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Symbolic Expert System

In expert system, symbolic artificial intelligence (likewise referred to as classical expert system or logic-based artificial intelligence) [1] [2] is the term for the collection of all approaches in expert system research study that are based upon high-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI used tools such as reasoning programs, production rules, semantic webs and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in influential ideas in search, symbolic shows languages, agents, multi-agent systems, the semantic web, and the strengths and constraints 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 encouraged that symbolic techniques would eventually be successful in producing a device with artificial general intelligence and considered this the supreme goal of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and pledges and was followed by the first AI Winter as moneying dried up. [5] [6] A second boom (1969-1986) accompanied the rise of expert systems, their pledge of recording corporate expertise, and a passionate corporate welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later disappointment. [8] Problems with troubles in knowledge acquisition, maintaining big understanding bases, and brittleness in managing out-of-domain problems emerged. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on attending to hidden issues in dealing with uncertainty and in knowledge acquisition. [10] Uncertainty was addressed with formal methods such as surprise Markov models, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic machine finding out attended to the knowledge acquisition issue with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree knowing, case-based learning, and inductive reasoning programs to discover relations. [13]

Neural networks, a subsymbolic method, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not considered as successful up until about 2012: „Until Big Data became prevalent, the general agreement in the Al neighborhood was that the so-called neural-network approach was hopeless. Systems simply didn’t work that well, compared to other approaches. … A revolution was available in 2012, when a variety of people, including a group of researchers dealing with Hinton, worked out a method to use the power of GPUs to tremendously increase the power of neural networks.“ [16] Over the next numerous years, deep learning had amazing success in handling vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, considering that 2020, as intrinsic troubles with predisposition, description, coherence, and robustness ended up being more obvious with deep learning methods; an increasing variety of AI scientists have actually called for combining the very best of both the symbolic and neural network approaches [17] [18] and dealing with locations that both approaches have problem with, such as common-sense thinking. [16]

A short history of symbolic AI to the present day follows below. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia post on the History of AI, with dates and titles varying a little for increased clearness.

The first AI summertime: illogical enthusiasm, 1948-1966

Success at early efforts in AI took place in three main areas: synthetic neural networks, understanding representation, and heuristic search, contributing to high expectations. This section sums up Kautz’s reprise of early AI history.

Approaches motivated by human or animal cognition or behavior

Cybernetic methods tried to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and 7 vacuum tubes for control, based upon a preprogrammed neural internet, was developed as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, reinforcement learning, and positioned robotics. [20]

A crucial 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 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later generalized this work to develop a domain-independent problem solver, GPS (General Problem Solver). GPS fixed problems represented with official operators via state-space search utilizing means-ends analysis. [21]

During the 1960s, symbolic methods attained fantastic success at simulating smart habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Each one developed its own style of research. Earlier methods based on cybernetics or synthetic neural networks were deserted or pressed into the background.

Herbert Simon and Allen Newell studied human analytical abilities and attempted to formalize them, and their work laid the structures of the field of synthetic intelligence, in addition to cognitive science, operations research study and management science. Their research study team used the outcomes of psychological experiments to develop programs that simulated the strategies that people used to fix issues. [22] [23] This custom, centered 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 sort of knowledge that we will see later on used in specialist systems, early symbolic AI scientists discovered another more general application of understanding. These were called heuristics, guidelines that direct a search in promising instructions: „How can non-enumerative search be practical when the underlying problem is greatly tough? The method advocated by Simon and Newell is to use heuristics: quick algorithms that might stop working on some inputs or output suboptimal services.“ [26] Another essential advance was to find a way to apply these heuristics that ensures a solution will be discovered, if there is one, not withstanding the periodic fallibility of heuristics: „The A * algorithm supplied a general frame for total and optimal heuristically assisted search. A * is used as a subroutine within practically every AI algorithm today however is still no magic bullet; its warranty of completeness is bought at the cost of worst-case rapid time. [26]

Early deal with knowledge representation and reasoning

Early work covered both applications of official reasoning stressing first-order reasoning, along with attempts to handle common-sense reasoning in a less formal way.

