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Symbolic Expert System

In expert system, symbolic expert system (likewise called classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all techniques in artificial intelligence research that are based on high-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI used tools such as logic programming, production guidelines, semantic webs and frames, and it established applications such as knowledge-based systems (in particular, professional systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to influential concepts in search, symbolic programs languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal understanding and thinking systems.

Symbolic AI was the dominant paradigm of AI research study from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would ultimately succeed in producing a machine with artificial basic intelligence and considered this the ultimate 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 promises and was followed by the first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) accompanied the rise of specialist systems, their guarantee of capturing business expertise, and an enthusiastic business embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later on frustration. [8] Problems with difficulties in knowledge acquisition, preserving big knowledge bases, and brittleness in dealing with out-of-domain issues arose. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on addressing hidden problems in managing uncertainty and in knowledge acquisition. [10] Uncertainty was addressed with formal approaches such as concealed Markov models, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic device finding out resolved the knowledge acquisition problem with contributions including Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based knowing, and inductive logic shows to learn relations. [13]

Neural networks, a subsymbolic approach, 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 work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not seen as effective until about 2012: “Until Big Data ended up being commonplace, the basic consensus in the Al community was that the so-called neural-network approach was helpless. Systems just didn’t work that well, compared to other methods. … A revolution came in 2012, when a number of individuals, consisting of a team of researchers dealing with Hinton, exercised a method to use the power of GPUs to enormously increase the power of neural networks.” [16] Over the next a number of years, deep learning had spectacular success in handling vision, speech recognition, speech synthesis, image generation, and device translation. However, because 2020, as inherent troubles with bias, explanation, comprehensibility, and robustness ended up being more evident with deep knowing techniques; an increasing variety of AI researchers have required combining the very best of both the symbolic and neural network approaches [17] [18] and resolving areas that both methods have difficulty with, such as common-sense thinking. [16]

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

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

Success at early efforts in AI happened in three main locations: artificial neural networks, knowledge representation, and heuristic search, contributing to high expectations. This area summarizes Kautz’s reprise of early AI history.

Approaches motivated by human or animal cognition or habits

Cybernetic methods tried to duplicate the feedback loops between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and seven vacuum tubes for control, based upon a preprogrammed neural internet, was built as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, reinforcement knowing, and located robotics. [20]

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

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

Herbert Simon and Allen Newell studied human problem-solving skills and tried to formalize them, and their work laid the foundations of the field of artificial intelligence, along with cognitive science, operations research study and management science. Their research team utilized the outcomes of mental experiments to develop programs that simulated the methods that individuals used to resolve issues. [22] [23] This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the center 1980s. [24] [25]

Heuristic search

In addition to the extremely specialized domain-specific sort of understanding that we will see later on used in professional systems, early symbolic AI researchers found another more general application of knowledge. These were called heuristics, guidelines of thumb that guide a search in promising directions: “How can non-enumerative search be useful when the underlying problem is significantly difficult? The approach promoted by Simon and Newell is to employ heuristics: fast algorithms that might fail on some inputs or output suboptimal options.” [26] Another crucial advance was to discover a way to use these heuristics that guarantees an option will be found, if there is one, not enduring the periodic fallibility of heuristics: “The A * algorithm provided a general frame for total and ideal heuristically assisted search. A * is utilized as a subroutine within almost every AI algorithm today however is still no magic bullet; its warranty of completeness is purchased the cost of worst-case exponential time. [26]

Early work on understanding representation and reasoning

Early work covered both applications of official thinking highlighting first-order logic, in addition to efforts to manage sensible reasoning in a less formal way.

