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Symbolic Artificial Intelligence
In expert system, symbolic expert system (also known as classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all approaches in artificial intelligence research study that are based upon top-level symbolic (human-readable) representations of problems, reasoning and search. [3] Symbolic AI used tools such as logic programs, production guidelines, semantic nets and frames, and it established applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to influential ideas in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and limitations of formal understanding and reasoning systems.
Symbolic AI was the dominant paradigm of AI research from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would ultimately prosper in creating a device with artificial general intelligence and considered this the supreme goal of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and guarantees and was followed by the very first AI Winter as moneying dried up. [5] [6] A 2nd boom (1969-1986) accompanied the rise of professional systems, their guarantee of catching corporate know-how, 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 dissatisfaction. [8] Problems with difficulties in knowledge acquisition, keeping big understanding bases, and brittleness in handling out-of-domain problems arose. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on resolving hidden problems in dealing with unpredictability and in knowledge acquisition. [10] Uncertainty was addressed with formal approaches such as concealed Markov models, Bayesian reasoning, and analytical relational knowing. [11] [12] Symbolic maker learning resolved the understanding acquisition issue with contributions including Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree knowing, case-based learning, and inductive reasoning shows to find out relations. [13]
Neural networks, a subsymbolic method, had actually been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not deemed successful till about 2012: “Until Big Data ended up being prevalent, the general consensus in the Al neighborhood was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other techniques. … A transformation can be found in 2012, when a variety of individuals, including a team of scientists dealing with Hinton, exercised 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 incredible success in managing vision, speech acknowledgment, speech synthesis, image generation, and machine translation. However, given that 2020, as inherent problems with predisposition, explanation, coherence, and robustness ended up being more apparent with deep learning methods; an increasing number of AI scientists have actually called for integrating the very best of both the symbolic and neural network techniques [17] [18] and resolving areas that both techniques have problem with, such as sensible reasoning. [16]
A short history of symbolic AI to the present day follows below. Period and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia article on the History of AI, with dates and titles differing a little for increased clarity.
The very first AI summertime: illogical spirit, 1948-1966
Success at early attempts in AI happened in three main areas: artificial neural networks, knowledge representation, and heuristic search, contributing to high expectations. This area summarizes Kautz’s reprise of early AI history.
Approaches influenced by human or animal cognition or habits
Cybernetic techniques tried to replicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and guiding, and 7 vacuum tubes for control, based on a preprogrammed neural internet, was constructed as early as 1948. This work can be seen as an early precursor to later work in neural networks, support knowing, and situated 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 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to create a domain-independent problem solver, GPS (General Problem Solver). GPS resolved issues represented with formal operators by means of state-space search using means-ends analysis. [21]
During the 1960s, symbolic approaches attained fantastic success at replicating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one established its own design of research. Earlier approaches based on cybernetics or artificial neural networks were abandoned or pressed into the background.
Herbert Simon and Allen Newell studied human problem-solving abilities and tried to formalize them, and their work laid the foundations of the field of expert system, along with cognitive science, operations research study and management science. Their research study team used the results of mental experiments to establish programs that simulated the techniques that individuals utilized to fix problems. [22] [23] This custom, focused at Carnegie Mellon University would ultimately culminate in the advancement of the Soar architecture in the middle 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific type of knowledge that we will see later on used in expert systems, early symbolic AI scientists found another more general application of knowledge. These were called heuristics, general rules that direct a search in promising instructions: “How can non-enumerative search be useful when the underlying problem is greatly hard? The approach advocated by Simon and Newell is to use heuristics: fast algorithms that might stop working on some inputs or output suboptimal services.” [26] Another crucial advance was to discover a method to use these heuristics that guarantees a service will be found, if there is one, not holding up against the occasional fallibility of heuristics: “The A * algorithm supplied a basic frame for complete and optimum heuristically assisted search. A * is used as a subroutine within almost every AI algorithm today however is still no magic bullet; its assurance of completeness is purchased at the cost of worst-case rapid time. [26]
Early deal with understanding representation and reasoning
Early work covered both applications of formal reasoning emphasizing first-order logic, in addition to efforts to deal with sensible reasoning in a less official manner.