Modeling formal reasoning with logic: the „neats“

Unlike Simon and Newell, John McCarthy felt that devices did not need to simulate the precise systems of human thought, but could instead look for the essence of abstract thinking and analytical with reasoning, [27] despite whether people utilized the exact same algorithms. [a] His laboratory at Stanford (SAIL) focused on utilizing official logic to resolve a variety of problems, including knowledge representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and elsewhere in Europe which led to 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] discovered that fixing difficult issues in vision and natural language processing required ad hoc solutions-they argued that no simple and basic principle (like reasoning) would record all the elements of intelligent habits. Roger Schank described their „anti-logic“ techniques as „shabby“ (instead of the „neat“ paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of „scruffy“ AI, considering that they need to be built by hand, one complex idea at a time. [38] [39] [40]

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

The first AI winter was a shock:

During the first AI summertime, lots of people believed that machine intelligence might be achieved in just a couple of years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to utilize AI to fix problems 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 begun to realize that attaining AI was going to be much harder than was expected a decade previously, but a mix of hubris and disingenuousness led lots of university and think-tank scientists to accept financing with guarantees of deliverables that they must have understood they might not meet. By the mid-1960s neither useful natural language translation systems nor self-governing tanks had been created, and a dramatic backlash set in. New DARPA leadership canceled existing AI funding programs.

Outside of the United States, the most fertile ground for AI research was the UK. The AI winter in the UK was spurred on not a lot by disappointed military leaders as by competing academics who viewed AI researchers as charlatans and a drain on research study funding. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research in the nation. The report stated that all of the issues being worked on 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 scale to real-world applications due to combinatorial explosion. [41]

The 2nd AI summer: knowledge is power, 1978-1987

Knowledge-based systems

As constraints with weak, domain-independent methods ended up being more and more evident, [42] scientists from all 3 traditions began to develop understanding into AI applications. [43] [7] The knowledge transformation was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.

Edward Feigenbaum stated:

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

( 1) The Knowledge Principle: if a program is to carry out a complex job well, it needs to understand a good deal about the world in which it operates.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are two extra capabilities necessary for intelligent habits in unanticipated scenarios: drawing on increasingly basic understanding, and analogizing to particular however far-flung understanding. [45]

Success with specialist systems

This „understanding revolution“ caused the advancement and release of specialist systems (presented by Edward Feigenbaum), the first commercially effective form of AI software application. [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 diagnosed bacteremia – and recommended more lab tests, when required – by translating lab results, client history, and medical professional observations. „With about 450 rules, MYCIN was able to perform as well as some specialists, and significantly better than junior doctors.“ [49] INTERNIST and CADUCEUS which tackled internal medicine medical diagnosis. Internist attempted to catch the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might eventually identify approximately 1000 different illness.
– GUIDON, which revealed how a knowledge base developed for professional problem fixing could be repurposed for teaching. [50] XCON, to set up VAX computer systems, a then laborious process that might take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is thought about the very first professional system that depend on knowledge-intensive problem-solving. It is explained listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

Among individuals at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I told him I wanted an induction „sandbox“, he said, „I have simply the one for you.“ His lab was doing mass spectrometry of amino acids. The concern was: how do you go from taking a look 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 excellent at heuristic search techniques, and he had an algorithm that was excellent at creating the chemical problem space.

We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, developer of the chemical behind the contraceptive pill, and also among the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We began to contribute to their understanding, creating 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 ended up being. We had really good results.

