for a skilled system understanding base is simply a collection or network of production guidelines. Production rules connect symbols in a relationship comparable to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what concerns to ask, using human-readable symbols. For instance, OPS5, CLIPS and their followers Jess and Drools operate in this style.
Expert systems can operate in either a forward chaining – from evidence to conclusions – or backwards chaining – from objectives to needed information and prerequisites – manner. More sophisticated knowledge-based systems, such as Soar can likewise perform meta-level thinking, that is thinking about their own reasoning in terms of choosing how to fix problems and keeping track of the success of problem-solving methods.
Blackboard systems are a 2nd sort of knowledge-based or skilled system architecture. They model a community of specialists incrementally contributing, where they can, to fix an issue. The problem is represented in multiple levels of abstraction or alternate views. The experts (understanding sources) volunteer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is updated as the issue circumstance changes. A controller decides how helpful each contribution is, and who should make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was originally motivated by research studies of how humans prepare to carry out multiple jobs in a trip. [55] An innovation of BB1 was to apply the very same blackboard model to resolving 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 proceeding and could change from one method to another as conditions – such as objectives or times – altered. BB1 has actually been applied in several domains: construction site preparation, intelligent tutoring systems, and real-time patient monitoring.
The 2nd AI winter, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP machines particularly targeted to accelerate the development of AI applications and research study. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were offering skilled system shells, training, and speaking with to corporations.
Unfortunately, the AI boom did not last and Kautz finest explains the 2nd AI winter season that followed:
Many factors can be used for the arrival of the second AI winter. The hardware business stopped working when much more economical basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the marketplace. Many industrial releases of professional systems were terminated when they proved too costly to preserve. Medical professional systems never ever caught on for several factors: the problem in keeping them up to date; the challenge for medical specialists to learn how to utilize an overwelming variety of different professional systems for different medical conditions; and perhaps most crucially, the reluctance of physicians to trust a computer-made diagnosis over their gut instinct, even for specific domains where the specialist systems could surpass an average doctor. Venture capital cash deserted AI almost overnight. The world AI conference IJCAI hosted an enormous and extravagant trade convention and countless nonacademic guests in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]
Including more strenuous structures, 1993-2011
Uncertain reasoning
Both statistical methods and extensions to reasoning were attempted.
One analytical technique, concealed Markov models, had currently been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a noise however efficient way of managing unsure reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were applied effectively in expert systems. [57] Even later, in the 1990s, statistical relational learning, a technique that combines probability with rational solutions, enabled likelihood to be combined with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to assistance were also tried. For instance, non-monotonic reasoning could be used with truth maintenance systems. A reality upkeep system tracked assumptions and validations for all inferences. It enabled reasonings 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 applied to produce it and after that continuing through underlying reasonings and rules all the way back to root presumptions. [58] Lofti Zadeh had introduced a different type of extension to deal with the representation of ambiguity. For example, in choosing how “heavy” or “high” a guy is, there is frequently no clear “yes” or “no” response, 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 logic further offered a method for propagating mixes of these worths through sensible formulas. [59]
Machine knowing
Symbolic machine discovering techniques were investigated to resolve the knowledge acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test method to generate possible rule hypotheses to test against spectra. Domain and job knowledge lowered the number of prospects tested to a manageable size. Feigenbaum described Meta-DENDRAL as
… the culmination of my imagine the early to mid-1960s having to do with theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it utilized layers of understanding to steer and prune the search. That understanding got in there 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 infer the understanding of mass spectrometry that DENDRAL could use to resolve specific hypothesis formation issues. We did it. We were even able to publish new knowledge of mass spectrometry in the Journal of the American Chemical Society, providing credit only in a footnote that a program, Meta-DENDRAL, in fact did it. We were able to do something that had actually been a dream: to have a computer system program come up with a brand-new and publishable piece of science. [51]
In contrast to the knowledge-intensive technique of Meta-DENDRAL, Ross Quinlan created a domain-independent method to analytical classification, decision tree learning, beginning initially with ID3 [60] and after that later extending its capabilities to C4.5. [61] The choice trees produced are glass box, interpretable classifiers, with human-interpretable classification guidelines.
