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  • Founded Date December 20, 1969
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Symbolic Artificial Intelligence

In artificial intelligence, symbolic expert system (likewise called classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all methods in expert system research that are based upon high-level symbolic (human-readable) representations of problems, reasoning and search. [3] Symbolic AI used tools such as reasoning programming, production rules, semantic nets and frames, and it established applications such as knowledge-based systems (in specific, professional systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm caused seminal concepts in search, symbolic shows languages, agents, multi-agent systems, the semantic web, and the strengths and restrictions of official understanding and reasoning 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 persuaded that symbolic methods would ultimately prosper in developing a device with artificial general intelligence and considered this the supreme objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, caused unrealistic expectations and pledges and was followed by the first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) occurred with the increase of professional systems, their promise of capturing corporate knowledge, and an enthusiastic business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later frustration. [8] Problems with difficulties in knowledge acquisition, keeping large knowledge bases, and brittleness in managing out-of-domain problems emerged. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on resolving hidden problems in handling unpredictability and in understanding acquisition. [10] Uncertainty was attended to with official techniques such as hidden Markov models, Bayesian reasoning, and statistical relational learning. [11] [12] Symbolic machine learning addressed the understanding acquisition problem with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive reasoning shows to discover relations. [13]

Neural networks, a subsymbolic method, had been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not viewed as successful till about 2012: “Until Big Data became commonplace, the basic consensus in the Al community was that the so-called neural-network technique was hopeless. Systems just didn’t work that well, compared to other approaches. … A revolution was available in 2012, when a number of individuals, including a group of researchers dealing with Hinton, worked out a method to utilize the power of GPUs to immensely increase the power of neural networks.” [16] Over the next a number of years, deep learning had amazing success in handling vision, speech recognition, speech synthesis, image generation, and device translation. However, since 2020, as intrinsic troubles with predisposition, explanation, coherence, and toughness ended up being more obvious with deep knowing approaches; an increasing number of AI researchers have actually required integrating the best of both the symbolic and neural network methods [17] [18] and dealing with locations that both techniques have problem with, such as common-sense thinking. [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 article on the History of AI, with dates and titles varying somewhat for increased clarity.

The very first AI summer season: irrational liveliness, 1948-1966

Success at early attempts in AI happened in 3 main areas: synthetic neural networks, knowledge representation, and heuristic search, contributing to high expectations. This section sums up Kautz’s reprise of early AI history.

Approaches inspired by human or animal cognition or behavior

Cybernetic approaches tried to replicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and guiding, and seven vacuum tubes for control, based upon a preprogrammed neural net, was constructed as early as 1948. This work can be viewed as an early precursor to later work in neural networks, support learning, and situated robotics. [20]

A crucial 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 create a domain-independent problem solver, GPS (General Problem Solver). GPS solved issues represented with official operators via state-space search utilizing means-ends analysis. [21]

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

Herbert Simon and Allen Newell studied human analytical abilities and tried to formalize them, and their work laid the structures of the field of synthetic intelligence, along with cognitive science, operations research study and management science. Their research team utilized the results of mental experiments to develop programs that simulated the techniques that people utilized to resolve issues. [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 extremely specialized domain-specific type of understanding that we will see later on utilized in professional systems, early symbolic AI researchers discovered another more general application of knowledge. These were called heuristics, guidelines that assist a search in appealing instructions: “How can non-enumerative search be useful when the underlying issue is greatly hard? The method promoted by Simon and Newell is to employ heuristics: fast algorithms that might stop working on some inputs or output suboptimal options.” [26] Another essential advance was to discover a method to apply these heuristics that guarantees a solution will be discovered, if there is one, not withstanding the occasional fallibility of heuristics: “The A * algorithm provided a basic frame for total and ideal heuristically directed search. A * is used as a subroutine within almost every AI algorithm today but is still no magic bullet; its assurance of completeness is purchased the expense of worst-case rapid time. [26]

Early deal with understanding representation and reasoning

Early work covered both applications of formal reasoning highlighting first-order logic, together with efforts to handle sensible reasoning in a less formal manner.