Modeling formal thinking with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that makers did not require to simulate the precise mechanisms of human thought, but could rather attempt to find the essence of abstract thinking and analytical with logic, [27] regardless of whether people used the very same algorithms. [a] His lab at Stanford (SAIL) concentrated on utilizing official logic to resolve a large range of problems, consisting of understanding representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and somewhere else in Europe which caused the advancement of the programming language Prolog and the science of logic shows. [32] [33]
Modeling implicit common-sense understanding with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that resolving difficult problems in vision and natural language processing needed advertisement hoc solutions-they argued that no easy and basic concept (like reasoning) would capture all the elements of intelligent habits. Roger Schank explained their “anti-logic” techniques as “scruffy” (as opposed to the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “shabby” AI, since they must be built by hand, one complex principle 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 very first AI summer, numerous individuals thought that machine intelligence might be accomplished in simply a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to utilize AI to resolve problems of national security; in particular, to automate the translation of Russian to English for intelligence operations and to produce autonomous tanks for the battlefield. Researchers had actually started to realize that accomplishing AI was going to be much more difficult than was supposed a decade previously, but a mix of hubris and disingenuousness led many university and think-tank scientists to accept financing with pledges of deliverables that they need to have known they might not satisfy. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had actually been created, and a remarkable reaction embeded in. New DARPA management canceled existing AI funding programs.
Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the UK was stimulated on not a lot by disappointed military leaders as by rival academics who viewed AI researchers as charlatans and a drain on research study funding. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research study in the nation. The report stated that all of the issues being worked on in AI would be much better managed by scientists from other disciplines-such as applied mathematics. The report also claimed that AI successes on toy problems might never ever scale to real-world applications due to combinatorial explosion. [41]
The 2nd AI summertime: understanding is power, 1978-1987
Knowledge-based systems
As restrictions with weak, domain-independent methods became more and more evident, [42] scientists from all 3 traditions started to construct understanding into AI applications. [43] [7] The understanding revolution was driven by the awareness that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– “In the lies the power.” [44]
to describe that high efficiency in a specific domain requires both general 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 job well, it must understand a good deal about the world in which it operates.
( 2) A possible extension of that concept, called the Breadth Hypothesis: there are two additional abilities necessary for intelligent habits in unexpected scenarios: drawing on progressively general knowledge, and analogizing to particular however remote understanding. [45]
Success with specialist systems
This “knowledge transformation” led to the advancement and implementation of expert systems (introduced by Edward Feigenbaum), the very first commercially successful form of AI software. [46] [47] [48]
Key professional systems were:
DENDRAL, which discovered the structure of natural molecules from their chemical formula and mass spectrometer readings.
MYCIN, which detected bacteremia – and suggested additional lab tests, when essential – by translating lab outcomes, patient history, and medical professional observations. “With about 450 guidelines, MYCIN was able to carry out as well as some experts, and considerably better than junior medical professionals.” [49] INTERNIST and CADUCEUS which dealt with internal medication diagnosis. Internist tried to catch the proficiency of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could eventually identify as much as 1000 different diseases.
– GUIDON, which showed how an understanding base built for specialist problem resolving might be repurposed for teaching. [50] XCON, to configure VAX computers, a then tiresome process that could take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that count on knowledge-intensive analytical. It is explained below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
Among the people at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I informed him I wanted an induction “sandbox”, he said, “I have simply the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from looking at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was great at heuristic search approaches, and he had an algorithm that was excellent at creating the chemical problem area.
We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, creator of the chemical behind the contraceptive pill, and likewise among the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate specialists in mass spectrometry. We began to include to their knowledge, inventing knowledge of engineering as we went along. These experiments totaled up to titrating DENDRAL more and more knowledge. The more you did that, the smarter the program became. We had very excellent outcomes.