The generalization was: in the understanding lies the power. That was the huge idea. In my profession that is the big, „Ah ha!,“ and it wasn’t the method AI was being done formerly. Sounds simple, however it’s most likely AI’s most powerful generalization. [51]

The other expert systems discussed above came after DENDRAL. MYCIN exhibits the traditional professional system architecture of a knowledge-base of guidelines coupled to a symbolic thinking mechanism, consisting of using certainty factors to handle uncertainty. GUIDON reveals how an explicit understanding base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a specific sort of knowledge-based application. Clancey showed that it was not enough merely to utilize MYCIN’s rules for guideline, but that he also required to include rules for dialogue management and trainee modeling. [50] XCON is significant due to the fact that of the countless dollars it saved DEC, which activated the expert system boom where most all significant corporations in the US had skilled systems groups, to record business competence, maintain it, and automate it:

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

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

Architecture of knowledge-based and expert systems

An essential component of the system architecture for all expert systems is the understanding base, which shops facts and guidelines for analytical. [53] The most basic approach for an expert system knowledge base is just a collection or network of production guidelines. Production guidelines connect symbols in a relationship comparable to an If-Then statement. The expert system processes the rules to make reductions and to identify what extra info it needs, i.e. what concerns to ask, utilizing human-readable signs. For instance, OPS5, CLIPS and their followers Jess and Drools run in this style.

Expert systems can run in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and prerequisites – way. Advanced knowledge-based systems, such as Soar can likewise carry out meta-level thinking, that is thinking about their own thinking in regards to choosing how to resolve issues and monitoring the success of analytical techniques.

Blackboard systems are a 2nd type of knowledge-based or skilled system architecture. They model a community of professionals incrementally contributing, where they can, to resolve an issue. The issue is represented in multiple levels of abstraction or alternate views. The experts (knowledge sources) offer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on a program that is updated as the problem situation modifications. A controller chooses how helpful each contribution is, and who need to make the next analytical action. One example, the BB1 blackboard architecture [54] was initially inspired by research studies of how people plan to carry out numerous tasks in a trip. [55] A development of BB1 was to apply the very same blackboard model to resolving its control problem, i.e., its controller performed meta-level thinking with understanding sources that kept track of how well a strategy or the analytical was continuing and could change from one strategy to another as conditions – such as goals or times – changed. BB1 has actually been used in several domains: building and construction website planning, intelligent tutoring systems, and real-time client monitoring.

The second AI winter, 1988-1993

At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to speed up the advancement of AI applications and research study. In addition, a number of expert system 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 second AI winter season that followed:

Many factors can be provided for the arrival of the second AI winter. The hardware companies stopped working when much more cost-efficient general Unix workstations from Sun together with great compilers for LISP and Prolog came onto the marketplace. Many industrial releases of professional systems were stopped when they showed too pricey to keep. Medical specialist systems never ever caught on for a number of factors: the problem in keeping them as much as date; the challenge for medical experts to discover how to use an overwelming variety of different expert systems for various medical conditions; and perhaps most crucially, the hesitation of medical professionals to trust a computer-made diagnosis over their gut impulse, even for specific domains where the professional systems might outperform an average medical professional. Equity capital cash deserted AI virtually over night. The world AI conference IJCAI hosted a huge and lavish trade convention and thousands of nonacademic attendees in 1987 in Vancouver; the main AI conference the following year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]

Adding in more rigorous structures, 1993-2011

Uncertain reasoning

Both statistical methods and extensions to logic were tried.

One analytical approach, concealed Markov models, had currently been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl promoted the usage of Bayesian Networks as a noise however efficient method of dealing with unsure thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were applied effectively in expert systems. [57] Even later on, in the 1990s, statistical relational knowing, an approach that integrates likelihood with logical formulas, enabled likelihood 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 assistance were likewise attempted. For instance, non-monotonic reasoning could be used with fact upkeep systems. A reality maintenance system tracked assumptions and validations for all reasonings. It permitted inferences to be withdrawn when presumptions were learnt to be inaccurate or a contradiction was obtained. Explanations could be offered for an inference by describing which guidelines were used to create it and then continuing through underlying reasonings and rules all the way back to root assumptions. [58] Lofti Zadeh had actually presented a different sort of extension to handle the representation of ambiguity. For example, in deciding how „heavy“ or „high“ a male is, there is regularly no clear „yes“ or „no“ response, 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 offered a method for propagating combinations of these worths through rational formulas. [59]

