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Symbolic Artificial Intelligence
In expert system, symbolic expert system (also referred to as classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all approaches in synthetic intelligence research study that are based upon top-level symbolic (human-readable) representations of issues, reasoning and search. [3] Symbolic AI utilized tools such as reasoning programs, production guidelines, semantic internet and frames, and it established applications such as knowledge-based systems (in specific, skilled systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in critical concepts in search, symbolic programs 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 study from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would eventually prosper in developing a machine with synthetic general intelligence and considered this the supreme objective of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and guarantees and was followed by the very first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) happened with the rise of professional systems, their guarantee of recording corporate know-how, and an enthusiastic business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later dissatisfaction. [8] Problems with problems in understanding acquisition, preserving big understanding bases, and brittleness in dealing with out-of-domain issues developed. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on attending to hidden issues in dealing with unpredictability and in knowledge acquisition. [10] Uncertainty was addressed with official approaches such as hidden Markov models, Bayesian thinking, and statistical relational learning. [11] [12] Symbolic machine finding out dealt with the understanding acquisition issue with contributions including Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree learning, case-based knowing, and inductive reasoning programming to learn relations. [13]
Neural networks, a subsymbolic technique, had actually 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 seen as successful up until about 2012: “Until Big Data ended up being commonplace, the basic consensus in the Al community was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other approaches. … A revolution came in 2012, when a number of individuals, including a team of scientists dealing with Hinton, exercised a way to use the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next several years, deep learning had amazing success in dealing with vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, since 2020, as inherent difficulties with predisposition, explanation, coherence, and effectiveness ended up being more obvious with deep learning approaches; an increasing variety of AI scientists have required combining the very best of both the symbolic and neural network approaches [17] [18] and dealing with locations that both techniques have difficulty with, such as sensible reasoning. [16]
A brief history of symbolic AI to the present day follows below. Period and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia post on the History of AI, with dates and titles varying slightly for increased clearness.
The first AI summer: unreasonable vitality, 1948-1966
Success at early efforts in AI happened in three primary areas: artificial 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 approaches attempted to reproduce the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based upon 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, support learning, and situated robotics. [20]
An essential early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to prove 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to develop a domain-independent problem solver, GPS (General Problem Solver). GPS solved problems represented with formal operators through state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic methods achieved excellent success at imitating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in 4 institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Every one developed its own design of research study. Earlier techniques based upon cybernetics or synthetic neural networks were deserted or pushed 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, as well as cognitive science, operations research and management science. Their research group utilized the outcomes of mental experiments to establish programs that simulated the methods that individuals used to fix problems. [22] [23] This tradition, focused at Carnegie Mellon University would eventually culminate in the advancement of the Soar architecture in the middle 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific type of understanding that we will see later on utilized in expert systems, early symbolic AI researchers found another more general application of understanding. These were called heuristics, guidelines of thumb that direct a search in promising directions: “How can non-enumerative search be practical when the underlying issue is greatly hard? The approach advocated by Simon and Newell is to use heuristics: quick algorithms that might fail on some inputs or output suboptimal options.” [26] Another important advance was to find a method to use these heuristics that guarantees a service will be discovered, if there is one, not holding up against the occasional fallibility of heuristics: “The A * algorithm supplied a basic frame for total and optimum heuristically directed search. A * is utilized as a subroutine within practically every AI algorithm today however is still no magic bullet; its warranty of efficiency is purchased the cost of worst-case exponential time. [26]
Early deal with understanding representation and thinking
Early work covered both applications of official reasoning highlighting first-order logic, in addition to efforts to handle common-sense thinking in a less official way.