The generalization was: in the knowledge lies the power. That was the huge idea. In my profession that is the big, “Ah ha!,” and it wasn’t the way AI was being done previously. Sounds easy, however it’s most likely AI’s most powerful generalization. [51]
The other professional systems mentioned above came after DENDRAL. MYCIN exemplifies the traditional expert system architecture of a knowledge-base of guidelines paired to a symbolic reasoning system, including making use of certainty elements to manage uncertainty. GUIDON shows how an explicit understanding base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a particular sort of knowledge-based application. Clancey revealed that it was not adequate just to utilize MYCIN’s guidelines for instruction, but that he likewise needed to include guidelines for dialogue management and trainee modeling. [50] XCON is substantial since of the millions of dollars it conserved DEC, which set off the expert system boom where most all significant corporations in the US had expert systems groups, to catch business expertise, preserve it, and automate it:
By 1988, DEC’s AI group had 40 specialist systems released, with more on the way. DuPont had 100 in usage and 500 in development. Nearly every major U.S. corporation had its own Al group and was either using or examining specialist systems. [49]
Chess expert understanding was encoded in Deep Blue. In 1996, this enabled IBM’s Deep Blue, with the help of symbolic AI, to win in a game of chess against 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 professional systems is the understanding base, which shops truths and rules for problem-solving. [53] The simplest method for an expert system understanding base is merely a collection or network of production rules. Production guidelines connect symbols in a relationship similar to an If-Then statement. The expert system processes the guidelines to make reductions and to determine what extra info it requires, i.e. what concerns to ask, utilizing human-readable signs. For instance, OPS5, CLIPS and their successors Jess and Drools run in this style.
Expert systems can operate in either a forward chaining – from evidence to conclusions – or backwards chaining – from objectives to required data and requirements – way. More sophisticated knowledge-based systems, such as Soar can likewise perform meta-level thinking, that is reasoning about their own reasoning in terms of deciding how to solve problems and keeping an eye on the success of analytical methods.
Blackboard systems are a 2nd sort of knowledge-based or skilled system architecture. They model a community of professionals incrementally contributing, where they can, to resolve a problem. The issue is represented in numerous 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 issue scenario modifications. A controller chooses how beneficial each contribution is, and who should make the next analytical action. One example, the BB1 blackboard architecture [54] was originally influenced by studies of how human beings prepare to carry out numerous jobs in a trip. [55] A development of BB1 was to apply the exact same chalkboard design to resolving its control problem, i.e., its controller performed meta-level reasoning with understanding sources that kept track of how well a plan or the analytical was continuing and might switch from one strategy to another as conditions – such as goals or times – changed. BB1 has actually been used in several domains: construction site planning, intelligent tutoring systems, and real-time client monitoring.
The second 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 speed up the advancement of AI applications and research study. In addition, several synthetic intelligence companies, such as Teknowledge and Inference Corporation, were offering expert system shells, training, and seeking advice from to corporations.
Unfortunately, the AI boom did not last and Kautz finest explains the second AI winter season that followed:
Many reasons can be offered for the arrival of the second AI winter season. The hardware companies stopped working when a lot more cost-efficient basic Unix workstations from Sun together with good compilers for LISP and Prolog came onto the market. Many industrial deployments of specialist systems were discontinued when they proved too expensive to maintain. Medical expert systems never ever caught on for a number of factors: the trouble in keeping them as much as date; the obstacle for medical specialists to discover how to utilize an overwelming variety of various specialist systems for different medical conditions; and maybe most crucially, the reluctance of medical professionals to rely on a computer-made medical diagnosis over their gut instinct, even for particular domains where the professional systems could exceed a typical physician. Equity capital money deserted AI almost over night. The world AI conference IJCAI hosted an enormous and luxurious trade convention and thousands of nonacademic participants in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]
Including more extensive structures, 1993-2011
Uncertain thinking
Both statistical techniques and extensions to reasoning were attempted.