Machine knowing

Symbolic maker learning methods were examined to resolve the knowledge acquisition bottleneck. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test method to create plausible rule hypotheses to test against spectra. Domain and task understanding lowered the variety of prospects checked to a manageable size. Feigenbaum described Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s having to do with theory formation. 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 people. But how did individuals get the knowledge? By taking a look at countless spectra. So we desired a program that would take a look at countless spectra and presume the understanding of mass spectrometry that DENDRAL could utilize to resolve individual hypothesis development issues. We did it. We were even able to publish new knowledge of mass spectrometry in the Journal of the American Chemical Society, giving credit only in a footnote that a program, Meta-DENDRAL, actually did it. We were able to do something that had actually been a dream: to have a computer system program created a new and publishable piece of science. [51]

In contrast to the knowledge-intensive approach of Meta-DENDRAL, Ross Quinlan created a domain-independent approach to statistical classification, decision tree learning, starting first with ID3 [60] and after that later extending its capabilities to C4.5. [61] The decision trees developed are glass box, interpretable classifiers, with human-interpretable category guidelines.

Advances were made in understanding artificial intelligence theory, too. Tom Mitchell presented variation space knowing which explains knowing as a search through a space of hypotheses, with upper, more general, and lower, more particular, boundaries encompassing all feasible hypotheses constant with the examples seen up until now. [62] More officially, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]

Symbolic machine learning included more than finding out by example. E.g., John Anderson supplied a cognitive design of human learning where skill practice results in a compilation of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a trainee might learn to apply „Supplementary angles are two angles whose measures sum 180 degrees“ as a number of various procedural rules. E.g., one rule might state that if X and Y are additional and you understand X, then Y will be 180 – X. He called his technique „knowledge collection“. ACT-R has been utilized effectively to design elements of human cognition, such as finding out and retention. ACT-R is likewise utilized in smart tutoring systems, called cognitive tutors, to effectively teach geometry, computer programs, and algebra to school children. [64]

Inductive reasoning programs was another technique to finding out that allowed 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 develop genetic shows, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more general approach to program synthesis that synthesizes a practical program in the course of proving its specs to be right. [66]

As an alternative to logic, Roger Schank introduced case-based thinking (CBR). The CBR technique detailed in his book, Dynamic Memory, [67] focuses initially on keeping in mind key problem-solving cases for future use and generalizing them where appropriate. When confronted with a new issue, CBR retrieves the most comparable previous case and adjusts it to the specifics of the present issue. [68] Another alternative to reasoning, genetic algorithms and genetic programs are based upon an evolutionary model of learning, where sets of guidelines are encoded into populations, the rules govern the habits of people, and choice of the fittest prunes out sets of unsuitable rules over numerous generations. [69]

Symbolic artificial intelligence was applied to learning ideas, rules, heuristics, and problem-solving. Approaches, besides those above, include:

1. Learning from guideline or advice-i.e., taking human guideline, impersonated recommendations, and identifying how to operationalize it in particular circumstances. For example, in a video game of Hearts, finding out precisely how to play a hand to „avoid taking points.“ [70] 2. Learning from exemplars-improving performance by accepting subject-matter specialist (SME) feedback throughout training. When problem-solving stops working, querying the professional to either find out a brand-new exemplar for problem-solving or to discover a new explanation regarding precisely why one exemplar is more appropriate than another. For example, the program Protos found out to diagnose tinnitus cases by communicating with an audiologist. [71] 3. Learning by analogy-constructing problem services based on similar issues seen in the past, and then modifying their options to fit a brand-new circumstance or domain. [72] [73] 4. Apprentice learning systems-learning unique options to issues by observing human analytical. Domain understanding explains why novel solutions are right and how the service can be generalized. LEAP learned how to create VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating tasks to perform experiments and after that gaining from the outcomes. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human gamers at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., searching for useful macro-operators to be gained from series of basic problem-solving actions. Good macro-operators simplify analytical by permitting problems to be solved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the increase of deep knowing, the symbolic AI technique has actually been compared to deep knowing as complementary „… with parallels having actually been drawn many times by AI researchers in between Kahneman’s research on human reasoning and decision making – shown in his book Thinking, Fast and Slow – and the so-called „AI systems 1 and 2″, which would in concept be by deep knowing and symbolic thinking, respectively.“ In this view, symbolic thinking is more apt for deliberative thinking, preparation, and description while deep knowing is more apt for fast pattern recognition in perceptual applications with loud information. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic techniques

Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a manner that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI efficient in thinking, discovering, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the effective building of abundant computational cognitive designs demands the combination of sound symbolic reasoning and efficient (maker) knowing designs. Gary Marcus, likewise, argues that: „We can not construct rich cognitive models in an appropriate, automated method without the set of three of hybrid architecture, rich prior understanding, and sophisticated strategies for thinking.“, [79] and in particular: „To construct a robust, knowledge-driven approach to AI we should have the machinery of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we understand of that can manipulate such abstract knowledge reliably is the apparatus of symbol control. “ [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a requirement to address the two sort of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 elements, System 1 and System 2. System 1 is quickly, automated, intuitive and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind utilized for pattern acknowledgment while System 2 is far much better fit for planning, reduction, and deliberative thinking. In this view, deep learning finest models the first kind of believing while symbolic thinking finest designs the 2nd kind and both are needed.

Garcez and Lamb explain research in this location as being continuous for a minimum of the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking 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 integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively small research neighborhood over the last 2 decades and has yielded numerous considerable outcomes. Over the last years, neural symbolic systems have been shown efficient in getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see likewise (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 applied to a number of issues in the areas of bioinformatics, control engineering, software application confirmation and adjustment, visual intelligence, ontology knowing, and computer games. [78]

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

– Symbolic Neural symbolic-is the current technique of many neural models in natural language processing, where words or subword tokens are both the supreme input and output of large language designs. Examples include 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 approach is Monte Carlo tree search and the neural methods find out how to examine video game positions.
– Neural|Symbolic-uses a neural architecture to interpret affective information as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to create or label training information that is subsequently discovered by a deep learning design, e.g., to train a neural design for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to create or identify examples.
– Neural _ Symbolic -uses a neural net that is produced from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree generated from understanding base guidelines and terms. Logic Tensor Networks [86] likewise fall under this classification.
– Neural [Symbolic] -enables a neural design to straight call a symbolic reasoning engine, e.g., to carry out an action or assess a state.

Many key research study questions remain, such as:

– What is the very best method to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible knowledge be discovered and reasoned about?
– How can abstract understanding that is tough to encode logically be dealt with?

Techniques and contributions

This section provides a summary of methods and contributions in a total context causing numerous other, more comprehensive posts in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section.

AI programs languages

The key AI programming language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest shows language after FORTRAN and was produced in 1958 by John McCarthy. LISP offered the first read-eval-print loop to support fast program advancement. Compiled functions could be freely combined with translated functions. Program tracing, stepping, and breakpoints were also provided, together with 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 originally composed in LISP and then ran interpretively to assemble the compiler code.

Other essential developments pioneered by LISP that have spread to other programs languages include:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

Programs were themselves data structures that other programs could operate on, permitting the simple meaning of higher-level languages.

In contrast to the US, in Europe the crucial AI programming language throughout that same duration was Prolog. Prolog supplied a built-in shop of facts and provisions that could be queried by a read-eval-print loop. The store could function as an understanding base and the clauses might function as guidelines or a limited kind of reasoning. As a subset of first-order reasoning Prolog was based upon Horn clauses with a closed-world assumption-any facts not known were thought about false-and a special name presumption for primitive terms-e.g., the identifier barack_obama was considered to refer to precisely one item. Backtracking and marriage are built-in to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the innovators of Prolog. Prolog is a form of logic shows, which was invented by Robert Kowalski. Its history was likewise influenced 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 also a sort of declarative programs. The reasoning clauses that describe programs are straight analyzed to run the programs specified. No specific series of actions is required, as is the case with important programs languages.