Modeling official thinking with reasoning: the “neats”
Unlike Simon and Newell, John McCarthy felt that makers did not need to mimic the specific systems of human idea, however could instead look for the essence of abstract thinking and analytical with reasoning, [27] regardless of whether people utilized the same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official logic to fix a wide array of problems, consisting of understanding representation, planning and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and in other places in Europe which caused the development of the shows language Prolog and the science of logic programming. [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] discovered that solving hard issues in vision and natural language processing needed ad hoc solutions-they argued that no simple and general principle (like logic) would catch all the elements of intelligent behavior. Roger Schank explained their “anti-logic” approaches as “shabby” (rather than the “neat” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, because they should be constructed by hand, one complicated concept at a time. [38] [39] [40]
The very first AI winter: crushed dreams, 1967-1977
The first AI winter was a shock:
During the very first AI summer season, many individuals believed that device intelligence might be accomplished in just a couple of years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research study to utilize AI to solve issues of national security; in particular, to automate the translation of Russian to English for intelligence operations and to produce autonomous tanks for the battlefield. Researchers had actually begun to understand that attaining AI was going to be much more difficult than was expected a decade earlier, but a mix of hubris and disingenuousness led many university and think-tank researchers to accept financing with pledges of deliverables that they must have known they could not fulfill. By the mid-1960s neither useful natural language translation systems nor self-governing tanks had been produced, and a remarkable backlash set in. New DARPA leadership canceled existing AI funding programs.
Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the United Kingdom was spurred on not a lot by dissatisfied military leaders as by rival academics who viewed AI researchers as charlatans and a drain on research financing. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the country. The report mentioned that all of the problems being dealt with in AI would be much better managed by scientists from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues could never ever scale to real-world applications due to combinatorial explosion. [41]
The 2nd AI summer: understanding is power, 1978-1987
Knowledge-based systems
As limitations with weak, domain-independent methods ended up being increasingly more apparent, [42] researchers from all three traditions began to build knowledge into AI applications. [43] [7] The knowledge transformation was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– “In the knowledge lies the power.” [44]
to describe that high performance in a particular domain requires both basic and extremely domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to carry out a complicated task well, it needs to know a good deal about the world in which it runs.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are 2 extra capabilities needed for intelligent habits in unforeseen circumstances: falling back on progressively basic understanding, and analogizing to particular but distant knowledge. [45]
Success with expert systems
This “understanding revolution” led to the development and deployment of professional systems (introduced by Edward Feigenbaum), the first commercially successful type of AI software application. [46] [47] [48]
Key professional systems were:
DENDRAL, which discovered the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and suggested additional laboratory tests, when necessary – by translating laboratory results, patient history, and doctor observations. “With about 450 guidelines, MYCIN was able to carry out along with some professionals, and considerably much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which took on internal medicine diagnosis. Internist tried to catch the competence of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately identify up to 1000 various illness.
– GUIDON, which revealed how an understanding base developed for specialist issue fixing could be repurposed for mentor. [50] XCON, to configure VAX computers, a then tiresome process that could use up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is considered the first expert system that relied on knowledge-intensive analytical. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
One of individuals at Stanford interested in computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I told him I desired an induction “sandbox”, he said, “I have simply the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from looking at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was good at producing the chemical issue area.
We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, developer 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 started to contribute to their knowledge, developing knowledge of engineering as we went along. These experiments totaled up to titrating DENDRAL increasingly more understanding. The more you did that, the smarter the program ended up being. We had great outcomes.
The generalization was: in the knowledge lies the power. That was the huge idea. In my profession that is the huge, “Ah ha!,” and it wasn’t the method AI was being done formerly. Sounds simple, however it’s probably AI’s most effective generalization. [51]
The other professional systems mentioned above followed DENDRAL. MYCIN exhibits the classic specialist system architecture of a knowledge-base of rules paired to a symbolic reasoning mechanism, including the usage of certainty elements 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 particular type of knowledge-based application. Clancey showed that it was not enough merely to utilize MYCIN’s guidelines for direction, however that he also needed to include guidelines for dialogue management and trainee modeling. [50] XCON is significant due to the fact that of the millions of dollars it conserved DEC, which set off the expert system boom where most all major corporations in the US had expert systems groups, to catch corporate competence, protect it, and automate it:
By 1988, DEC’s AI group had 40 expert systems deployed, with more en route. DuPont had 100 in use and 500 in advancement. Nearly every significant U.S. corporation had its own Al group and was either using or investigating specialist systems. [49]
Chess specialist knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the assistance of symbolic AI, to win in a game of chess against the world champ at that time, Garry Kasparov. [52]
Architecture of knowledge-based and expert systems
A key part of the system architecture for all professional systems is the knowledge base, which shops truths and guidelines for analytical. [53] The simplest method for an expert system understanding base is simply a collection or network of production rules. Production rules link symbols in a relationship similar to an If-Then statement. The professional system processes the guidelines to make deductions and to determine what extra information it needs, i.e. what concerns to ask, using human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools run in this fashion.