Advances were made in understanding artificial intelligence theory, too. Tom Mitchell presented version space learning which explains knowing as a search through an area of hypotheses, with upper, more basic, and lower, more particular, boundaries including all feasible hypotheses constant with the examples seen so far. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]
Symbolic maker discovering incorporated more than learning by example. E.g., John Anderson offered a cognitive design of human knowing where skill practice leads to a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a student may discover to apply “Supplementary angles are 2 angles whose procedures sum 180 degrees” as a number of various procedural guidelines. E.g., one rule might say 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 used effectively to model elements of human cognition, such as discovering and retention. ACT-R is likewise utilized in intelligent tutoring systems, called cognitive tutors, to effectively teach geometry, computer programming, and algebra to school kids. [64]
Inductive reasoning programs 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) could manufacture Prolog programs from examples. [65] John R. Koza used hereditary algorithms to program synthesis to create genetic programming, which he used to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic technique to program synthesis that manufactures a functional program in the course of showing its specs to be correct. [66]
As an alternative to reasoning, Roger Schank introduced case-based thinking (CBR). The CBR approach detailed in his book, Dynamic Memory, [67] focuses first on remembering key problem-solving cases for future usage and generalizing them where proper. When faced with a brand-new problem, CBR retrieves the most comparable previous case and adapts it to the specifics of the current issue. [68] Another option to reasoning, hereditary algorithms and hereditary shows are based on an evolutionary model of knowing, where sets of rules are encoded into populations, the guidelines govern the habits of people, and selection of the fittest prunes out sets of unsuitable rules over many generations. [69]
Symbolic artificial intelligence was used to discovering principles, rules, heuristics, and analytical. Approaches, besides those above, consist of:
1. Learning from guideline or advice-i.e., taking human direction, presented as advice, and figuring out how to operationalize it in specific scenarios. For instance, in a video game of Hearts, learning precisely how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter expert (SME) feedback throughout training. When problem-solving stops working, querying the expert to either learn a brand-new prototype for problem-solving or to find out a brand-new explanation as to precisely why one prototype is more pertinent than another. For instance, the program Protos learned to diagnose tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing problem solutions based on similar issues seen in the past, and then modifying their solutions to fit a new scenario or domain. [72] [73] 4. Apprentice learning systems-learning novel options to issues by observing human problem-solving. Domain knowledge explains why unique solutions are appropriate and how the service can be generalized. LEAP found out how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating jobs to bring out experiments and after that finding out from the outcomes. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human players at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., looking for helpful macro-operators to be gained from sequences of standard analytical actions. Good macro-operators streamline analytical by permitting issues to be resolved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now
With the rise of deep knowing, the symbolic AI method has actually been compared to deep knowing as complementary “… with parallels having been drawn often times by AI scientists between Kahneman’s research on human reasoning and decision making – reflected in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in principle be modelled by deep learning and symbolic thinking, respectively.” In this view, symbolic reasoning is more apt for deliberative reasoning, planning, and explanation while deep knowing is more apt for fast pattern recognition in perceptual applications with noisy information. [17] [18]
Neuro-symbolic AI: incorporating neural and symbolic methods
Neuro-symbolic AI efforts to incorporate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI capable of reasoning, learning, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the efficient construction of abundant computational cognitive models requires the combination of sound symbolic reasoning and effective (maker) knowing models. Gary Marcus, likewise, argues that: “We can not construct abundant cognitive designs in a sufficient, automatic way without the set of three of hybrid architecture, rich prior knowledge, and advanced strategies for reasoning.”, [79] and in particular: “To develop a robust, knowledge-driven method to AI we need to have the machinery of symbol-manipulation in our toolkit. Too much of useful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we know of that can control such abstract understanding reliably is the device of sign manipulation. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based on a requirement to address the 2 kinds of believing gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 elements, System 1 and System 2. System 1 is quickly, automatic, intuitive and unconscious. System 2 is slower, detailed, and specific. System 1 is the kind utilized for pattern acknowledgment while System 2 is far better fit for planning, reduction, and deliberative thinking. In this view, deep learning best models the very first sort of thinking while symbolic thinking finest models the second kind and both are required.