One statistical technique, hidden Markov designs, had already been popularized in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a noise however efficient method of handling unsure reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were applied effectively in specialist systems. [57] Even later on, in the 1990s, statistical relational knowing, an approach that integrates probability with logical solutions, enabled possibility 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 logic to support were likewise tried. For instance, non-monotonic reasoning might be used with truth maintenance systems. A fact maintenance system tracked assumptions and reasons for all inferences. It enabled inferences to be withdrawn when assumptions were discovered out to be inaccurate or a contradiction was derived. Explanations could be offered an inference by discussing which rules were used to create it and after that continuing through underlying reasonings and guidelines all the method back to root assumptions. [58] Lofti Zadeh had actually presented a different type of extension to handle the representation of ambiguity. For example, in choosing how “heavy” or “tall” a male is, there is often no clear “yes” or “no” answer, and a predicate for heavy or tall would rather return values in between 0 and 1. Those worths represented to what degree the predicates were true. His fuzzy reasoning further offered a means for propagating mixes of these worths through logical formulas. [59]
Artificial intelligence
Symbolic device discovering approaches were examined to deal with the understanding acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to generate possible rule hypotheses to test against spectra. Domain and job understanding minimized the number of prospects evaluated to a manageable size. Feigenbaum described Meta-DENDRAL as
… the culmination of my imagine the early to mid-1960s having to do with theory development. The conception was that you had an issue solver like DENDRAL that took some inputs and produced an output. In doing so, it utilized layers of knowledge to guide and prune the search. That understanding got in there because we talked to people. But how did individuals get the knowledge? By looking at countless spectra. So we desired a program that would take a look at countless spectra and infer the understanding of mass spectrometry that DENDRAL could utilize to fix specific hypothesis formation issues. We did it. We were even able to publish new knowledge of mass spectrometry in the Journal of the American Chemical Society, giving credit just in a footnote that a program, Meta-DENDRAL, actually did it. We had the ability to do something that had been a dream: to have a computer 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 analytical category, choice tree knowing, 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 guidelines.
Advances were made in understanding artificial intelligence theory, too. Tom Mitchell introduced version space learning which explains learning as an explore an area of hypotheses, with upper, more basic, and lower, more particular, borders including 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 device knowing. [63]
Symbolic machine learning included more than discovering by example. E.g., John Anderson provided a cognitive design of human knowing 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 may find out to use “Supplementary angles are two angles whose measures sum 180 degrees” as several various procedural guidelines. E.g., one guideline may say that if X and Y are supplementary and you understand X, then Y will be 180 – X. He called his method “knowledge compilation”. ACT-R has been used successfully to design aspects of human cognition, such as discovering and retention. ACT-R is likewise utilized in intelligent tutoring systems, called cognitive tutors, to effectively teach geometry, computer programming, and algebra to school children. [64]
Inductive reasoning programs was another approach to finding out that allowed reasoning programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could synthesize Prolog programs from examples. [65] John R. Koza applied hereditary algorithms to program synthesis to create genetic programs, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger supplied a more general technique to program synthesis that synthesizes a practical program in the course of showing its requirements to be appropriate. [66]
As an option to logic, Roger Schank presented case-based reasoning (CBR). The CBR technique outlined in his book, Dynamic Memory, [67] focuses first on remembering key problem-solving cases for future use and generalizing them where suitable. When confronted with a new issue, CBR retrieves the most similar previous case and adapts it to the specifics of the current issue. [68] Another alternative to reasoning, hereditary algorithms and genetic programs are based upon an evolutionary model of learning, where sets of guidelines are encoded into populations, the guidelines govern the habits of people, and selection of the fittest prunes out sets of inappropriate rules over lots of generations. [69]
Symbolic artificial intelligence was used to discovering principles, rules, heuristics, and problem-solving. Approaches, other than those above, include:
1. Learning from instruction or advice-i.e., taking human guideline, posed as recommendations, and figuring out how to operationalize it in particular scenarios. For instance, in a video game of Hearts, learning exactly how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter specialist (SME) feedback throughout training. When problem-solving stops working, querying the specialist to either find out a new prototype for problem-solving or to find out a brand-new explanation as to exactly why one exemplar is more appropriate than another. For instance, the program Protos learned to diagnose tinnitus cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing problem services based on comparable issues seen in the past, and after that modifying their solutions to fit a new scenario or domain. [72] [73] 4. Apprentice learning systems-learning novel options to problems by observing human problem-solving. Domain understanding explains why novel options are right and how the solution can be generalized. LEAP discovered how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to perform experiments and then gaining from the results. 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., browsing for beneficial macro-operators to be discovered from series of basic analytical actions. Good macro-operators streamline analytical by allowing issues to be resolved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the rise of deep learning, the symbolic AI technique has actually been compared to deep learning as complementary “… with parallels having been drawn lots of times by AI scientists in between Kahneman’s research study on human thinking and choice making – shown in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in principle be modelled by deep knowing and symbolic thinking, respectively.” In this view, symbolic reasoning is more apt for deliberative thinking, preparation, and description while deep learning is more apt for fast pattern recognition in perceptual applications with loud information. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic methods
Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI capable of thinking, discovering, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the efficient construction of rich computational cognitive models demands the mix of sound symbolic reasoning and effective (maker) learning designs. Gary Marcus, likewise, argues that: “We can not construct rich cognitive designs in an appropriate, automated way without the set of three of hybrid architecture, abundant prior knowledge, and advanced techniques for thinking.”, [79] and in specific: “To develop a robust, knowledge-driven approach to AI we must have the equipment of symbol-manipulation in our toolkit. Too much of beneficial knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only machinery that we know of that can manipulate such abstract understanding dependably is the device of sign control. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based upon a need to deal with the 2 kinds of thinking gone over 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 fast, automatic, instinctive and unconscious. System 2 is slower, detailed, and specific. System 1 is the kind used for pattern acknowledgment while System 2 is far much better suited for preparation, reduction, and deliberative thinking. In this view, deep learning best designs the first sort of thinking while symbolic thinking best models the second kind and both are required.