Modeling official thinking with reasoning: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not require to mimic the precise systems of human idea, but might instead attempt to discover the essence of abstract thinking and analytical with reasoning, [27] no matter whether people utilized the exact same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on utilizing official reasoning to solve a wide array of issues, consisting of understanding representation, preparation and learning. [31] Logic was also the focus of the work at the University of Edinburgh and somewhere else in Europe which caused the development of the programming language Prolog and the science of logic shows. [32] [33]

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

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that fixing challenging problems in vision and natural language processing needed advertisement hoc solutions-they argued that no basic and general concept (like logic) would capture all the aspects of smart habits. Roger Schank explained their “anti-logic” techniques as “shabby” (as opposed to the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “shabby” AI, because they need to be developed by hand, one complex idea 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 summertime, lots of individuals believed that machine intelligence might be achieved in just a couple of years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research to utilize AI to resolve problems of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to create self-governing tanks for the battlefield. Researchers had actually begun to recognize that attaining AI was going to be much harder than was supposed a years previously, but a mix of hubris and disingenuousness led numerous university and think-tank researchers to accept financing with promises of deliverables that they need to have understood they might not satisfy. By the mid-1960s neither helpful natural language translation systems nor self-governing tanks had been developed, and a dramatic backlash set in. New DARPA management canceled existing AI financing programs.

Outside of the United States, the most fertile ground for AI research was the UK. The AI winter season in the UK was stimulated on not a lot by disappointed military leaders as by rival academics who saw AI scientists as charlatans and a drain on research study financing. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine 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 managed by researchers from other disciplines-such as applied mathematics. The report also declared that AI successes on toy problems might never ever scale to real-world applications due to combinatorial explosion. [41]

The second AI summertime: knowledge is power, 1978-1987

Knowledge-based systems

As limitations with weak, domain-independent methods became a growing number of obvious, [42] scientists from all three traditions started to build knowledge into AI applications. [43] [7] The understanding revolution was driven by the awareness that knowledge underlies high-performance, domain-specific AI applications.

Edward Feigenbaum stated:

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

( 1) The Knowledge Principle: if a program is to perform a complicated task well, it needs to understand a lot about the world in which it operates.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are 2 additional capabilities necessary for intelligent behavior in unexpected scenarios: drawing on significantly basic understanding, and analogizing to specific however distant knowledge. [45]

Success with expert systems

This “understanding transformation” led to the advancement and deployment of expert systems (introduced by Edward Feigenbaum), the first commercially effective form of AI software. [46] [47] [48]

Key professional systems were:

DENDRAL, which discovered the structure of natural molecules from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested more lab tests, when needed – by interpreting laboratory results, patient history, and physician observations. “With about 450 guidelines, MYCIN was able to carry out along with some professionals, and significantly better than junior physicians.” [49] INTERNIST and CADUCEUS which took on internal medicine diagnosis. Internist attempted to capture the expertise of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS could ultimately detect up to 1000 various diseases.
– GUIDON, which showed how an understanding base built for professional issue resolving might be repurposed for teaching. [50] XCON, to configure VAX computer systems, a then laborious procedure that might take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that depend on knowledge-intensive problem-solving. It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

Among individuals at Stanford thinking about 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 simply the one for you.” His laboratory was doing mass spectrometry of amino acids. The question was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we started the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was proficient at producing the chemical problem area.

We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists. Carl and his postdocs were world-class specialists in mass spectrometry. We began to add 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 extremely great results.