Expert systems can operate in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and prerequisites – manner. Advanced knowledge-based systems, such as Soar can likewise perform meta-level reasoning, that is reasoning about their own thinking in regards to deciding how to resolve problems and keeping an eye on the success of analytical strategies.
Blackboard systems are a second kind of knowledge-based or skilled system architecture. They model a neighborhood of experts incrementally contributing, where they can, to resolve an issue. The problem is represented in multiple levels of abstraction or alternate views. The professionals (knowledge sources) offer their services whenever they recognize they can contribute. Potential analytical actions are represented on an agenda that is updated as the problem situation changes. A controller chooses how helpful each contribution is, and who need to make the next analytical action. One example, the BB1 chalkboard architecture [54] was initially influenced by studies of how human beings plan to perform multiple tasks in a trip. [55] An innovation of BB1 was to use the same chalkboard design to fixing its control issue, i.e., its controller carried out meta-level reasoning with understanding sources that kept an eye on how well a plan or the analytical was continuing and might change from one technique to another as conditions – such as goals or times – altered. BB1 has been applied in multiple domains: construction website preparation, smart tutoring systems, and real-time patient tracking.
The second AI winter, 1988-1993
At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP makers particularly targeted to speed up the development of AI applications and research. In addition, several expert system business, such as Teknowledge and Inference Corporation, were offering expert system shells, training, and consulting to corporations.
Unfortunately, the AI boom did not last and Kautz finest explains the second AI winter that followed:
Many reasons can be offered for the arrival of the 2nd AI winter. The hardware companies failed when far more affordable general Unix workstations from Sun together with good compilers for LISP and Prolog came onto the market. Many commercial deployments of professional systems were discontinued when they proved too pricey to maintain. Medical professional systems never ever captured on for numerous factors: the trouble in keeping them up to date; the challenge for medical professionals to discover how to use an overwelming range of different expert systems for various medical conditions; and maybe most crucially, the reluctance of medical professionals to rely on a computer-made medical diagnosis over their gut instinct, even for specific domains where the expert systems could surpass an average medical professional. Equity capital cash deserted AI almost over night. The world AI conference IJCAI hosted an enormous and lavish trade convention and thousands of nonacademic attendees in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]
Adding in more strenuous structures, 1993-2011
Uncertain reasoning
Both analytical approaches and extensions to reasoning were tried.
One statistical approach, concealed Markov models, had actually already been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a noise however efficient method of dealing with unpredictable reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were applied effectively in specialist systems. [57] Even later, in the 1990s, statistical relational knowing, a technique that combines probability with logical solutions, allowed probability to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to support were also tried. For instance, non-monotonic reasoning could be utilized with truth maintenance systems. A reality upkeep system tracked presumptions and justifications for all reasonings. It enabled inferences to be withdrawn when presumptions were found out to be incorrect or a contradiction was obtained. Explanations might be provided for a reasoning by describing which rules were used to create it and after that continuing through underlying inferences and guidelines all the method back to root assumptions. [58] Lofti Zadeh had introduced a various type of extension to manage the representation of uncertainty. For example, in choosing how “heavy” or “high” a male is, there is regularly no clear “yes” or “no” answer, and a predicate for heavy or high would instead return worths between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy reasoning even more offered a way for propagating mixes of these worths through rational solutions. [59]
Machine learning
Symbolic device discovering methods were investigated to resolve the knowledge acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test technique to create plausible rule hypotheses to test against spectra. Domain and job understanding reduced the number of candidates evaluated to a manageable size. Feigenbaum explained Meta-DENDRAL as
… the conclusion of my dream of 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 knowledge to steer and prune the search. That understanding acted due to the fact that we interviewed individuals. But how did the people get the understanding? By taking a look at countless spectra. So we desired a program that would take a look at thousands of spectra and presume the knowledge of mass spectrometry that DENDRAL could use to resolve specific hypothesis formation issues. We did it. We were even able to publish brand-new understanding of mass spectrometry in the Journal of the American Chemical Society, offering credit only in a footnote that a program, Meta-DENDRAL, in fact did it. We had the ability 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 invented a domain-independent technique to analytical category, decision tree knowing, starting initially with ID3 [60] and then later extending its abilities to C4.5. [61] The decision trees produced are glass box, interpretable classifiers, with human-interpretable classification guidelines.