Garcez and Lamb explain research in this area as being ongoing for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic learning systems. [84] A series of workshops on neuro-symbolic thinking has actually been held every year given that 2005, see http://www.neural-symbolic.org/ for information.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The combination of the symbolic and connectionist paradigms of AI has actually been pursued by a reasonably little research neighborhood over the last twenty years and has actually yielded several considerable outcomes. Over the last decade, neural symbolic systems have been shown capable of conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in action to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and pieces of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a number of problems in the locations of bioinformatics, control engineering, software confirmation and adjustment, visual intelligence, ontology learning, and computer games. [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 method of many neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are utilized to call neural methods. In this case the symbolic approach is Monte Carlo tree search and the neural methods learn how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to analyze perceptual data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to produce or identify training information that is consequently discovered by a deep knowing design, e.g., to train a neural design for symbolic calculation by using a Macsyma-like symbolic mathematics system to produce or identify examples.
– Neural _ Symbolic -utilizes a neural web that is generated from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree produced 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 essential research questions stay, such as:
– What is the finest way to integrate 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 logically be managed?
Techniques and contributions
This area offers an introduction of strategies and contributions in a total context causing many other, more comprehensive short articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history section.
AI shows languages
The key AI programs language in the US throughout the last symbolic AI boom duration was LISP. LISP is the second oldest programs language after FORTRAN and was created in 1958 by John McCarthy. LISP supplied the first read-eval-print loop to support quick program development. Compiled functions might be easily blended with analyzed functions. Program tracing, stepping, and breakpoints were likewise provided, along with the capability to alter worths or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, suggesting that the compiler itself was originally written in LISP and then ran interpretively to put together the compiler code.
Other essential developments pioneered by LISP that have spread out to other programs languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves information structures that other programs could operate on, enabling the simple definition of higher-level languages.
In contrast to the US, in Europe the key AI programs language throughout that very same period was Prolog. Prolog provided an integrated shop of truths and clauses that could be queried by a read-eval-print loop. The store might function as a knowledge base and the stipulations could function as rules or a limited form of reasoning. As a subset of first-order logic Prolog was based upon Horn clauses with a closed-world assumption-any truths not known were considered false-and a special name assumption for primitive terms-e.g., the identifier barack_obama was considered to describe exactly one things. Backtracking and unification are integrated to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the developers of Prolog. Prolog is a form of reasoning shows, which was created by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more detail see the section on the origins of Prolog in the PLANNER short article.
Prolog is likewise a sort of declarative programs. The reasoning clauses that explain programs are straight translated to run the programs specified. No specific series of actions is required, as holds true with imperative programming languages.
Japan promoted Prolog for its Fifth Generation Project, intending to build special hardware for high efficiency. Similarly, LISP devices were constructed to run LISP, however as the 2nd AI boom turned to bust these companies might not take on new workstations that could now run LISP or Prolog natively at comparable speeds. See the history section for more information.
Smalltalk was another prominent AI programs language. For instance, it introduced metaclasses and, along with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits multiple inheritance, in addition to incremental extensions to both classes and metaclasses, thus offering a run-time meta-object procedure. [88]
For other AI programs languages see this list of programs languages for artificial intelligence. Currently, Python, a multi-paradigm shows language, is the most popular programming language, partially due to its extensive plan library that supports information science, natural language processing, and deep learning. Python includes a read-eval-print loop, functional aspects such as higher-order functions, and object-oriented programs that includes metaclasses.