Japan championed Prolog for its Fifth Generation Project, meaning to construct unique hardware for high efficiency. Similarly, LISP machines were constructed to run LISP, however as the 2nd AI boom turned to bust these business might not take on brand-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 programming language. For example, it presented metaclasses and, in addition to Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that allows multiple inheritance, in addition to incremental extensions to both classes and metaclasses, thus offering a run-time meta-object protocol. [88]

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

Search

Search emerges in numerous kinds of issue fixing, including preparation, restraint fulfillment, and playing games such as checkers, chess, and go. The best 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 stipulation learning, and the DPLL algorithm. For adversarial search when playing games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and thinking

Multiple various approaches to represent understanding and then factor with those representations have actually been investigated. Below is a fast summary of methods to understanding representation and automated thinking.

Knowledge representation

Semantic networks, conceptual charts, frames, and logic are all techniques to modeling understanding such as domain understanding, analytical knowledge, and the semantic meaning of language. Ontologies design crucial concepts and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can likewise be seen as an ontology. YAGO integrates 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 currently being used.

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

First-order reasoning is more basic than description logic. The automated theorem provers talked about below can show theorems in first-order reasoning. Horn clause logic is more restricted than first-order logic and is used in logic shows languages such as Prolog. Extensions to first-order logic include temporal logic, to deal with time; epistemic reasoning, to factor about agent understanding; modal logic, to handle possibility and need; and probabilistic reasonings to handle logic and probability together.

Automatic theorem proving

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 deal with evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise understood as Nqthm.

Reasoning in knowledge-based systems

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

Forward chaining inference engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more minimal rational representation is used, Horn Clauses. Pattern-matching, particularly unification, is utilized in Prolog.

A more versatile sort of analytical occurs when reasoning about what to do next takes place, rather than merely choosing among the offered actions. This sort of meta-level thinking is utilized in Soar and in the BB1 blackboard architecture.

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

Commonsense thinking

Marvin Minsky first proposed frames as a way of translating typical visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has actually attempted to capture beneficial sensible understanding and has „micro-theories“ to deal with particular kinds of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about ignorant physics, such as what occurs when we heat up a liquid in a pot on the range. We anticipate it to heat and potentially boil over, although we might not understand its temperature, its boiling point, or other information, such as air pressure.

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

Constraints and constraint-based thinking

Constraint solvers perform a more minimal kind of inference than first-order reasoning. They can simplify sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, in addition to fixing other type of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint logic programming can be used to resolve scheduling problems, for instance with restraint handling guidelines (CHR).

Automated planning

The General Problem Solver (GPS) cast preparation as analytical used means-ends analysis to develop strategies. STRIPS took a various approach, seeing preparation as theorem proving. Graphplan takes a least-commitment method to planning, rather than sequentially selecting actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is a method to preparing where a planning problem is decreased to a Boolean satisfiability problem.

Natural language processing

Natural language processing concentrates on treating language as information to carry out jobs such as recognizing subjects without always understanding the designated significance. Natural language understanding, in contrast, constructs a meaning representation and uses that for additional processing, such as responding to questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all aspects of natural language processing long managed by symbolic AI, but given that enhanced by deep learning methods. In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence significances. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of files. In the latter case, vector components are interpretable as ideas called by Wikipedia short articles.

New deep knowing approaches based on Transformer models have actually now eclipsed these earlier symbolic AI methods and attained advanced efficiency 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 meaning of the vector elements is opaque.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s standard textbook on artificial intelligence is arranged to reflect agent architectures of increasing elegance. [91] The elegance of agents differs from basic reactive representatives, to those with a model 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 with time to select actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep learning for understanding. [92]

In contrast, a multi-agent system consists of multiple representatives that communicate among themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The representatives require not all have the same internal architecture. Advantages of multi-agent systems consist of the capability to divide work amongst the agents and to increase fault tolerance when representatives are lost. Research problems include how representatives reach agreement, distributed problem solving, multi-agent knowing, multi-agent preparation, and dispersed constraint optimization.