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

The other specialist systems mentioned above followed DENDRAL. MYCIN exemplifies the classic specialist system architecture of a knowledge-base of guidelines combined to a symbolic thinking mechanism, including the usage of certainty aspects to manage unpredictability. GUIDON demonstrates how an explicit knowledge base can be repurposed for a second application, tutoring, and is an example of a smart tutoring system, a particular kind of knowledge-based application. Clancey showed that it was not adequate just to use MYCIN’s rules for direction, but that he likewise needed to include guidelines for discussion management and student modeling. [50] XCON is substantial due to the fact that of the millions of dollars it conserved DEC, which activated the expert system boom where most all significant corporations in the US had expert systems groups, to record corporate competence, protect it, and automate it:

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

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

Architecture of knowledge-based and professional systems

A key component of the system architecture for all expert systems is the knowledge base, which stores realities and rules for analytical. [53] The most basic technique for a skilled system understanding base is simply a collection or network of production guidelines. Production guidelines connect signs in a relationship similar to an If-Then statement. The professional system processes the rules to make deductions and to determine what extra info it needs, i.e. what questions to ask, utilizing human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools operate in this fashion.

Expert systems can operate in either a forward chaining – from evidence to conclusions – or backwards chaining – from goals to needed data and requirements – way. Advanced knowledge-based systems, such as Soar can also carry out meta-level thinking, that is thinking about their own reasoning in regards to deciding how to resolve problems and keeping an eye on the success of problem-solving techniques.

Blackboard systems are a second kind of knowledge-based or skilled system architecture. They design a neighborhood of professionals incrementally contributing, where they can, to solve a problem. The issue is represented in multiple levels of abstraction or alternate views. The specialists (knowledge sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is upgraded as the problem scenario changes. A controller decides how helpful each contribution is, and who need to make the next analytical action. One example, the BB1 blackboard architecture [54] was initially motivated by studies of how humans plan to perform multiple jobs in a journey. [55] An innovation of BB1 was to apply the same chalkboard design to solving its control problem, i.e., its controller carried out meta-level reasoning with knowledge sources that kept an eye on how well a strategy or the analytical was continuing and could change from one strategy to another as conditions – such as goals or times – altered. BB1 has been applied in several domains: building and construction site preparation, smart tutoring systems, and real-time patient tracking.

The second AI winter season, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, a number of expert system companies, such as Teknowledge and Inference Corporation, were selling professional system shells, training, and speaking with to corporations.

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

Many reasons can be used for the arrival of the second AI winter. The hardware business stopped working when a lot more cost-effective basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the market. Many commercial deployments of expert systems were stopped when they proved too pricey to maintain. Medical specialist systems never ever caught on for a number of reasons: the difficulty in keeping them approximately date; the obstacle for medical professionals to learn how to use an overwelming range of different expert systems for different medical conditions; and maybe most crucially, the unwillingness of doctors to rely on a computer-made diagnosis over their gut impulse, even for particular domains where the expert systems could surpass an average medical professional. Venture capital money deserted AI virtually overnight. The world AI conference IJCAI hosted a massive and extravagant trade convention and thousands of nonacademic guests in 1987 in Vancouver; the main AI conference the following year, AAAI 1988 in St. Paul, was a little and strictly academic affair. [9]

Adding in more strenuous foundations, 1993-2011

Uncertain thinking

Both analytical approaches and extensions to logic were tried.

One analytical technique, hidden Markov models, had already been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl promoted the usage of Bayesian Networks as a noise but efficient way of managing unpredictable thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were used successfully in specialist systems. [57] Even later, in the 1990s, statistical relational learning, a method that integrates possibility with logical formulas, permitted possibility to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order reasoning to support were also attempted. For instance, non-monotonic thinking might be used with truth upkeep systems. A truth maintenance system tracked presumptions and reasons for all inferences. It allowed inferences to be withdrawn when presumptions were discovered out to be incorrect or a contradiction was derived. Explanations could be attended to an inference by describing which rules were used to produce it and then continuing through underlying inferences and rules all the method back to root assumptions. [58] Lofti Zadeh had introduced a various type of extension to manage the representation of vagueness. For instance, in choosing how “heavy” or “tall” a guy is, there is often no clear “yes” or “no” answer, and a predicate for heavy or tall would instead return worths between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy logic even more offered a means for propagating mixes of these worths through rational formulas. [59]