Search
Search occurs in numerous type of issue fixing, including planning, restraint complete satisfaction, and playing games such as checkers, chess, and go. The very best known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven provision learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and thinking
Multiple various approaches to represent understanding and then reason with those representations have actually been investigated. Below is a fast introduction of techniques to understanding representation and automated thinking.
Knowledge representation
Semantic networks, conceptual charts, frames, and logic are all techniques to modeling knowledge such as domain understanding, analytical understanding, and the semantic significance of language. Ontologies model essential principles and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can also be considered as an ontology. YAGO integrates WordNet as part of its ontology, to align truths drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.
Description reasoning is a logic for automated category of ontologies and for identifying irregular classification data. OWL is a language utilized to represent ontologies with description reasoning. Protégé is an ontology editor that can check out in OWL ontologies and after that inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order reasoning is more basic than description logic. The automated theorem provers gone over below can prove theorems in first-order reasoning. Horn clause logic is more restricted than first-order logic and is used in logic programs languages such as Prolog. Extensions to first-order logic consist of temporal logic, to manage time; epistemic reasoning, to reason about representative understanding; modal logic, to deal with possibility and need; and probabilistic logics to manage reasoning and probability together.
Automatic theorem showing
Examples of automated theorem provers for first-order logic are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in combination with the Mace4 model checker. ACL2 is a theorem prover that can deal with proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise referred to as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit understanding base, generally of guidelines, to boost reusability across domains by separating procedural code and domain understanding. A different reasoning engine processes rules and includes, deletes, or modifies an understanding store.
Forward chaining inference engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more minimal rational representation is utilized, Horn Clauses. Pattern-matching, particularly unification, is utilized in Prolog.
A more versatile kind of problem-solving occurs when thinking about what to do next takes place, rather than just selecting one of the available actions. This kind of meta-level thinking is used in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R might have additional abilities, such as the capability to compile often utilized understanding into higher-level chunks.
Commonsense thinking
Marvin Minsky initially proposed frames as a way of translating typical visual scenarios, such as an office, and Roger Schank extended this idea to scripts for typical regimens, such as dining out. Cyc has actually tried to record useful sensible understanding and has “micro-theories” to manage specific sort of domain-specific thinking.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about naive physics, such as what occurs when we warm a liquid in a pot on the stove. We expect it to heat and perhaps boil over, although we might not know 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 thinking about spatial relationships. Both can be resolved with restriction solvers.
Constraints and constraint-based reasoning
Constraint solvers carry out a more limited type of reasoning than first-order reasoning. They can streamline sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, in addition to solving other type of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programming can be used to resolve scheduling problems, for example with restraint dealing with guidelines (CHR).
Automated planning
The General Problem Solver (GPS) cast preparation as analytical utilized means-ends analysis to produce strategies. STRIPS took a different technique, seeing preparation as theorem proving. Graphplan takes a least-commitment method to preparation, instead of sequentially picking actions from an initial state, working forwards, or a goal state if working backwards. Satplan is a method to planning where a planning problem is decreased to a Boolean satisfiability problem.
Natural language processing
Natural language processing focuses on treating language as data to carry out tasks such as identifying topics without always comprehending the designated significance. Natural language understanding, on the other hand, constructs a significance representation and uses that for further processing, such as responding to concerns.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all aspects of natural language processing long dealt with by symbolic AI, but because enhanced by deep learning methods. In symbolic AI, discourse representation theory and first-order reasoning have been utilized to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of files. In the latter case, vector components are interpretable as concepts called by Wikipedia posts.
New deep knowing methods based upon Transformer models have now eclipsed these earlier symbolic AI techniques and attained state-of-the-art efficiency in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the significance of the vector parts 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 textbook on expert system is organized to reflect representative architectures of increasing sophistication. [91] The sophistication of agents differs from easy reactive agents, to those with a model of the world and automated preparation capabilities, perhaps a BDI representative, i.e., one with beliefs, desires, and objectives – or additionally a reinforcement finding out model learned in time to pick actions – up to a combination of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep knowing for perception. [92]
On the other hand, a multi-agent system consists of numerous representatives that communicate amongst themselves with some inter-agent interaction 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 amongst the agents and to increase fault tolerance when agents are lost. Research issues include how agents reach consensus, dispersed issue resolving, multi-agent learning, multi-agent preparation, and dispersed restraint optimization.