Advances were made in understanding device learning theory, too. Tom Mitchell presented variation space knowing which describes knowing as an explore an area of hypotheses, with upper, more general, and lower, more specific, boundaries including all practical 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 discovering by example. E.g., John Anderson offered a cognitive model of human knowing where skill practice results in a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a student may find out to apply “Supplementary angles are 2 angles whose measures sum 180 degrees” as several various procedural guidelines. E.g., one guideline might state that if X and Y are extra and you understand X, then Y will be 180 – X. He called his technique “knowledge compilation”. ACT-R has actually been utilized successfully to design elements of human cognition, such as discovering and retention. ACT-R is also utilized in smart tutoring systems, called cognitive tutors, to effectively teach geometry, computer programs, and algebra to school kids. [64]
Inductive logic programming was another approach to finding out that permitted reasoning programs to be synthesized 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 produce hereditary programs, which he used to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more general method to program synthesis that manufactures a functional program in the course of showing its specs to be appropriate. [66]
As an alternative to logic, Roger Schank presented case-based thinking (CBR). The CBR approach detailed in his book, Dynamic Memory, [67] focuses first on keeping in mind key analytical cases for future use and generalizing them where suitable. When faced with a brand-new problem, CBR retrieves the most comparable previous case and adjusts it to the specifics of the existing issue. [68] Another option to reasoning, hereditary algorithms and hereditary programs are based on an evolutionary design of learning, where sets of rules are encoded into populations, the rules govern the habits of individuals, and selection of the fittest prunes out sets of inappropriate guidelines over lots of generations. [69]
Symbolic machine knowing was applied to finding out concepts, guidelines, heuristics, and problem-solving. Approaches, besides those above, consist of:
1. Learning from direction or advice-i.e., taking human instruction, impersonated advice, and figuring out how to operationalize it in specific scenarios. For example, in a video game of Hearts, discovering precisely how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter professional (SME) feedback throughout training. When analytical fails, querying the professional to either find out a new exemplar for problem-solving or to discover a new explanation as to exactly why one prototype is more relevant than another. For example, the program Protos found out to diagnose tinnitus cases by communicating with an audiologist. [71] 3. Learning by analogy-constructing problem services based on comparable problems seen in the past, and after that modifying their solutions to fit a new circumstance or domain. [72] [73] 4. Apprentice knowing systems-learning unique services to issues by observing human problem-solving. Domain understanding explains why unique services are correct and how the option can be generalized. LEAP discovered how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to perform experiments and then learning from the results. Doug Lenat’s Eurisko, for example, learned heuristics to beat human players at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., browsing for helpful macro-operators to be found out from sequences of fundamental analytical actions. Good macro-operators simplify problem-solving by allowing issues to be fixed 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 been compared to deep learning as complementary “… with parallels having been drawn lot of times by AI scientists between Kahneman’s research study on human reasoning and decision making – reflected in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in concept be modelled by deep knowing and symbolic reasoning, respectively.” In this view, symbolic reasoning is more apt for deliberative reasoning, planning, and explanation while deep knowing is more apt for fast pattern acknowledgment in affective applications with noisy data. [17] [18]
Neuro-symbolic AI: incorporating neural and symbolic approaches
Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a manner that addresses strengths and weak points of each, in a complementary style, in order to support robust AI efficient in thinking, finding out, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the reliable building of abundant computational cognitive models demands the combination of sound symbolic thinking and effective (maker) learning models. Gary Marcus, likewise, argues that: “We can not construct rich cognitive models in an appropriate, automatic way without the set of three of hybrid architecture, rich anticipation, and advanced strategies for thinking.”, [79] and in specific: “To build a robust, knowledge-driven technique to AI we should have the equipment of symbol-manipulation in our toolkit. Excessive of helpful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only equipment that we understand of that can manipulate such abstract understanding dependably is the device of symbol manipulation. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a need to address the 2 sort of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having two parts, System 1 and System 2. System 1 is quick, automatic, instinctive and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind used for pattern acknowledgment while System 2 is far much better matched for preparation, reduction, and deliberative thinking. In this view, deep knowing best designs the first kind of believing while symbolic reasoning best designs the 2nd kind and both are needed.