Machine knowing

Symbolic device discovering approaches were investigated to resolve the understanding acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test technique to produce plausible rule hypotheses to evaluate versus spectra. Domain and job understanding minimized the number of candidates tested to a workable size. Feigenbaum described Meta-DENDRAL as

… the culmination of my imagine the early to mid-1960s pertaining to theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of understanding to guide and prune the search. That knowledge acted due to the fact that we interviewed individuals. But how did individuals get the knowledge? By taking a look at countless spectra. So we desired a program that would look at thousands of spectra and presume the knowledge of mass spectrometry that DENDRAL could utilize to fix specific hypothesis formation issues. We did it. We were even able to release brand-new knowledge of mass spectrometry in the Journal of the American Chemical Society, offering credit only in a footnote that a program, Meta-DENDRAL, in fact did it. We had the ability to do something that had 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 approach of Meta-DENDRAL, Ross Quinlan invented a domain-independent approach to statistical classification, choice 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 rules.

Advances were made in understanding artificial intelligence theory, too. Tom Mitchell introduced variation space knowing which describes knowing as a search through an area of hypotheses, with upper, more basic, and lower, more particular, borders incorporating all feasible hypotheses consistent with the examples seen up until now. [62] More officially, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]

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

Inductive reasoning programs was another technique to discovering that allowed logic programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might manufacture Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to produce hereditary programs, which he utilized to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger offered a more basic approach to program synthesis that manufactures a functional program in the course of proving its specs to be proper. [66]

As an alternative to logic, Roger Schank presented case-based reasoning (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses initially on keeping in mind essential problem-solving cases for future usage and generalizing them where suitable. When confronted with a brand-new issue, CBR obtains the most comparable previous case and adjusts it to the specifics of the existing problem. [68] Another alternative to logic, hereditary algorithms and hereditary shows are based upon an evolutionary model of learning, where sets of rules are encoded into populations, the guidelines govern the behavior of people, and selection of the fittest prunes out sets of unsuitable guidelines over lots of generations. [69]

Symbolic machine knowing was used to learning ideas, rules, heuristics, and analytical. Approaches, other than those above, consist of:

1. Learning from instruction or advice-i.e., taking human direction, impersonated suggestions, and identifying how to operationalize it in particular situations. For example, in a video game of Hearts, finding out exactly 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 fails, querying the professional to either learn a new prototype for analytical or to discover a new description as to exactly why one prototype is more relevant than another. For instance, the program Protos discovered to detect tinnitus cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing issue solutions based on similar problems seen in the past, and then customizing their services to fit a brand-new scenario or domain. [72] [73] 4. Apprentice learning systems-learning novel services to issues by observing human problem-solving. Domain understanding discusses why novel solutions are correct and how the option can be generalized. LEAP found out how to create VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating jobs to carry out experiments and after that discovering from the results. Doug Lenat’s Eurisko, for example, found out heuristics to beat human gamers at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., looking for helpful macro-operators to be learned from sequences of fundamental analytical actions. Good macro-operators simplify problem-solving by allowing issues to be resolved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the increase of deep learning, the symbolic AI method has actually been compared to deep knowing as complementary “… with parallels having actually been drawn often times by AI researchers in 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 concept be modelled by deep learning and symbolic thinking, respectively.” In this view, symbolic thinking is more apt for deliberative thinking, preparation, and explanation while deep learning is more apt for quick pattern acknowledgment in perceptual applications with noisy data. [17] [18]

Neuro-symbolic AI: integrating neural and symbolic methods

Neuro-symbolic AI attempts to incorporate 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, learning, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the efficient construction of abundant computational cognitive designs requires the mix of sound symbolic reasoning and effective (maker) knowing models. Gary Marcus, likewise, argues that: “We can not build rich cognitive models in an appropriate, automated way without the triumvirate of hybrid architecture, rich anticipation, and sophisticated methods for thinking.”, [79] and in particular: “To build a robust, knowledge-driven method to AI we need to have the equipment of symbol-manipulation in our toolkit. Excessive of useful knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only equipment that we understand of that can manipulate such abstract knowledge dependably 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 need to address the two kinds of believing talked about 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 fast, automatic, user-friendly and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far much better fit for planning, reduction, and deliberative thinking. In this view, deep knowing best models the first kind of thinking while symbolic reasoning finest models the second kind and both are needed.