Modeling official thinking with logic: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not need to simulate the specific systems of human thought, but might rather search for the essence of abstract reasoning and analytical with logic, [27] despite whether individuals used the exact same algorithms. [a] His laboratory at Stanford (SAIL) focused on using formal reasoning to fix a large range of issues, consisting of knowledge representation, preparation and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and elsewhere in Europe which resulted in the advancement of the shows language Prolog and the science of logic programs. [32] [33]

Modeling implicit common-sense knowledge with frames and scripts: the “scruffies”

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that fixing difficult issues in vision and natural language processing needed ad hoc solutions-they argued that no simple and general principle (like reasoning) would record all the aspects of smart habits. Roger Schank described their “anti-logic” methods as “scruffy” (instead of the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, given that they need to be developed by hand, one complicated concept at a time. [38] [39] [40]

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

The very first AI winter was a shock:

During the very first AI summer, lots of people thought that device intelligence could be attained in simply a few years. The Defense Advance Research Projects Agency (DARPA) introduced programs to support AI research study to use AI to solve issues of national security; in specific, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battleground. Researchers had actually begun to understand that accomplishing AI was going to be much harder than was expected a decade earlier, but a mix of hubris and disingenuousness led numerous university and think-tank scientists to accept financing with promises of deliverables that they ought to have known they might not fulfill. By the mid-1960s neither helpful natural language translation systems nor autonomous tanks had actually been developed, and a dramatic backlash set in. New DARPA leadership canceled existing AI financing programs.

Beyond the United States, the most fertile ground for AI research was the United Kingdom. The AI winter season in the UK was stimulated on not so much by disappointed military leaders as by competing academics who saw AI researchers as charlatans and a drain on research study financing. A teacher of used mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research in the country. The report mentioned that all of the problems being dealt with in AI would be much better handled by scientists from other disciplines-such as applied mathematics. The report likewise declared that AI successes on toy problems could never ever scale to real-world applications due to combinatorial surge. [41]

The second AI summer season: understanding is power, 1978-1987

Knowledge-based systems

As constraints with weak, domain-independent approaches ended up being more and more evident, [42] researchers from all three customs started to build understanding into AI applications. [43] [7] The knowledge revolution was driven by the realization that understanding underlies high-performance, domain-specific AI applications.

Edward Feigenbaum stated:

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

( 1) The Knowledge Principle: if a program is to perform a complicated task well, it should know a good deal about the world in which it runs.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are 2 extra abilities needed for intelligent habits in unanticipated scenarios: drawing on increasingly basic knowledge, and analogizing to specific but far-flung knowledge. [45]

Success with specialist systems

This “understanding transformation” resulted in the advancement and implementation of specialist systems (presented by Edward Feigenbaum), the very first commercially successful type of AI software application. [46] [47] [48]

Key specialist systems were:

DENDRAL, which discovered the structure of organic molecules from their chemical formula and mass spectrometer readings.
MYCIN, which detected bacteremia – and suggested additional lab tests, when necessary – by translating laboratory outcomes, patient history, and doctor observations. “With about 450 rules, MYCIN had the ability to carry out along with some specialists, and considerably much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which took on internal medicine diagnosis. Internist attempted to capture the proficiency of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately diagnose approximately 1000 different illness.
– GUIDON, which showed how a knowledge base constructed for specialist problem fixing could be repurposed for teaching. [50] XCON, to set up VAX computer systems, a then laborious process that could take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is considered the very 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:

Among individuals at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. 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 question 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 great at heuristic search techniques, and he had an algorithm that was great at generating the chemical issue area.

We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, innovator of the chemical behind the contraceptive pill, and also one of the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We began to add to their knowledge, creating knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL increasingly more knowledge. The more you did that, the smarter the program ended up being. We had great outcomes.