Garcez and Lamb describe research study in this location as being ongoing for a minimum of the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has actually 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 fairly small research study community over the last 20 years and has yielded numerous significant results. Over the last decade, neural symbolic systems have been shown efficient in overcoming the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were shown efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and pieces of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of issues in the locations of bioinformatics, control engineering, software application verification and adaptation, visual intelligence, ontology knowing, and video game. [78]
Approaches for combination are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, along with some examples, follows:
– Symbolic Neural symbolic-is the existing approach of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language models. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural strategies. In this case the symbolic technique is Monte Carlo tree search and the neural strategies learn how to examine 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 thinking to generate or label training data that is subsequently discovered by a deep learning design, e.g., to train a neural design for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to develop or label examples.
– Neural _ Symbolic -uses a neural net that is created from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree created from understanding base guidelines and terms. Logic Tensor Networks [86] likewise fall into this category.
– Neural [Symbolic] -allows a neural model to directly call a symbolic thinking engine, e.g., to perform an action or assess a state.
Many key research study concerns stay, such as:
– What is the best method to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should common-sense understanding be discovered and reasoned about?
– How can abstract knowledge that is difficult to encode logically be handled?
Techniques and contributions
This area offers an overview of methods and contributions in a general context resulting in lots of other, more comprehensive short articles in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history area.
AI programs languages
The key AI programs language in the US during the last symbolic AI boom duration was LISP. LISP is the second earliest programs language after FORTRAN and was developed in 1958 by John McCarthy. LISP offered the very first read-eval-print loop to support quick program advancement. Compiled functions might be freely blended with interpreted functions. Program tracing, stepping, and breakpoints were also provided, along with the capability to alter worths or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, implying that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code.
Other key innovations pioneered by LISP that have infected other shows languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves information structures that other programs might operate on, enabling the simple meaning of higher-level languages.
In contrast to the US, in Europe the essential AI shows language during that exact same period was Prolog. Prolog supplied an integrated shop of truths and stipulations that might be queried by a read-eval-print loop. The store might serve as an understanding base and the provisions could serve as guidelines or a limited type of reasoning. As a subset of first-order reasoning Prolog was based on Horn stipulations with a closed-world assumption-any realities not known were thought about false-and a special name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe precisely one object. Backtracking and unification are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the innovators of Prolog. Prolog is a form of reasoning shows, which was developed by Robert Kowalski. Its history was also affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of techniques. For more detail see the section on the origins of Prolog in the PLANNER article.
Prolog is also a sort of declarative shows. The reasoning stipulations that describe programs are directly translated to run the programs defined. No explicit series of actions is required, as holds true with important shows languages.
Japan championed Prolog for its Fifth Generation Project, meaning to build special hardware for high efficiency. Similarly, LISP machines were built to run LISP, however as the second AI boom turned to bust these companies might not take on brand-new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history section for more detail.