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

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

The combination of the symbolic and connectionist paradigms of AI has been pursued by a relatively little research study community over the last twenty years and has yielded a number of substantial outcomes. Over the last decade, neural symbolic systems have actually been shown capable of getting rid of 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 logics (d’Avila Garcez and Lamb, 2006) and pieces of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of issues in the areas of bioinformatics, control engineering, software confirmation and adaptation, visual intelligence, ontology knowing, and video game. [78]

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

– Symbolic Neural symbolic-is the present method of lots of neural models 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 strategies are utilized to call neural techniques. In this case the symbolic technique is Monte Carlo tree search and the neural methods discover how to evaluate game positions.
– Neural|Symbolic-uses a neural architecture to translate perceptual data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to generate or label training information that is subsequently learned by a deep knowing model, e.g., to train a neural design for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to create or label examples.
– Neural _ Symbolic -utilizes a neural internet that is created from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree produced from knowledge base rules and terms. Logic Tensor Networks [86] also fall into this classification.
– Neural [Symbolic] -allows a neural design to straight call a symbolic thinking engine, e.g., to carry out an action or evaluate a state.

Many essential research questions stay, such as:

– What is the best method to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and drawn out from them?
– How should sensible understanding be learned and reasoned about?
– How can abstract understanding that is tough to encode logically be handled?

Techniques and contributions

This section offers an introduction of strategies and contributions in a general context leading to many other, more comprehensive articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history area.

AI programs languages

The key AI programs language in the US throughout the last symbolic AI boom duration was LISP. LISP is the 2nd oldest programming language after FORTRAN and was produced in 1958 by John McCarthy. LISP supplied the very first read-eval-print loop to support fast program development. Compiled functions might be easily combined with interpreted functions. Program tracing, stepping, and breakpoints were also provided, together with the ability to change worths or functions and continue from breakpoints or errors. It had the first self-hosting compiler, implying that the compiler itself was initially written in LISP and after that ran interpretively to assemble the compiler code.

Other crucial innovations originated by LISP that have actually spread to other programming languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the key AI shows language throughout that same period was Prolog. Prolog supplied an integrated shop of truths and provisions that might be queried by a read-eval-print loop. The shop might act as an understanding base and the clauses might serve as guidelines or a restricted kind of reasoning. As a subset of first-order logic Prolog was based upon Horn stipulations with a closed-world assumption-any realities not understood were considered false-and a special name assumption for primitive terms-e.g., the identifier barack_obama was considered to describe precisely one item. Backtracking and unification are integrated to Prolog.

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

Prolog is likewise a kind of declarative programs. The reasoning clauses that explain programs are straight interpreted to run the programs specified. No explicit series of actions is required, as holds true with essential programs languages.

Japan promoted Prolog for its Fifth Generation Project, planning to develop unique hardware for high performance. Similarly, LISP machines were built to run LISP, however as the second AI boom turned to bust these business might not take on new workstations that might now run LISP or Prolog natively at comparable speeds. See the history section for more detail.

Smalltalk was another prominent AI shows language. For instance, it introduced metaclasses and, in addition to Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the current standard 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 offering a run-time meta-object procedure. [88]

For other AI programs languages see this list of programming languages for expert system. Currently, Python, a multi-paradigm programs language, is the most popular programs language, partially due to its extensive bundle library that supports data science, natural language processing, and deep learning. Python consists of a read-eval-print loop, practical aspects such as higher-order functions, and object-oriented programs that includes metaclasses.

Search

Search develops in many type of issue resolving, including planning, constraint complete satisfaction, and playing video games such as checkers, chess, and go. The finest understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven provision 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 various methods to represent understanding and after that reason with those representations have actually been examined. Below is a fast summary of methods to knowledge representation and automated reasoning.