Garcez and Lamb describe research in this area as being continuous for at least the previous twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has been held every year given that 2005, see http://www.neural-symbolic.org/ for details.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The integration of the symbolic and connectionist paradigms of AI has been pursued by a relatively little research neighborhood over the last 20 years and has yielded a number of significant results. Over the last decade, neural symbolic systems have been revealed capable of 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 capable of representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and fragments of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of issues in the locations of bioinformatics, control engineering, software verification and adjustment, visual intelligence, ontology learning, and computer system games. [78]
Approaches for combination are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, along with some examples, follows:
– Symbolic Neural symbolic-is the present approach of many neural designs in natural language processing, where words or subword tokens are both the ultimate input and output of big language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural methods. In this case the symbolic method is Monte Carlo tree search and the neural methods discover how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to interpret perceptual data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to generate or identify training information that is consequently discovered by a deep knowing design, e.g., to train a neural model for symbolic computation by using a Macsyma-like symbolic mathematics system to create or label 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 knowledge base rules and terms. Logic Tensor Networks [86] also fall under this category.
– Neural [Symbolic] -enables a neural design to straight call a symbolic reasoning engine, e.g., to perform an action or assess a state.
Many crucial research concerns remain, such as:
– What is the very best way 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 understanding be learned and reasoned about?
– How can abstract knowledge that is tough to encode realistically be dealt with?
Techniques and contributions
This area offers an introduction of methods and contributions in a general context resulting in numerous other, more detailed posts in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history area.
AI shows languages
The crucial AI shows language in the US throughout the last symbolic AI boom period was LISP. LISP is the second oldest shows language after FORTRAN and was produced in 1958 by John McCarthy. LISP provided the very first read-eval-print loop to support quick program advancement. Compiled functions might be freely blended with translated functions. Program tracing, stepping, and breakpoints were likewise supplied, in addition to the capability to alter values or functions and continue from breakpoints or mistakes. It had the very first self-hosting compiler, suggesting that the compiler itself was initially composed in LISP and after that ran interpretively to assemble the compiler code.
Other essential innovations pioneered by LISP that have actually infected other programs languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs might operate on, allowing the simple meaning of higher-level languages.
In contrast to the US, in Europe the key AI shows language throughout that very same period was Prolog. Prolog offered a built-in shop of realities and stipulations that might be queried by a read-eval-print loop. The shop could function as a knowledge base and the stipulations might act as guidelines or a limited type of logic. As a subset of first-order logic Prolog was based upon Horn provisions with a closed-world assumption-any realities not known were thought about false-and a distinct name presumption for primitive terms-e.g., the identifier barack_obama was considered to refer to precisely one item. Backtracking and marriage are integrated to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of reasoning programs, which was developed by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. 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 explain programs are straight interpreted to run the programs defined. No specific series of actions is needed, as is the case with imperative programming languages.
Japan promoted Prolog for its Fifth Generation Project, intending to construct unique hardware for high performance. Similarly, LISP devices were developed to run LISP, but as the second AI boom turned to bust these companies could not take on brand-new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history area for more information.
Smalltalk was another influential AI shows language. For instance, it presented metaclasses and, along with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits several inheritance, in addition to incremental extensions to both classes and metaclasses, thus supplying a run-time meta-object protocol. [88]
For other AI programming languages see this list of shows languages for expert system. Currently, Python, a multi-paradigm shows language, is the most popular shows language, partially due to its comprehensive bundle library that supports data science, natural language processing, and deep knowing. Python includes a read-eval-print loop, practical elements such as higher-order functions, and object-oriented programs that includes metaclasses.