Smalltalk was another influential AI programming language. For example, it presented metaclasses and, together with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present basic Lisp dialect. CLOS is a Lisp-based object-oriented system that permits numerous inheritance, in addition to incremental extensions to both classes and metaclasses, hence providing a run-time meta-object procedure. [88]
For other AI programming languages see this list of programs languages for expert system. Currently, Python, a multi-paradigm programs language, is the most popular programming language, partly due to its extensive bundle library that supports information 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 consists of metaclasses.
Search
Search emerges in numerous kinds of problem solving, including preparation, constraint fulfillment, 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 games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and reasoning
Multiple different approaches to represent knowledge and then factor with those representations have been investigated. Below is a quick summary of techniques to understanding representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual charts, frames, and reasoning are all approaches to modeling understanding such as domain understanding, problem-solving knowledge, and the semantic significance of language. Ontologies design essential ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can likewise be deemed an ontology. YAGO integrates WordNet as part of its ontology, to align realities extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.
Description reasoning is a reasoning for automated category of ontologies and for finding irregular category information. OWL is a language used to represent ontologies with description reasoning. Protégé is an ontology editor that can check out in OWL ontologies and then examine consistency with deductive classifiers such as such as HermiT. [89]
First-order reasoning is more general than description reasoning. The automated theorem provers discussed below can prove theorems in first-order logic. Horn stipulation reasoning is more restricted than first-order logic and is utilized in logic programs languages such as Prolog. Extensions to first-order logic consist of temporal logic, to handle time; epistemic reasoning, to factor about agent knowledge; modal reasoning, to deal with possibility and requirement; and probabilistic logics to manage reasoning 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 deal with evidence 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, typically of rules, to improve reusability throughout domains by separating procedural code and domain knowledge. A different inference engine procedures guidelines and adds, deletes, or customizes an understanding shop.
Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more limited rational representation is utilized, Horn Clauses. Pattern-matching, particularly unification, is used in Prolog.
A more flexible kind of analytical takes place when reasoning about what to do next takes place, rather than simply selecting among the offered actions. This sort of meta-level thinking is used in Soar and in the BB1 blackboard architecture.
Cognitive architectures such as ACT-R might have extra capabilities, such as the capability to assemble regularly utilized knowledge into higher-level portions.
Commonsense reasoning
Marvin Minsky first proposed frames as a way of interpreting common visual scenarios, such as a workplace, and Roger Schank extended this idea to scripts for typical routines, such as dining out. Cyc has actually attempted to capture beneficial sensible understanding and has “micro-theories” to handle specific sort of domain-specific thinking.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about naive physics, such as what happens when we heat up a liquid in a pot on the stove. We anticipate it to heat and possibly boil over, although we might not know its temperature level, its boiling point, or other details, such as climatic pressure.
Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be solved with constraint solvers.
Constraints and constraint-based thinking
Constraint solvers perform a more restricted kind of inference than first-order reasoning. They can streamline sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, in addition to resolving other type of puzzle issues, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint logic programs can be used to solve scheduling problems, for example with constraint dealing with guidelines (CHR).
Automated planning
The General Problem Solver (GPS) cast planning as analytical utilized means-ends analysis to develop strategies. STRIPS took a different technique, viewing preparation as theorem proving. Graphplan takes a least-commitment technique to preparation, instead of sequentially choosing actions from an initial state, working forwards, or a goal state if working in reverse. Satplan is an approach to preparing where a preparation issue is minimized to a Boolean satisfiability problem.
Natural language processing
Natural language processing focuses on treating language as information to carry out tasks such as recognizing 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 addressing concerns.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long managed by symbolic AI, but given that enhanced by deep knowing techniques. In symbolic AI, discourse representation theory and first-order logic have been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also offered vector representations of files. In the latter case, vector components are interpretable as concepts called by Wikipedia posts.
New deep learning methods based on Transformer models have now eclipsed these earlier symbolic AI techniques and achieved cutting edge performance in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the meaning of the vector parts is opaque.