Knowledge representation

Semantic networks, conceptual graphs, frames, and logic are all methods to modeling understanding such as domain understanding, problem-solving understanding, and the semantic significance of language. Ontologies design key ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can likewise be seen as an ontology. YAGO includes WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.

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

First-order reasoning is more basic than description reasoning. The automated theorem provers discussed below can prove theorems in first-order logic. Horn stipulation reasoning is more limited than first-order logic and is utilized in logic shows languages such as Prolog. Extensions to first-order logic include temporal reasoning, to deal with time; epistemic reasoning, to factor about agent knowledge; modal reasoning, to manage possibility and requirement; and probabilistic logics 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 conjunction with the Mace4 model checker. ACL2 is a theorem prover that can handle evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, also known as Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have an explicit knowledge base, generally of rules, to improve reusability throughout domains by separating procedural code and domain understanding. A separate reasoning engine processes guidelines and adds, deletes, or customizes a knowledge store.

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

A more versatile kind of analytical occurs when reasoning about what to do next takes place, rather than simply selecting among the readily available actions. This kind of meta-level thinking is utilized in Soar and in the BB1 chalkboard architecture.

Cognitive architectures such as ACT-R may have additional capabilities, such as the capability to put together often utilized knowledge into higher-level pieces.

Commonsense reasoning

Marvin Minsky initially proposed frames as a method of translating typical visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has actually tried to record useful common-sense understanding and has “micro-theories” to manage specific type of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about ignorant physics, such as what occurs when we warm a liquid in a pot on the stove. We expect it to heat and perhaps boil over, despite the fact that we might not know its temperature, its boiling point, or other information, such as atmospheric pressure.

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

Constraints and constraint-based thinking

Constraint solvers carry out 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, in addition to fixing other type of puzzle issues, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint logic programming can be used to solve scheduling problems, for instance with constraint dealing with guidelines (CHR).

Automated preparation

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

Natural language processing

Natural language processing concentrates on treating language as information to perform jobs such as determining subjects without always comprehending the designated meaning. Natural language understanding, on the other hand, constructs a significance representation and uses that for additional processing, such as answering questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all elements of natural language processing long managed by symbolic AI, however since enhanced by deep learning 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 likewise supplied vector representations of files. In the latter case, vector parts are interpretable as principles named by Wikipedia articles.

New deep knowing methods based on Transformer models have actually now eclipsed these earlier symbolic AI techniques and achieved cutting edge performance in natural language processing. However, Transformer designs 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 opaque.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s standard book on expert system is arranged to show agent architectures of increasing elegance. [91] The sophistication of agents varies from easy reactive representatives, to those with a model of the world and automated planning capabilities, potentially a BDI agent, i.e., one with beliefs, desires, and intentions – or additionally a support learning model found out with time to select actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep knowing for perception. [92]

In contrast, a multi-agent system includes several representatives that interact amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The representatives need not all have the exact same internal architecture. Advantages of multi-agent systems consist of the capability to divide work amongst the representatives and to increase fault tolerance when representatives are lost. Research issues consist of how representatives reach agreement, distributed problem fixing, multi-agent learning, multi-agent preparation, and distributed constraint optimization.

Controversies arose from early on in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who embraced AI but rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were primarily from theorists, on intellectual premises, but likewise from funding firms, specifically during the two AI winter seasons.

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

Limitations were discovered in utilizing simple first-order reasoning to reason about vibrant domains. Problems were discovered both with regards to identifying the prerequisites for an action to succeed and in supplying axioms for what did not alter after an action was performed.

McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A simple example occurs in “proving that one person could enter discussion with another”, as an axiom asserting “if a person has a telephone he still has it after looking up a number in the telephone book” would be required for the deduction to succeed. Similar axioms would be needed for other domain actions to specify what did not change.