The generalization was: in the knowledge lies the power. That was the huge idea. In my career that is the substantial, “Ah ha!,” and it wasn’t the method AI was being done formerly. Sounds basic, but it’s most likely AI’s most powerful generalization. [51]

The other professional systems pointed out above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of guidelines paired to a symbolic reasoning mechanism, consisting of the use of certainty elements to manage uncertainty. GUIDON demonstrates how a specific understanding base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a particular kind of knowledge-based application. Clancey showed that it was not sufficient merely to utilize MYCIN’s guidelines for instruction, but that he likewise required to add rules for discussion management and student modeling. [50] XCON is substantial due to the fact that of the millions of dollars it conserved DEC, which triggered the specialist system boom where most all significant corporations in the US had professional systems groups, to catch corporate proficiency, protect it, and automate it:

By 1988, DEC’s AI group had 40 professional systems released, with more en route. DuPont had 100 in use and 500 in development. Nearly every significant U.S. corporation had its own Al group and was either using or examining specialist systems. [49]

Chess specialist knowledge was encoded in Deep Blue. In 1996, this allowed IBM’s Deep Blue, with the help of symbolic AI, to win in a game of chess versus the world champ at that time, Garry Kasparov. [52]

Architecture of knowledge-based and expert systems

A key element of the system architecture for all professional systems is the understanding base, which stores facts and guidelines for problem-solving. [53] The easiest method for an expert system understanding base is simply a collection or network of production guidelines. Production guidelines connect signs in a relationship comparable to an If-Then declaration. The professional system processes the guidelines to make reductions and to determine what extra information it requires, 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 objectives to needed data and prerequisites – way. More advanced knowledge-based systems, such as Soar can also perform meta-level thinking, that is thinking about their own thinking in terms of choosing how to resolve problems and keeping an eye on the success of analytical methods.

Blackboard systems are a second sort of knowledge-based or professional system architecture. They design a community of specialists incrementally contributing, where they can, to resolve a problem. The problem is represented in several levels of abstraction or alternate views. The professionals (knowledge sources) volunteer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on an agenda that is updated as the issue scenario changes. A controller chooses how helpful each contribution is, and who ought to make the next analytical action. One example, the BB1 chalkboard architecture [54] was initially influenced by studies of how humans prepare to carry out numerous tasks in a journey. [55] An innovation of BB1 was to apply the exact same blackboard design to resolving its control problem, i.e., its controller performed meta-level reasoning with understanding sources that monitored how well a strategy or the problem-solving was continuing and could switch from one technique to another as conditions – such as goals or times – changed. BB1 has been used in numerous domains: construction site planning, intelligent tutoring systems, and real-time client tracking.

The 2nd AI winter, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP devices specifically targeted to accelerate the development of AI applications and research. In addition, a number of synthetic intelligence business, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and seeking advice from to corporations.

Unfortunately, the AI boom did not last and Kautz best explains the 2nd AI winter season that followed:

Many reasons can be offered for the arrival of the 2nd AI winter. The hardware business stopped working when far more cost-efficient basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the marketplace. Many business deployments of expert systems were terminated when they showed too costly to keep. Medical professional systems never ever captured on for several reasons: the problem in keeping them approximately date; the challenge for physician to discover how to use a bewildering range of various specialist systems for different medical conditions; and perhaps most crucially, the unwillingness of doctors to trust a computer-made medical diagnosis over their gut impulse, even for particular domains where the expert systems might outperform an average medical professional. Equity capital money deserted AI practically overnight. The world AI conference IJCAI hosted an enormous and extravagant exhibition and thousands of nonacademic participants in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a little and strictly academic affair. [9]

Adding in more rigorous foundations, 1993-2011

Uncertain thinking

Both analytical techniques and extensions to logic were attempted.

One statistical method, concealed Markov models, had already been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl promoted using Bayesian Networks as a sound however effective method of managing unpredictable reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were used effectively in specialist systems. [57] Even later, in the 1990s, statistical relational knowing, a technique that integrates probability with rational solutions, enabled likelihood to be combined with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order reasoning to assistance were likewise tried. For example, non-monotonic thinking might be used with fact maintenance systems. A reality maintenance system tracked assumptions and justifications for all inferences. It permitted inferences to be withdrawn when presumptions were discovered to be incorrect or a contradiction was obtained. Explanations might be attended to a reasoning by discussing which guidelines were applied to develop it and then continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had actually introduced a various sort of extension to deal with the representation of ambiguity. For example, in choosing how “heavy” or “high” a guy is, there is often no clear “yes” or “no” answer, and a predicate for heavy or tall would rather return values between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy reasoning further provided a means for propagating mixes of these values through logical formulas. [59]