Agents and multi-agent systems
Agents are autonomous systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s standard textbook on expert system is arranged to reflect representative architectures of increasing sophistication. [91] The sophistication of agents varies from easy reactive representatives, to those with a model of the world and automated planning abilities, possibly a BDI representative, i.e., one with beliefs, desires, and objectives – or alternatively a reinforcement finding out model discovered over time to select actions – approximately a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep knowing for perception. [92]
In contrast, a multi-agent system consists of numerous agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The representatives require not all have the same internal architecture. Advantages of multi-agent systems include the capability to divide work amongst the agents and to increase fault tolerance when agents are lost. Research issues include how representatives reach consensus, distributed issue fixing, multi-agent knowing, multi-agent planning, and dispersed restriction optimization.
Controversies occurred from early on in symbolic AI, both within the field-e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who accepted AI however declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were mostly from thinkers, on intellectual grounds, but likewise from financing firms, specifically throughout the two AI winters.
The Frame Problem: knowledge representation obstacles for first-order logic
Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to identifying the prerequisites for an action to succeed and in providing axioms for what did not alter 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 Expert System.” [93] A basic example happens in “proving that a person person might 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 reduction to be successful. Similar axioms would be required for other domain actions to specify what did not change.
A comparable issue, called the Qualification Problem, takes place in attempting to enumerate the preconditions for an action to succeed. A boundless variety of pathological conditions can be pictured, e.g., a banana in a tailpipe could prevent a car from running correctly.
McCarthy’s approach to repair the frame problem was circumscription, a sort of non-monotonic logic where reductions might be made from actions that require just specify what would change while not having to clearly define everything that would not alter. Other non-monotonic logics offered reality upkeep systems that revised beliefs leading to contradictions.
Other ways of handling more open-ended domains included probabilistic thinking systems and artificial intelligence to find out brand-new principles and rules. McCarthy’s Advice Taker can be deemed an inspiration here, as it might include new knowledge supplied by a human in the type of assertions or rules. For example, speculative symbolic device discovering systems explored the ability to take high-level natural language suggestions and to analyze it into domain-specific actionable guidelines.
Similar to the issues in dealing with vibrant domains, sensible reasoning is likewise tough to catch in official thinking. Examples of sensible thinking consist of implicit reasoning about how individuals believe or general knowledge of day-to-day events, objects, and living creatures. This sort of understanding is considered approved and not viewed as noteworthy. Common-sense reasoning is an open area of research study and challenging both for symbolic systems (e.g., Cyc has attempted to catch crucial parts of this knowledge over more than a years) 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 bike).
McCarthy viewed his Advice Taker as having sensible, however his definition of sensible was different than the one above. [94] He defined a program as having sound judgment “if it instantly deduces for itself an adequately wide class of immediate repercussions of anything it is informed and what it currently knows. “
Connectionist AI: philosophical difficulties and sociological disputes
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 work in deep knowing.
Three philosophical positions [96] have been described among connectionists:
1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected absolutely, and connectionist architectures underlie intelligence and are completely enough 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, explained the moderate connectionism view as essentially suitable with existing research study in neuro-symbolic hybrids:
The 3rd and last position I wish to analyze here is what I call the moderate connectionist view, a more eclectic view of the current argument between connectionism and symbolic AI. Among the researchers 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 safeguarded hybrid (partially symbolic, partially connectionist) systems. He claimed that (a minimum of) two type 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 reasoning, and generative sign control procedures) the symbolic paradigm offers adequate designs, and not only “approximations” (contrary to what radical connectionists would claim). [97]
Gary Marcus has declared that the animus in the deep knowing community against symbolic approaches now may be more sociological than philosophical:
To believe that we can simply abandon symbol-manipulation is to suspend shock.
And yet, for the many part, that’s how most current AI proceeds. Hinton and lots of others have actually tried difficult to eradicate symbols altogether. The deep knowing hope-seemingly grounded not a lot in science, but in a sort of historical grudge-is that smart habits will emerge purely from the confluence of huge information and deep learning. Where classical computer systems and software application fix jobs by defining sets of symbol-manipulating rules committed to particular tasks, such as modifying a line in a word processor or carrying out a calculation in a spreadsheet, neural networks normally try to solve jobs by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his associates have actually been emphatically “anti-symbolic”:
When deep knowing reemerged in 2012, it was with a kind of take-no-prisoners attitude that has actually characterized most of the last decade. By 2015, his hostility toward all things symbols had actually fully crystallized. He provided a talk at an AI workshop at Stanford comparing signs to aether, among science’s biggest errors.