A comparable issue, called the Qualification Problem, happens in trying to identify the prerequisites for an action to succeed. A boundless number of pathological conditions can be thought of, e.g., a banana in a tailpipe might prevent a vehicle from running correctly.

McCarthy’s approach to fix the frame problem was circumscription, a sort of non-monotonic reasoning where deductions might be made from actions that require just define what would alter while not having to explicitly define whatever that would not change. Other non-monotonic logics provided reality maintenance systems that modified beliefs causing contradictions.

Other methods of managing more open-ended domains consisted of probabilistic thinking systems and device learning to discover brand-new principles and guidelines. McCarthy’s Advice Taker can be deemed an inspiration here, as it could integrate new understanding offered by a human in the form of assertions or rules. For example, speculative symbolic maker discovering systems checked out the capability to take top-level natural language advice and to analyze it into domain-specific actionable guidelines.

Similar to the problems in dealing with vibrant domains, common-sense reasoning is also hard to catch in formal thinking. Examples of sensible reasoning consist of implicit reasoning about how people think or general knowledge of day-to-day occasions, things, and living creatures. This kind of understanding is taken for approved and not seen as noteworthy. Common-sense thinking is an open location of research and challenging both for symbolic systems (e.g., Cyc has tried to catch crucial parts of this understanding over more than a years) and neural systems (e.g., self-driving vehicles that do not understand not to drive into cones or not to strike pedestrians strolling a bicycle).

McCarthy saw his Advice Taker as having common-sense, but his meaning of sensible was various than the one above. [94] He specified a program as having typical sense “if it for itself a sufficiently large class of immediate effects of anything it is informed and what it currently understands. “

Connectionist AI: philosophical obstacles and sociological conflicts

Connectionist techniques include earlier deal with neural networks, [95] such as perceptrons; operate in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s more advanced approaches, such as Transformers, GANs, and other operate in deep knowing.

Three philosophical positions [96] have been described among connectionists:

1. Implementationism-where connectionist architectures carry out the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined absolutely, and connectionist architectures underlie intelligence and are totally enough to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed 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 basically suitable with existing research in neuro-symbolic hybrids:

The 3rd and last position I would like to examine here is what I call the moderate connectionist view, a more eclectic view of the current dispute between connectionism and symbolic AI. Among the scientists who has actually elaborated this position most explicitly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark defended hybrid (partially symbolic, partially connectionist) systems. He declared that (a minimum of) 2 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 models. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative symbol control procedures) the symbolic paradigm provides appropriate designs, and not just “approximations” (contrary to what extreme connectionists would claim). [97]

Gary Marcus has actually declared that the animus in the deep knowing neighborhood against symbolic methods now may be more sociological than philosophical:

To think that we can just desert symbol-manipulation is to suspend shock.

And yet, for the most part, that’s how most current AI profits. Hinton and many others have striven to eradicate symbols completely. The deep learning hope-seemingly grounded not so much in science, but in a sort of historic grudge-is that intelligent behavior will emerge purely from the confluence of massive information and deep knowing. Where classical computers and software application fix tasks by specifying sets of symbol-manipulating guidelines dedicated to specific tasks, such as modifying a line in a word processor or carrying out a calculation in a spreadsheet, neural networks typically try to resolve jobs by analytical approximation and gaining from examples.

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

When deep learning reemerged in 2012, it was with a type of take-no-prisoners mindset that has actually identified most of the last decade. By 2015, his hostility towards all things symbols had fully taken shape. He lectured at an AI workshop at Stanford comparing symbols to aether, one of science’s biggest mistakes.