Machine knowing

Symbolic device finding out methods were examined to address the knowledge acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test strategy to produce plausible guideline hypotheses to test against spectra. Domain and job understanding decreased the variety of candidates tested to a manageable size. Feigenbaum explained Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s pertaining to theory formation. The conception was that you had a problem solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of understanding to steer and prune the search. That understanding got in there because we spoke with individuals. But how did individuals get the knowledge? By looking at countless spectra. So we wanted a program that would take a look at countless spectra and infer the knowledge of mass spectrometry that DENDRAL could utilize to solve private hypothesis development issues. We did it. We were even able to publish brand-new knowledge of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, really did it. We had the ability to do something that had been a dream: to have a computer system program created a new and publishable piece of science. [51]

In contrast to the knowledge-intensive method of Meta-DENDRAL, Ross Quinlan developed a domain-independent technique to statistical classification, choice tree learning, beginning first with ID3 [60] and then later on extending its capabilities to C4.5. [61] The choice trees created are glass box, interpretable classifiers, with human-interpretable classification rules.

Advances were made in comprehending machine knowing theory, too. Tom Mitchell introduced version space learning which explains learning as an explore a space of hypotheses, with upper, more basic, and lower, more specific, borders encompassing all feasible hypotheses consistent with the examples seen so far. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]

Symbolic maker finding out incorporated more than finding out by example. E.g., John Anderson offered a cognitive design of human knowing where skill practice results in a collection of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a trainee might learn to use “Supplementary angles are 2 angles whose steps sum 180 degrees” as numerous various procedural rules. 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 method “knowledge compilation”. ACT-R has actually been used successfully to design elements of human cognition, such as learning and retention. ACT-R is also used in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer programs, and algebra to school kids. [64]

Inductive reasoning programs was another method to finding out that enabled logic 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 genetic algorithms to program synthesis to produce genetic programs, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger supplied a more basic approach to program synthesis that synthesizes a practical program in the course of proving its specs to be proper. [66]

As an option to logic, Roger Schank introduced case-based thinking (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses initially on keeping in mind essential analytical cases for future usage and generalizing them where appropriate. When confronted with a new issue, CBR recovers the most comparable previous case and adjusts it to the specifics of the existing problem. [68] Another option to reasoning, hereditary algorithms and hereditary programs are based on an evolutionary model of learning, where sets of guidelines are encoded into populations, the rules govern the habits of individuals, and selection of the fittest prunes out sets of inappropriate rules over lots of generations. [69]

Symbolic artificial intelligence was applied to discovering concepts, guidelines, heuristics, and problem-solving. Approaches, besides those above, include:

1. Learning from instruction or advice-i.e., taking human guideline, impersonated recommendations, and figuring out how to operationalize it in specific circumstances. For example, in a video game of Hearts, discovering exactly how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter professional (SME) feedback during training. When problem-solving fails, querying the specialist to either discover a new exemplar for analytical or to find out a new description as to exactly why one prototype is more appropriate than another. For example, the program Protos learned to diagnose tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing problem solutions based on comparable issues seen in the past, and after that customizing their options to fit a brand-new scenario or domain. [72] [73] 4. Apprentice learning systems-learning novel services to problems by observing human analytical. Domain understanding describes why unique services are right and how the solution can be generalized. LEAP found out how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to perform experiments and after that gaining from the results. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human players at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., looking for useful macro-operators to be gained from sequences of standard problem-solving actions. Good macro-operators streamline analytical by allowing problems to be resolved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now

With the increase of deep knowing, the symbolic AI technique has been compared to deep knowing as complementary “… with parallels having been drawn lot of times by AI researchers in between Kahneman’s research on human reasoning and choice making – reflected in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in concept be modelled by deep learning and symbolic reasoning, respectively.” In this view, symbolic thinking is more apt for deliberative reasoning, planning, and explanation while deep learning is more apt for quick pattern acknowledgment in perceptual applications with noisy data. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic techniques