Controversies developed from at an early stage in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who embraced AI however rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mostly from philosophers, on intellectual premises, however likewise from funding agencies, especially during the 2 AI winter seasons.
The Frame Problem: knowledge representation challenges for first-order logic
Limitations were found in using easy first-order reasoning to reason about vibrant domains. Problems were discovered both with regards to identifying the preconditions for an action to succeed and in offering axioms for what did not change after an action was performed.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] An easy example takes place in “showing that one person might enter 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 book” would be required for the reduction to be successful. Similar axioms would be needed for other domain actions to define what did not alter.
A similar problem, called the Qualification Problem, takes place in trying to mention the preconditions for an action to be successful. A limitless variety of pathological conditions can be envisioned, e.g., a banana in a tailpipe could avoid an automobile from operating correctly.
McCarthy’s method to repair the frame problem was circumscription, a sort of non-monotonic reasoning where reductions might be made from actions that require just specify what would alter while not needing to explicitly specify whatever that would not alter. Other non-monotonic logics supplied reality maintenance systems that revised beliefs resulting in contradictions.
Other methods of handling more open-ended domains included probabilistic reasoning systems and device knowing to discover brand-new principles and rules. McCarthy’s Advice Taker can be considered as a motivation here, as it could include brand-new understanding supplied by a human in the form of assertions or rules. For example, speculative symbolic maker finding out systems checked out the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules.
Similar to the issues in dealing with dynamic domains, common-sense thinking is likewise hard to capture in official thinking. Examples of sensible thinking include implicit thinking about how people think or general knowledge of day-to-day events, things, and living animals. This sort of knowledge is taken for given and not considered as noteworthy. Common-sense reasoning is an open location of research study and challenging both for symbolic systems (e.g., Cyc has actually attempted to capture crucial parts of this knowledge over more than a years) and neural systems (e.g., self-driving cars that do not understand not to drive into cones or not to strike pedestrians strolling a bicycle).
McCarthy viewed his Advice Taker as having sensible, but his definition of sensible was various than the one above. [94] He defined a program as having sound judgment “if it immediately deduces for itself an adequately large class of immediate consequences of anything it is told and what it currently knows. “
Connectionist AI: philosophical challenges 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 advanced methods, such as Transformers, GANs, and other work in deep learning.
Three philosophical positions [96] have been outlined amongst connectionists:
1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected totally, and connectionist architectures underlie intelligence and are completely enough to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are required for intelligence
Olazaran, in his sociological history of the debates within the neural network community, described the moderate connectionism consider as essentially compatible with existing research study in neuro-symbolic hybrids:
The 3rd and last position I want to analyze here is what I call the moderate connectionist view, a more eclectic view of the current argument in between connectionism and symbolic AI. One of the researchers who has actually elaborated this position most explicitly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partly symbolic, partly connectionist) systems. He declared that (a minimum of) two type of theories are needed in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern recognition) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative symbol manipulation processes) the symbolic paradigm uses appropriate models, and not just “approximations” (contrary to what extreme connectionists would claim). [97]
Gary Marcus has actually claimed that the animus in the deep learning community versus symbolic approaches now may be more sociological than philosophical:
To believe that we can merely abandon symbol-manipulation is to suspend disbelief.
And yet, for the many part, that’s how most current AI profits. Hinton and numerous others have striven to banish signs completely. The deep knowing hope-seemingly grounded not a lot in science, but in a sort of historic grudge-is that smart habits will emerge simply from the confluence of enormous information and deep learning. Where classical computers and software solve tasks by defining sets of symbol-manipulating rules committed to specific jobs, such as editing a line in a word processor or performing an estimation in a spreadsheet, neural networks usually try to fix tasks by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his colleagues have been emphatically “anti-symbolic”:
When deep learning reemerged in 2012, it was with a sort of take-no-prisoners attitude that has defined many of the last years. By 2015, his hostility towards all things symbols had actually totally crystallized. He provided a talk at an AI workshop at Stanford comparing signs to aether, among science’s greatest errors.