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 most crucial journals, Nature. It closed with a direct attack on symbol adjustment, calling not for reconciliation but for outright replacement. Later, Hinton told a gathering of European Union leaders that investing any additional cash in symbol-manipulating techniques was “a big mistake,” comparing it to investing in internal combustion engines in the period of electric vehicles. [98]

Part of these disputes may be due to uncertain terms:

Turing award winner Judea Pearl offers a review of artificial intelligence which, unfortunately, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the undertone of the term tends to be that of expert systems dispossessed of any ability to learn. The use of the terminology needs explanation. 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 knowing being the choice of representation, localist rational instead of dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production rules written by hand. A proper definition of AI concerns understanding representation and thinking, self-governing multi-agent systems, planning and argumentation, in addition to learning. [99]

Situated robotics: the world as a design

Another review of symbolic AI is the embodied cognition method:

The embodied cognition technique claims that it makes no sense to consider the brain separately: 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 regularities in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensors end up being central, not peripheral. [100]

Rodney Brooks developed behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this technique, is considered as an alternative to both symbolic AI and connectionist AI. His method declined representations, either symbolic or distributed, as not only unnecessary, but as damaging. Instead, he produced the subsumption architecture, a layered architecture for embodied representatives. Each layer attains a various function and should function in the real life. For example, the very first robotic he explains in Intelligence Without Representation, has 3 layers. The bottom layer analyzes finder sensing units to prevent things. The middle layer triggers the robotic to roam around when there are no obstacles. The leading layer triggers the robotic to go to more far-off places for more expedition. Each layer can temporarily inhibit or reduce a lower-level layer. He criticized AI scientists for specifying AI issues for their systems, when: “There is no clean division between perception (abstraction) and reasoning in the real world.” [101] He called his robots “Creatures” and each layer was “made up of a fixed-topology network of easy finite state devices.” [102] In the Nouvelle AI method, “First, it is critically important to evaluate the Creatures we construct in the real life; i.e., in the same world that we humans live in. It is disastrous to fall into the temptation of testing them in a streamlined world initially, even with the very best objectives of later transferring activity to an unsimplified world.” [103] His emphasis on real-world testing was in contrast to “Early operate in AI focused on video games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and using the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, but has been slammed by the other techniques. Symbolic AI has been criticized as disembodied, accountable to the credentials problem, and bad in managing the affective issues where deep discovering excels. In turn, connectionist AI has been criticized as poorly suited for deliberative detailed issue solving, incorporating knowledge, and dealing with planning. Finally, Nouvelle AI stands out in reactive and real-world robotics domains however has been slammed for problems in including knowing and understanding.

Hybrid AIs incorporating one or more of these methods are currently considered as the path forward. [19] [81] [82] Russell and Norvig conclude that:

Overall, Dreyfus saw areas where AI did not have complete answers and said that Al is therefore difficult; we now see a number of these very same locations undergoing continued research study and development leading to increased ability, 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 artificial intelligence
Inductive reasoning programming
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
Model monitoring
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet

Notes

^ McCarthy when said: “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 stated “Expert system is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck composes that there are “2 significant branches of expert system: one intended at producing intelligent behavior regardless of how it was achieved, and the other focused on modeling smart processes discovered in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the objective of their field as making ‘devices 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 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.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep learning with symbolic synthetic intelligence: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). “Learning representations by back-propagating errors”. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). “Backpropagation Applied to Handwritten Postal Code Recognition”. Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. “Thinking Fast and Slow in AI”. AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. “AAAI Presidential Address: The State of AI“. AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). “On the thresholds of knowledge”. Proceedings of the International Workshop on Expert System for Industrial Applications: 291-300. doi:10.1109/ AIIA.1988.13308. S2CID 11778085.
^ 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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^ Carbonell, Jaime. “Chapter 5: Learning by Analogy: Formulating and Generalizing Plans from Past Experience”. In Michalski, Carbonell & Mitchell (1983 ), pp. 137-162.
^ Carbonell, Jaime. “Chapter 14: Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition”. In Michalski, Carbonell & Mitchell (1986 ), pp. 371-392.
^ Mitchell, Tom; Mabadevan, Sridbar; Steinberg, Louis. “Chapter 10: LEAP: A Learning Apprentice for VLSI Design”. In Kodratoff & Michalski (1990 ), pp. 271-289.
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^ a b Rossi 2022.
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^ Garcez et al. 2002.
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