Search
Search emerges in lots of sort of problem fixing, consisting of planning, constraint complete satisfaction, and playing games such as checkers, chess, and go. The finest known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven stipulation learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and thinking
Multiple different techniques to represent knowledge and after that reason with those representations have actually been investigated. Below is a fast overview of approaches to knowledge representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and logic are all techniques to modeling understanding such as domain knowledge, problem-solving understanding, and the semantic significance of language. Ontologies model crucial concepts 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 considered as an ontology. YAGO incorporates 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 currently being utilized.
Description logic is a logic for automated category of ontologies and for detecting inconsistent 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 reasoning is more general than description reasoning. The automated theorem provers discussed below can prove theorems in first-order reasoning. Horn stipulation logic is more limited than first-order logic and is used in reasoning programming languages such as Prolog. Extensions to first-order logic include temporal reasoning, to deal with time; epistemic logic, to factor about agent understanding; modal reasoning, to manage possibility and need; and probabilistic reasonings to manage logic and probability together.
Automatic theorem proving
Examples of automated theorem provers for first-order logic are:
Prover9.
ACL2.
Vampire.
Prover9 can be used in combination with the Mace4 model checker. ACL2 is a theorem prover that can handle proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise known as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have a specific understanding base, generally of rules, to improve reusability across domains by separating procedural code and domain understanding. A different reasoning engine processes rules and includes, deletes, or modifies a knowledge store.
Forward chaining inference engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more limited logical representation is used, Horn Clauses. Pattern-matching, particularly marriage, is used in Prolog.
A more flexible type of problem-solving happens when reasoning about what to do next takes place, rather than simply choosing one of the available actions. This type of meta-level thinking is used in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R might have additional abilities, such as the ability to assemble regularly used understanding into higher-level portions.
Commonsense thinking
Marvin Minsky initially proposed frames as a way of interpreting common visual situations, such as a workplace, and Roger Schank extended this idea to scripts for typical routines, such as dining out. Cyc has tried to catch beneficial sensible knowledge and has “micro-theories” to handle specific sort of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about ignorant physics, such as what occurs when we heat up a liquid in a pot on the range. We anticipate it to heat and possibly boil over, although we may not understand its temperature, its boiling point, or other information, such as air pressure.
Similarly, Allen’s temporal period algebra is a simplification of thinking about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be solved with restraint solvers.
Constraints and constraint-based thinking
Constraint solvers perform a more minimal type of inference than first-order logic. They can streamline sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, along with solving other sort of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programming can be utilized to fix scheduling issues, for instance with constraint managing rules (CHR).
Automated preparation
The General Problem Solver (GPS) cast preparation as problem-solving utilized means-ends analysis to produce plans. STRIPS took a different method, viewing planning as theorem proving. Graphplan takes a least-commitment method to preparation, instead of sequentially picking actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is a method to planning where a planning problem is lowered to a Boolean satisfiability issue.
Natural language processing
Natural language processing concentrates on dealing with language as data to carry out tasks such as identifying topics without always understanding the intended significance. Natural language understanding, on the other hand, constructs a significance representation and utilizes that for further processing, such as responding to questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all elements of natural language processing long dealt with by symbolic AI, but since enhanced by deep learning methods. In symbolic AI, discourse representation theory and first-order reasoning have been utilized to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector parts are interpretable as ideas called by Wikipedia short articles.
New deep learning techniques based upon Transformer models have now eclipsed these earlier symbolic AI methods and obtained cutting edge performance in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the significance of the vector components is nontransparent.
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 standard textbook on expert system is arranged to reflect representative architectures of increasing elegance. [91] The sophistication of representatives varies from simple reactive agents, to those with a model of the world and automated planning abilities, potentially a BDI agent, i.e., one with beliefs, desires, and objectives – or alternatively a reinforcement learning model found out over time to select actions – as much as a combination 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 among themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The representatives need not all have the exact same internal architecture. Advantages of multi-agent systems consist of the ability to divide work among the agents and to increase fault tolerance when agents are lost. Research problems include how representatives reach consensus, dispersed issue resolving, multi-agent knowing, multi-agent planning, and dispersed constraint optimization.