Controversies developed from at an early stage in symbolic AI, both within the field-e.g., between logicists (the pro-logic „neats“) and non-logicists (the anti-logic „scruffies“)- and between those who accepted AI however turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mostly from theorists, on intellectual premises, but likewise from financing firms, particularly during the two AI winter seasons.

The Frame Problem: knowledge representation difficulties for first-order logic

Limitations were discovered in using basic first-order reasoning to factor about dynamic domains. Problems were discovered both with regards to identifying the preconditions for an action to be successful and in providing axioms for what did not alter after an action was performed.

McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, „Some Philosophical Problems from the Standpoint of Artificial Intelligence.“ [93] A basic example occurs in „showing that a person person could get into conversation with another“, as an axiom asserting „if a person has a telephone he still has it after looking up a number in the telephone directory“ would be required for the deduction to be successful. Similar axioms would be needed for other domain actions to specify what did not alter.

A similar issue, called the Qualification Problem, takes place in attempting to mention the prerequisites for an action to prosper. A boundless number of pathological conditions can be thought of, e.g., a banana in a tailpipe could prevent a car from running properly.

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

Other methods of handling more open-ended domains included probabilistic thinking systems and artificial intelligence to find out brand-new principles and rules. McCarthy’s Advice Taker can be seen as an inspiration here, as it might include new understanding offered by a human in the kind of assertions or rules. For instance, speculative symbolic maker learning systems checked out the ability to take high-level natural language guidance and to analyze it into domain-specific actionable guidelines.

Similar to the problems in handling vibrant domains, sensible thinking is also challenging to capture in official reasoning. Examples of common-sense thinking include implicit reasoning about how people think or basic knowledge of everyday occasions, things, and living animals. This sort of knowledge is taken for given and not viewed as noteworthy. Common-sense thinking is an open area of research study and challenging both for symbolic systems (e.g., Cyc has tried to capture crucial parts of this knowledge over more than a years) and neural systems (e.g., self-driving cars that do not know not to drive into cones or not to hit pedestrians walking a bike).

McCarthy saw his Advice Taker as having sensible, but his definition of sensible was various than the one above. [94] He specified a program as having common sense „if it instantly deduces for itself a sufficiently broad class of instant consequences of anything it is informed and what it currently understands. „

Connectionist AI: philosophical challenges and sociological disputes

Connectionist approaches consist of 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 innovative methods, such as Transformers, GANs, and other operate in deep learning.

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

1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined completely, and connectionist architectures underlie intelligence and are fully enough to describe it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are needed for intelligence

Olazaran, in his sociological history of the controversies within the neural network neighborhood, explained the moderate connectionism view as essentially compatible with present research study in neuro-symbolic hybrids:

The 3rd and last position I wish to take a look at here is what I call the moderate connectionist view, a more eclectic view of the existing argument in between connectionism and symbolic AI. Among the researchers who has elaborated this position most explicitly is Andy Clark, a thinker from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partially symbolic, partially connectionist) systems. He declared that (a minimum of) two sort of theories are required 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 designs. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative symbol adjustment processes) the symbolic paradigm provides sufficient models, and not only „approximations“ (contrary to what extreme connectionists would declare). [97]

Gary Marcus has actually declared that the animus in the deep learning community against symbolic methods now might be more sociological than philosophical:

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

And yet, for the many part, that’s how most current AI profits. Hinton and lots of others have actually attempted hard to get rid of signs altogether. The deep learning hope-seemingly grounded not a lot in science, however in a sort of historic grudge-is that smart habits will emerge simply from the confluence of enormous data and deep knowing. Where classical computer systems and software application solve jobs by defining sets of symbol-manipulating rules devoted to particular tasks, such as modifying a line in a word processor or performing a calculation in a spreadsheet, neural networks generally attempt to fix tasks by statistical approximation and discovering from examples.

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

When deep knowing reemerged in 2012, it was with a kind of take-no-prisoners attitude that has defined many of the last decade. By 2015, his hostility toward all things symbols had actually completely crystallized. He provided a talk at an AI workshop at Stanford comparing signs to aether, among science’s biggest errors.