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 reasoning, discovering, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the efficient building of abundant computational cognitive designs demands the combination of sound symbolic thinking and efficient (machine) knowing models. Gary Marcus, likewise, argues that: “We can not build rich cognitive models in an adequate, automated method without the triumvirate of hybrid architecture, rich anticipation, and sophisticated methods for thinking.”, [79] and in particular: “To develop a robust, knowledge-driven technique to AI we must have the equipment of symbol-manipulation in our toolkit. Excessive of useful 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 control such abstract knowledge dependably is the device of symbol manipulation. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based upon a requirement to address the two kinds of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is quick, automated, user-friendly and unconscious. System 2 is slower, step-by-step, and specific. System 1 is the kind utilized for pattern acknowledgment while System 2 is far better suited for preparation, reduction, and deliberative thinking. In this view, deep learning best models the very first type of believing while symbolic reasoning finest designs the 2nd kind and both are required.

Garcez and Lamb explain research study in this location as being continuous for at least the past twenty years, [83] dating from their 2002 book on neurosymbolic learning systems. [84] A series of workshops on neuro-symbolic reasoning has actually been held every year considering 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 actually been pursued by a fairly small research community over the last twenty years and has actually yielded several substantial outcomes. Over the last years, neural symbolic systems have actually been shown efficient in conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed capable of representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and fragments of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of issues in the areas of bioinformatics, control engineering, software application confirmation and adjustment, visual intelligence, ontology learning, and video game. [78]

Approaches for integration are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:

– Symbolic Neural symbolic-is the current approach of many neural models in natural language processing, where words or subword tokens are both the ultimate input and output of big language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic methods are used to call neural techniques. In this case the symbolic technique is Monte Carlo tree search and the neural methods learn how to evaluate video game positions.
– Neural|Symbolic-uses a neural architecture to translate affective information as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to produce or identify training data that is subsequently found out by a deep learning design, e.g., to train a neural design for symbolic computation by using a Macsyma-like symbolic mathematics system to develop or label examples.
– Neural _ Symbolic -uses a neural net that is produced from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree generated from understanding base guidelines and terms. Logic Tensor Networks [86] also fall into this classification.
– Neural [Symbolic] -enables a neural model to directly call a symbolic thinking engine, e.g., to carry out an action or evaluate a state.

Many crucial research concerns remain, such as:

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

Techniques and contributions

This area provides a summary of strategies and contributions in an overall context resulting in many other, more comprehensive short articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history area.

AI programming languages

The key AI shows language in the US during the last symbolic AI boom duration was LISP. LISP is the second earliest shows language after FORTRAN and was created in 1958 by John McCarthy. LISP offered the very first read-eval-print loop to support fast program development. Compiled functions might be freely combined with interpreted functions. Program tracing, stepping, and breakpoints were also supplied, in addition to the capability to alter worths or functions and continue from breakpoints or mistakes. It had the very first self-hosting compiler, indicating that the compiler itself was initially composed in LISP and then ran interpretively to compile the compiler code.

Other essential developments pioneered by LISP that have actually spread to other shows languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the key AI shows language during that same duration was Prolog. Prolog supplied an integrated shop of truths and clauses that might be queried by a read-eval-print loop. The store could act as a knowledge base and the provisions could serve as guidelines or a limited kind of reasoning. As a subset of first-order logic Prolog was based upon Horn provisions with a closed-world assumption-any truths not known were considered false-and an unique name presumption for primitive terms-e.g., the identifier barack_obama was thought about to refer to exactly one item. Backtracking and unification are built-in to Prolog.

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

Prolog is also a type of declarative programs. The reasoning provisions that explain programs are straight translated to run the programs defined. No explicit series of actions is required, as holds true with necessary programming languages.