Controversies arose from early in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and in between those who accepted AI however declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mostly from philosophers, on intellectual premises, however likewise from financing firms, especially during the two AI winters.
The Frame Problem: knowledge representation obstacles for first-order logic
Limitations were found in using basic first-order reasoning to factor about dynamic domains. Problems were discovered both with concerns to specifying the preconditions for an action to be successful and in supplying axioms for what did not change after an action was carried out.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] An easy example takes place in “proving that a person individual could enter into discussion with another”, as an axiom asserting “if an individual has a telephone he still has it after looking up a number in the telephone book” would be needed for the deduction to prosper. Similar axioms would be required for other domain actions to define what did not alter.
A similar issue, called the Qualification Problem, takes place in trying to specify the prerequisites for an action to be successful. An unlimited number of pathological conditions can be envisioned, e.g., a banana in a tailpipe could prevent an automobile from operating correctly.
McCarthy’s approach to fix the frame issue was circumscription, a sort of non-monotonic reasoning where reductions might be made from actions that require just define what would alter while not needing to explicitly specify whatever that would not alter. Other non-monotonic logics provided reality maintenance systems that modified beliefs causing contradictions.
Other methods of managing more open-ended domains included probabilistic thinking systems and machine knowing to learn brand-new ideas and rules. McCarthy’s Advice Taker can be considered as a motivation here, as it could integrate brand-new knowledge supplied by a human in the type of assertions or guidelines. For example, speculative symbolic maker discovering systems checked out the ability to take top-level natural language recommendations and to translate it into domain-specific actionable rules.
Similar to the problems in dealing with dynamic domains, sensible thinking is also difficult to capture in formal reasoning. Examples of common-sense reasoning include implicit reasoning about how individuals believe or general understanding of daily occasions, items, and living creatures. This type of understanding is considered granted and not considered as noteworthy. Common-sense reasoning is an open location of research study and challenging both for symbolic systems (e.g., Cyc has actually tried to catch crucial parts of this understanding over more than a decade) and neural systems (e.g., self-driving cars and trucks that do not understand not to drive into cones or not to strike pedestrians strolling a bicycle).
McCarthy viewed his Advice Taker as having sensible, however his definition of sensible was various than the one above. [94] He specified a program as having typical sense “if it instantly deduces for itself a sufficiently broad class of instant repercussions of anything it is told and what it currently knows. “
Connectionist AI: philosophical difficulties and sociological conflicts
Connectionist approaches consist of earlier deal with 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 actually been outlined among connectionists:
1. Implementationism-where connectionist architectures execute the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down totally, and connectionist architectures underlie intelligence and are completely enough to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are needed for intelligence
Olazaran, in his sociological history of the debates within the neural network neighborhood, described the moderate connectionism deem basically compatible with current research in neuro-symbolic hybrids:
The third and last position I wish to examine here is what I call the moderate connectionist view, a more diverse view of the existing debate between connectionism and symbolic AI. Among the scientists who has actually 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 defended hybrid (partly symbolic, partly connectionist) systems. He declared that (at least) two kinds of theories are needed in order to study and model cognition. On the one hand, for some information-processing tasks (such as pattern recognition) connectionism has benefits over symbolic designs. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative sign adjustment processes) the symbolic paradigm provides sufficient models, and not just “approximations” (contrary to what radical connectionists would claim). [97]
Gary Marcus has claimed that the animus in the deep learning community against symbolic techniques now might be more sociological than philosophical:
To think that we can merely abandon symbol-manipulation is to suspend disbelief.