…
Ever since, his anti-symbolic campaign has actually just increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in one of science’s essential journals, Nature. It closed with a direct attack on symbol manipulation, calling not for reconciliation but for straight-out replacement. Later, Hinton informed a gathering of European Union leaders that investing any more money in symbol-manipulating methods was “a huge mistake,” comparing it to buying internal combustion engines in the period of electric vehicles. [98]
Part of these disagreements might be due to uncertain terminology:
Turing award winner Judea Pearl offers a critique of maker learning which, regrettably, conflates the terms maker learning and deep learning. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of professional systems dispossessed of any capability to discover. Using the terminology needs information. Machine knowing is not confined to association rule mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep learning being the option of representation, localist logical rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not almost production guidelines written by hand. A proper meaning of AI issues knowledge representation and thinking, self-governing multi-agent systems, planning and argumentation, in addition to learning. [99]
Situated robotics: the world as a design
Another review of symbolic AI is the embodied cognition technique:
The embodied cognition approach claims that it makes no sense to think about the brain separately: cognition happens within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s functioning exploits regularities in its environment, consisting of the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units become main, not peripheral. [100]
Rodney Brooks created behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this technique, is deemed an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or distributed, as not only unnecessary, but as harmful. Instead, he produced the subsumption architecture, a layered architecture for embodied agents. Each layer attains a various purpose and must operate in the real world. For example, the first robotic he describes in Intelligence Without Representation, has 3 layers. The bottom layer translates finder sensing units to prevent things. The middle layer causes the robot to roam around when there are no barriers. The leading layer triggers the robotic to go to more distant locations for additional exploration. Each layer can briefly prevent or reduce a lower-level layer. He criticized AI researchers for defining AI issues for their systems, when: “There is no clean department between understanding (abstraction) and thinking in the real life.” [101] He called his robots “Creatures” and each layer was “composed of a fixed-topology network of easy limited state makers.” [102] In the Nouvelle AI technique, “First, it is essential to evaluate the Creatures we integrate in the real life; i.e., in the exact same world that we humans occupy. It is dreadful to fall into the temptation of testing them in a simplified world initially, even with the finest intents of later transferring activity to an unsimplified world.” [103] His emphasis on real-world testing remained in contrast to “Early operate in AI focused on games, geometrical issues, symbolic algebra, theorem proving, and other formal systems” [104] and using the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, however has been criticized by the other methods. Symbolic AI has actually been criticized as disembodied, responsible to the credentials problem, and bad in managing the affective issues where deep learning excels. In turn, connectionist AI has actually been criticized as improperly matched for deliberative step-by-step problem solving, integrating knowledge, and handling planning. Finally, Nouvelle AI masters reactive and real-world robotics domains but has been slammed for troubles in incorporating learning and knowledge.
Hybrid AIs incorporating several of these methods are currently considered as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have total answers and said that Al is for that reason impossible; we now see numerous of these very same locations going through ongoing research and development resulting in increased ability, not impossibility. [100]
Expert system.
Automated planning and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep knowing
First-order logic
GOFAI
History of synthetic intelligence
Inductive logic programming
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
Model monitoring
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of artificial intelligence
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as said: “This is AI, so we don’t care if it’s emotionally genuine”. [4] McCarthy repeated 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 artificial intelligence: one focused on producing intelligent behavior regardless of how it was accomplished, and the other focused on modeling smart procedures found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the goal of their field as making ‘machines that fly so exactly like pigeons that they can deceive even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing with symbolic expert system: representing items and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep knowing with symbolic synthetic intelligence: representing items and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). “Learning representations by back-propagating mistakes”. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ 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.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). “On the limits of understanding”. Proceedings of the International Workshop on Artificial Intelligence 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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^ Russell & Norvig 2021, pp. 335-337.
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^ Shapiro, Ehud Y (1981 ). “The Model Inference System”. Proceedings of the 7th global joint conference on Expert system. IJCAI. Vol. 2. p. 1064.
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^ Bareiss, Ray; Porter, Bruce; Wier, Craig. “Chapter 4: Protos: An Exemplar-Based Learning Apprentice”. In Michalski, Carbonell & Mitchell (1986 ), pp. 112-139.
^ Carbonell, Jaime. “Chapter 5: Learning by Analogy: Formulating and Generalizing Plans from Past Experience”. In Michalski, Carbonell & Mitchell (1983 ), pp. 137-162.
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^ a b Rossi 2022.
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^ Garcez et al. 2002.
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