Japan championed Prolog for its Fifth Generation Project, planning to build unique hardware for high efficiency. Similarly, LISP devices were built to run LISP, but as the 2nd AI boom turned to bust these companies might not take on new workstations that might now run LISP or Prolog natively at similar speeds. See the history section for more detail.

Smalltalk was another prominent AI programs language. For example, it introduced metaclasses and, along with Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits several inheritance, in addition to incremental extensions to both classes and metaclasses, hence supplying a run-time meta-object procedure. [88]

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

Search

Search occurs in lots of kinds of problem solving, consisting of preparation, constraint 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 knowing, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and thinking

Multiple various methods to represent understanding and then reason with those representations have been examined. Below is a fast overview of approaches to understanding representation and automated thinking.

Knowledge representation

Semantic networks, conceptual charts, frames, and reasoning are all techniques to modeling knowledge such as domain knowledge, problem-solving understanding, and the semantic meaning of language. Ontologies design key concepts and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can likewise be deemed an ontology. YAGO incorporates WordNet as part of its ontology, to align realities drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being utilized.

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

First-order logic is more general than description reasoning. The automated theorem provers talked about below can prove theorems in first-order reasoning. Horn stipulation logic is more restricted than first-order logic and is utilized in logic shows languages such as Prolog. Extensions to first-order logic include temporal reasoning, to handle time; epistemic reasoning, to factor about representative understanding; modal logic, to manage possibility and necessity; and probabilistic reasonings to manage logic and likelihood together.

Automatic theorem showing

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

Prover9.
ACL2.
Vampire.

Prover9 can be used in combination with the Mace4 model checker. ACL2 is a theorem prover that can manage evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise called Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have an explicit knowledge base, normally of guidelines, to enhance reusability throughout domains by separating procedural code and domain understanding. A different reasoning engine procedures rules and adds, deletes, or modifies an understanding shop.

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

A more versatile kind of problem-solving happens when reasoning about what to do next takes place, instead of simply choosing among the offered actions. This sort of meta-level reasoning is utilized in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R might have extra abilities, such as the capability to compile often used understanding into higher-level chunks.

Commonsense reasoning

Marvin Minsky first proposed frames as a way of interpreting typical visual situations, such as an office, and Roger Schank extended this concept to scripts for typical routines, such as eating in restaurants. Cyc has actually attempted to record helpful sensible understanding and has “micro-theories” to deal with particular kinds of domain-specific reasoning.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about ignorant physics, such as what takes place when we heat up a liquid in a pot on the range. We expect it to heat and possibly boil over, despite the fact that we may not know its temperature, its boiling point, or other information, such as air pressure.

Similarly, Allen’s temporal period 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 restriction solvers.

Constraints and constraint-based reasoning

Constraint solvers carry out a more restricted sort of inference than first-order reasoning. They can streamline sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, in addition to solving other kinds of puzzle issues, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic shows can be utilized to solve scheduling issues, for example with restriction dealing with guidelines (CHR).

Automated planning

The General Problem Solver (GPS) cast planning as analytical used means-ends analysis to create plans. STRIPS took a different technique, viewing planning 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 backwards. Satplan is a technique to planning where a planning issue is decreased to a Boolean satisfiability issue.

Natural language processing

Natural language processing focuses on dealing with language as data to carry out tasks such as determining subjects without always comprehending the intended significance. Natural language understanding, on the other hand, constructs a meaning representation and uses that for further processing, such as responding to questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long managed by symbolic AI, however considering that improved by deep knowing techniques. In symbolic AI, discourse representation theory and first-order reasoning have actually been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also provided vector representations of files. In the latter case, vector components are interpretable as concepts named by Wikipedia short articles.