And yet, for the many part, that’s how most present AI earnings. Hinton and lots of others have striven to get rid of symbols entirely. The deep knowing hope-seemingly grounded not so much in science, but in a sort of historical grudge-is that intelligent behavior will emerge purely from the confluence of massive information and deep learning. Where classical computers and software resolve tasks by defining sets of symbol-manipulating guidelines committed to specific jobs, such as editing a line in a word processor or performing an estimation in a spreadsheet, neural networks normally attempt to fix jobs by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his colleagues have actually been vehemently “anti-symbolic”:
When deep learning reemerged in 2012, it was with a type of take-no-prisoners attitude that has actually identified many of the last decade. 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.
…
Ever since, his anti-symbolic project has actually only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s crucial journals, Nature. It closed with a direct attack on symbol manipulation, calling not for reconciliation however for outright replacement. Later, Hinton told an event of European Union leaders that investing any additional cash in symbol-manipulating methods was “a huge error,” likening it to buying internal combustion engines in the age of electrical cars. [98]
Part of these disputes may be because of unclear terminology:
Turing award winner Judea Pearl offers a review of artificial intelligence which, sadly, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of specialist systems dispossessed of any ability to find out. Using the terms requires explanation. Machine knowing is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the choice of representation, localist rational rather than distributed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not simply about production guidelines written by hand. A proper meaning of AI issues understanding representation and reasoning, self-governing multi-agent systems, preparation and argumentation, as well as knowing. [99]
Situated robotics: the world as a model
Another critique of symbolic AI is the embodied cognition approach:
The embodied cognition method declares that it makes no sense to consider the brain separately: cognition happens within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s working exploits consistencies in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units become main, not peripheral. [100]
Rodney Brooks developed behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this technique, is seen as an alternative to both symbolic AI and connectionist AI. His technique declined representations, either symbolic or distributed, as not only unnecessary, but as detrimental. Instead, he created the subsumption architecture, a layered architecture for embodied agents. Each layer achieves a different function and needs to operate in the real world. For instance, the first robot he explains in Intelligence Without Representation, has 3 layers. The bottom layer interprets finder sensors to avoid things. The middle layer triggers the robot to roam around when there are no barriers. The leading layer causes the robotic to go to more remote places for additional exploration. Each layer can briefly inhibit or suppress a lower-level layer. He criticized AI researchers for specifying AI problems for their systems, when: “There is no tidy department between understanding (abstraction) and reasoning in the genuine world.” [101] He called his robots “Creatures” and each layer was “composed of a fixed-topology network of basic limited state machines.” [102] In the Nouvelle AI method, “First, it is critically important to test the Creatures we build in the genuine world; i.e., in the very same world that we people occupy. It is disastrous to fall under the temptation of testing them in a simplified world first, even with the very best intentions of later transferring activity to an unsimplified world.” [103] His focus on real-world testing remained in contrast to “Early operate in AI concentrated on games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, but has been slammed by the other approaches. Symbolic AI has actually been slammed as disembodied, liable to the certification problem, and poor in dealing with the perceptual issues where deep finding out excels. In turn, connectionist AI has been slammed as inadequately matched for deliberative detailed issue solving, including knowledge, and handling preparation. Finally, Nouvelle AI excels in reactive and real-world robotics domains however has been criticized for difficulties in including knowing and understanding.
Hybrid AIs incorporating several of these methods are presently seen as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw locations where AI did not have complete answers and said that Al is for that reason difficult; we now see a lot of these very same areas undergoing continued research and development resulting in increased capability, not impossibility. [100]
Expert system.
Automated planning and scheduling
Automated theorem proving
Belief modification
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep learning
First-order logic
GOFAI
History of expert system
Inductive logic programs
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
Model monitoring
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of synthetic intelligence
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy when said: “This is AI, so we don’t care if it’s mentally real”. [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he said “Expert system is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 major branches of expert system: one intended at producing smart habits despite how it was accomplished, and the other targeted at modeling intelligent processes found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not specify the objective of their field as making ‘machines that fly so precisely like pigeons that they can deceive even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing with symbolic expert system: representing objects and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep 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 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.
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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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