…
Ever since, his anti-symbolic campaign has only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s essential journals, Nature. It closed with a direct attack on sign manipulation, calling not for reconciliation but for straight-out replacement. Later, Hinton told a gathering of European Union leaders that investing any additional money in symbol-manipulating techniques was “a huge mistake,” likening it to purchasing internal combustion engines in the era of electric cars. [98]
Part of these disagreements might be because of uncertain terminology:
Turing award winner Judea Pearl uses a critique of artificial intelligence which, unfortunately, conflates the terms machine learning and deep learning. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of expert systems dispossessed of any capability to discover. Making use of the terms requires clarification. Artificial intelligence is not restricted to association rule mining, c.f. the body of work on symbolic ML and relational learning (the distinctions to deep learning being the option of representation, localist sensible instead of distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not just about production rules composed by hand. An appropriate definition of AI issues understanding representation and thinking, self-governing multi-agent systems, preparation and argumentation, as well as knowing. [99]
Situated robotics: the world as a model
Another review of symbolic AI is the embodied cognition technique:
The embodied cognition method claims that it makes no sense to consider the brain individually: cognition happens within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s functioning exploits consistencies in its environment, consisting of the rest of its body. Under the embodied cognition method, robotics, vision, and other sensing units become main, not peripheral. [100]
Rodney Brooks developed behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this approach, is considered as an alternative to both symbolic AI and connectionist AI. His technique turned down representations, either symbolic or dispersed, as not only unnecessary, however as destructive. Instead, he developed the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a various purpose and must function in the genuine world. For instance, the very first robotic he explains in Intelligence Without Representation, has three layers. The bottom layer analyzes sonar sensors to prevent things. The middle layer triggers the robot to wander around when there are no barriers. The top layer causes the robotic to go to more distant places for additional expedition. Each layer can temporarily inhibit or suppress a lower-level layer. He criticized AI scientists for specifying AI issues for their systems, when: “There is no clean division between understanding (abstraction) and reasoning in the real world.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of basic finite state makers.” [102] In the Nouvelle AI method, “First, it is critically important to test the Creatures we integrate in the real life; i.e., in the very same world that we human beings populate. It is devastating to fall into the temptation of evaluating them in a simplified world initially, even with the very best intentions of later transferring activity to an unsimplified world.” [103] His focus on real-world screening was in contrast to “Early work in AI focused on video games, geometrical problems, symbolic algebra, theorem proving, and other formal systems” [104] and using the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, but has actually been slammed by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification issue, and bad in dealing with the perceptual problems where deep discovering excels. In turn, connectionist AI has been criticized as inadequately suited for deliberative detailed problem solving, including knowledge, and dealing with preparation. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been slammed for troubles in including learning and understanding.
Hybrid AIs incorporating several of these techniques are currently deemed the path forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have total answers and stated that Al is therefore difficult; we now see a number of these same areas undergoing ongoing research study and advancement leading to increased ability, not impossibility. [100]
Artificial intelligence.
Automated planning and scheduling
Automated theorem proving
Belief revision
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep knowing
First-order reasoning
GOFAI
History of synthetic intelligence
Inductive logic shows
Knowledge-based systems
Knowledge representation and reasoning
Logic programs
Machine knowing
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 once stated: “This is AI, so we don’t care if it’s emotionally genuine”. [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he said “Expert system is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 major branches of expert system: one focused on producing smart behavior no matter how it was achieved, and the other targeted at modeling intelligent processes discovered in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not specify the objective of their field as making ‘makers that fly so exactly like pigeons that they can trick even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep learning with symbolic synthetic intelligence: 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 Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep knowing with symbolic artificial intelligence: 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.
^ 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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^ Garcez et al. 2002.
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