New deep learning techniques based on Transformer models have now eclipsed these earlier symbolic AI techniques and attained advanced performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the significance of the vector components is nontransparent.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig’s basic book on expert system is organized to show agent architectures of increasing sophistication. [91] The sophistication of agents differs from simple reactive agents, to those with a design of the world and automated preparation capabilities, possibly a BDI representative, i.e., one with beliefs, desires, and intentions – or additionally a support discovering design found out in time to pick actions – as much as a combination of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep knowing for perception. [92]

On the other hand, a multi-agent system consists of multiple 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 very same internal architecture. Advantages of multi-agent systems consist of the ability to divide work among the agents and to increase fault tolerance when representatives are lost. Research problems consist of how representatives reach consensus, distributed problem fixing, multi-agent knowing, multi-agent preparation, and distributed restriction optimization.

Controversies emerged from early 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 welcomed AI but rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from outside of the field were mainly from thinkers, on intellectual grounds, however also from financing firms, specifically during the two AI winters.

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

Limitations were found in using basic first-order reasoning to factor about dynamic domains. Problems were found both with regards to enumerating the preconditions for an action to prosper and in offering axioms for what did not change after an action was performed.

McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A basic example occurs in “proving that a person person might enter conversation with another”, as an axiom asserting “if an individual has a telephone he still has it after searching for a number in the telephone directory” would be required for the deduction to prosper. Similar axioms would be needed for other domain actions to define what did not change.

A similar problem, called the Qualification Problem, occurs in trying to identify the prerequisites for an action to be successful. An unlimited number of pathological conditions can be pictured, e.g., a banana in a tailpipe could avoid a cars and truck from operating properly.

McCarthy’s method to fix the frame problem was circumscription, a sort of non-monotonic reasoning where deductions could be made from actions that require just define what would alter while not needing to clearly define whatever that would not alter. Other non-monotonic reasonings offered reality maintenance systems that modified beliefs leading to contradictions.

Other ways of managing more open-ended domains included probabilistic thinking systems and machine learning to find out new ideas and guidelines. McCarthy’s Advice Taker can be considered as an inspiration here, as it could include brand-new knowledge provided by a human in the type of assertions or rules. For example, experimental symbolic device learning systems checked out the ability to take top-level natural language advice and to translate it into domain-specific actionable guidelines.

Similar to the issues in managing dynamic domains, sensible reasoning is likewise hard to catch in official thinking. Examples of sensible thinking consist of implicit reasoning about how individuals think or general understanding of day-to-day events, objects, and living creatures. This sort of understanding is considered granted and not deemed noteworthy. Common-sense thinking is an open location of research and challenging both for symbolic systems (e.g., Cyc has actually attempted to catch essential parts of this understanding over more than a years) and neural systems (e.g., self-driving automobiles that do not understand not to drive into cones or not to strike pedestrians walking a bike).

McCarthy viewed his Advice Taker as having sensible, however his meaning of common-sense was different than the one above. [94] He specified a program as having good sense “if it automatically deduces for itself a sufficiently broad class of instant repercussions of anything it is told and what it already understands. “

Connectionist AI: philosophical difficulties and sociological conflicts

Connectionist approaches include 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 more sophisticated methods, such as Transformers, GANs, and other work in deep learning.

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

1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down totally, and connectionist architectures underlie intelligence and are completely sufficient to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are needed for intelligence

Olazaran, in his sociological history of the controversies within the neural network community, explained the moderate connectionism consider as basically suitable with current research in neuro-symbolic hybrids:

The third and last position I would like to analyze here is what I call the moderate connectionist view, a more diverse view of the existing debate in between connectionism and symbolic AI. Among the scientists who has actually elaborated this position most clearly is Andy Clark, a thinker from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark safeguarded hybrid (partially symbolic, partly connectionist) systems. He declared that (a minimum of) two kinds of theories are needed in order to study and design cognition. On the one hand, for some information-processing tasks (such as pattern acknowledgment) connectionism has benefits over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive reasoning, and generative symbol manipulation processes) the symbolic paradigm offers adequate models, and not only “approximations” (contrary to what radical connectionists would declare). [97]

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

To believe that we can just desert symbol-manipulation is to suspend disbelief.