Since then, his anti-symbolic campaign has actually just increased in strength. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s crucial journals, Nature. It closed with a direct attack on symbol adjustment, 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 methods was „a huge error,“ likening it to investing in internal combustion engines in the era of electrical cars. [98]

Part of these disagreements may be because of unclear terms:

Turing award winner Judea Pearl uses a critique of artificial intelligence which, unfortunately, conflates the terms artificial intelligence and deep learning. Similarly, when Geoffrey Hinton describes symbolic AI, the undertone of the term tends to be that of expert systems dispossessed of any ability to discover. The use of the terms needs information. Machine learning is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep learning being the choice of representation, localist rational rather than distributed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production guidelines written by hand. A proper definition of AI issues understanding representation and thinking, self-governing multi-agent systems, planning and argumentation, as well as knowing. [99]

Situated robotics: the world as a model

Another critique of symbolic AI is the embodied cognition technique:

The embodied cognition method declares that it makes no sense to consider the brain individually: cognition happens 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 method, robotics, vision, and other sensing units end up being central, not peripheral. [100]

Rodney Brooks created behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this method, is seen as an alternative to both symbolic AI and connectionist AI. His approach rejected representations, either symbolic or dispersed, as not only unnecessary, however as harmful. Instead, he created the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a various function and should work in the genuine world. For example, the very first robot he describes in Intelligence Without Representation, has three layers. The bottom layer analyzes finder sensors to prevent things. The middle layer causes the robotic to wander around when there are no obstacles. The leading layer triggers the robot to go to more far-off places for more exploration. Each layer can briefly prevent or suppress a lower-level layer. He criticized AI researchers for specifying AI issues for their systems, when: „There is no clean division between perception (abstraction) and thinking in the genuine world.“ [101] He called his robots „Creatures“ and each layer was „composed of a fixed-topology network of easy limited state machines.“ [102] In the Nouvelle AI approach, „First, it is critically important to test the Creatures we integrate in the genuine world; i.e., in the very same world that we human beings live in. It is disastrous to fall under the temptation of evaluating them in a streamlined world first, even with the best objectives of later transferring activity to an unsimplified world.“ [103] His focus on real-world testing was in contrast to „Early work in AI focused on 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, however has been slammed by the other methods. Symbolic AI has been criticized as disembodied, liable to the qualification issue, and poor in dealing with the perceptual problems where deep learning excels. In turn, connectionist AI has actually been criticized as badly suited for deliberative step-by-step issue solving, including knowledge, and managing planning. Finally, Nouvelle AI masters reactive and real-world robotics domains however has been criticized for difficulties in integrating knowing and understanding.

Hybrid AIs integrating one or more of these approaches are presently deemed the path forward. [19] [81] [82] Russell and Norvig conclude that:

Overall, Dreyfus saw locations where AI did not have total responses and said that Al is therefore difficult; we now see a lot of these same areas going through continued research and advancement leading to increased ability, not impossibility. [100]

Expert system.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep learning
First-order logic
GOFAI
History of artificial intelligence
Inductive logic programming
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
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 when stated: „This is AI, so we do not care if it’s psychologically real“. [4] McCarthy restated his position in 2006 at the AI@50 conference where he stated „Artificial intelligence is not, by meaning, simulation of human intelligence“. [28] Pamela McCorduck composes that there are „2 major branches of expert system: one focused on producing smart habits regardless of how it was achieved, and the other focused on modeling smart processes discovered in nature, especially human ones.“, [29] Stuart Russell and Peter Norvig wrote „Aeronautical engineering texts do not define the objective of their field as making ‚makers that fly so precisely like pigeons that they can trick even other pigeons.'“ [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). „Reconciling deep learning with symbolic expert system: representing objects and relations“. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). „Logic-Based 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 objects and relations“. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). „Learning representations by back-propagating mistakes“. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). „Backpropagation Applied to Handwritten Zip 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.
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^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). „An interview with Ed Feigenbaum“. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
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