And yet, for the a lot of part, that’s how most existing AI profits. Hinton and lots of others have actually attempted difficult to banish signs entirely. The deep learning hope-seemingly grounded not so much in science, but in a sort of historical grudge-is that smart habits will emerge purely from the confluence of massive data and deep learning. Where classical computer systems and software solve tasks by specifying sets of symbol-manipulating guidelines dedicated to specific jobs, such as modifying a line in a word processor or performing an estimation in a spreadsheet, neural networks generally attempt to fix tasks by statistical approximation and finding out from examples.

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

When deep knowing reemerged in 2012, it was with a sort of take-no-prisoners mindset that has actually identified the majority of the last years. By 2015, his hostility towards all things symbols had totally crystallized. He lectured at an AI workshop at Stanford comparing symbols to aether, among science’s biggest mistakes.

Since then, his anti-symbolic project has just 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 however for outright replacement. Later, Hinton told an event of European Union leaders that investing any further cash in symbol-manipulating techniques was “a substantial mistake,” likening it to buying internal combustion engines in the period of electrical automobiles. [98]

Part of these disputes might be due to unclear terms:

Turing award winner Judea Pearl uses a critique of machine learning which, regrettably, conflates the terms maker learning and deep learning. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of professional systems dispossessed of any capability to find out. The usage of the terminology requires clarification. Artificial intelligence is not restricted to association guideline mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the option of representation, localist logical instead of distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not practically production guidelines composed by hand. A correct definition of AI concerns knowledge representation and reasoning, autonomous 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 approach declares that it makes no sense to consider the brain individually: cognition occurs within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s functioning exploits regularities in its environment, including the rest of its body. Under the embodied cognition technique, robotics, vision, and other sensors become central, not peripheral. [100]

Rodney Brooks invented behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this method, is considered as an alternative to both symbolic AI and connectionist AI. His technique turned down representations, either symbolic or distributed, as not just unnecessary, however as damaging. Instead, he produced the subsumption architecture, a layered architecture for embodied agents. Each layer accomplishes a different purpose and should work in the real world. For example, the first robot he explains in Intelligence Without Representation, has 3 layers. The bottom layer analyzes sonar sensors to avoid items. The middle layer causes the robot to wander around when there are no challenges. The top layer causes the robot to go to more far-off locations for further expedition. Each layer can temporarily inhibit or suppress a lower-level layer. He slammed AI researchers for specifying AI problems for their systems, when: “There is no tidy division in between understanding (abstraction) and reasoning in the real life.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of simple limited state machines.” [102] In the Nouvelle AI method, “First, it is vitally essential to test the Creatures we in the real life; i.e., in the very same world that we human beings occupy. It is dreadful to fall into the temptation of checking them in a streamlined world first, even with the finest objectives of later transferring activity to an unsimplified world.” [103] His emphasis on real-world screening was in contrast to “Early operate in AI concentrated on video games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and the usage of the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, but has been slammed by the other approaches. Symbolic AI has been criticized as disembodied, liable to the credentials issue, and poor in dealing with the affective issues where deep finding out excels. In turn, connectionist AI has actually been slammed as poorly suited for deliberative step-by-step problem solving, integrating understanding, and managing preparation. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has actually been criticized for problems in including learning and understanding.

Hybrid AIs including several of these techniques are presently considered as the path forward. [19] [81] [82] Russell and Norvig conclude that:

Overall, Dreyfus saw areas where AI did not have complete answers and stated that Al is for that reason impossible; we now see a number of these very same areas going through continued research and advancement causing increased capability, not impossibility. [100]

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

Notes

^ McCarthy once said: “This is AI, so we do not care if it’s emotionally genuine”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he stated “Expert system is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck composes that there are “2 significant branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other targeted at modeling intelligent procedures found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the goal of their field as making ‘makers that fly so precisely like pigeons that they can deceive 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 learning with symbolic expert system: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. 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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