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Symbolic Artificial Intelligence
In expert system, symbolic expert system (likewise understood as classical artificial intelligence or logic-based synthetic intelligence) [1] [2] is the term for the collection of all approaches in artificial intelligence research that are based on high-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI utilized tools such as logic programming, production rules, semantic internet and frames, and it developed applications such as knowledge-based systems (in specific, skilled systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm caused seminal concepts in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and constraints of official knowledge and thinking 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 persuaded that symbolic approaches would ultimately prosper in developing a device with synthetic basic intelligence and considered this the ultimate objective of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, led to impractical expectations and guarantees and was followed by the first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) accompanied the increase of professional systems, their promise of recording business competence, and an enthusiastic corporate accept. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later on dissatisfaction. [8] Problems with troubles in knowledge acquisition, preserving large knowledge bases, and brittleness in managing out-of-domain issues developed. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on dealing with underlying problems in dealing with uncertainty and in knowledge acquisition. [10] Uncertainty was attended to with official techniques such as surprise Markov models, Bayesian thinking, and analytical relational knowing. [11] [12] Symbolic device finding out resolved the understanding acquisition problem with contributions including Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based knowing, and inductive reasoning programming to learn relations. [13]
Neural networks, a subsymbolic method, had actually been pursued from early days and reemerged strongly 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 viewed as successful up until about 2012: “Until Big Data became prevalent, the basic consensus in the Al community was that the so-called neural-network method was hopeless. Systems just didn’t work that well, compared to other methods. … A transformation was available in 2012, when a number of people, including a team of scientists working with Hinton, worked out a way to use the power of GPUs to immensely increase the power of neural networks.” [16] Over the next several years, deep knowing had magnificent success in vision, speech recognition, speech synthesis, image generation, and machine translation. However, because 2020, as inherent difficulties with bias, explanation, comprehensibility, and effectiveness ended up being more apparent with deep learning techniques; an increasing variety of AI scientists have actually required combining the very best of both the symbolic and neural network techniques [17] [18] and addressing areas that both approaches have trouble with, such as sensible reasoning. [16]
A brief history of symbolic AI to the present day follows below. Time durations 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 somewhat for increased clearness.
The first AI summer season: unreasonable exuberance, 1948-1966
Success at early efforts in AI happened in 3 primary locations: synthetic neural networks, knowledge representation, and heuristic search, contributing to high expectations. This area summarizes Kautz’s reprise of early AI history.
Approaches motivated by human or animal cognition or habits
Cybernetic methods attempted to replicate 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 on a preprogrammed neural net, was developed as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, support learning, and located 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 had the ability 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 issue solver, GPS (General Problem Solver). GPS fixed problems represented with official operators through state-space search using means-ends analysis. [21]
During the 1960s, symbolic techniques attained fantastic success at mimicing smart behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was focused in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Every one developed its own style of research study. Earlier approaches based on cybernetics or synthetic neural networks were deserted or pushed into the background.
Herbert Simon and Allen Newell studied human analytical skills and tried to formalize them, and their work laid the structures of the field of synthetic intelligence, along with cognitive science, operations research study and management science. Their research study team utilized the results of mental experiments to develop programs that simulated the strategies that individuals utilized to resolve problems. [22] [23] This tradition, centered at Carnegie Mellon University would ultimately culminate in the advancement of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific type of understanding that we will see later on used in professional systems, early symbolic AI researchers discovered another more basic application of knowledge. These were called heuristics, guidelines of thumb that assist a search in promising instructions: “How can non-enumerative search be useful when the underlying problem is exponentially hard? The approach advocated by Simon and Newell is to use heuristics: quick algorithms that might stop working on some inputs or output suboptimal options.” [26] Another important advance was to find a way to use these heuristics that ensures a service will be found, if there is one, not enduring the occasional fallibility of heuristics: “The A * algorithm provided a basic frame for total and optimum heuristically guided search. A * is utilized as a subroutine within virtually every AI algorithm today however is still no magic bullet; its warranty of completeness is bought at the expense of worst-case rapid time. [26]
Early work on understanding representation and thinking
Early work covered both applications of official thinking emphasizing first-order logic, together with efforts to deal with common-sense thinking in a less official manner.
Modeling official thinking with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that makers did not need to replicate the precise systems of human thought, however might rather search for the essence of abstract reasoning and analytical with reasoning, [27] regardless of whether individuals used the exact same algorithms. [a] His lab at Stanford (SAIL) focused on using formal logic to resolve a wide range of issues, consisting of knowledge representation, preparation and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and in other places in Europe which led to the advancement of the programming language Prolog and the science of logic programs. [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 tough issues in vision and natural language processing needed ad hoc solutions-they argued that no simple and general concept (like logic) would record all the elements of intelligent behavior. Roger Schank described their “anti-logic” methods as “scruffy” (as opposed to the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, given that they need to be constructed by hand, one complex principle at a time. [38] [39] [40]
The first AI winter: crushed dreams, 1967-1977
The first AI winter season was a shock:
During the very first AI summer, lots of individuals believed that machine intelligence might be accomplished in simply a few years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to utilize AI to solve issues of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battleground. Researchers had begun to understand that accomplishing AI was going to be much harder than was supposed a decade earlier, however a combination of hubris and disingenuousness led many university and think-tank researchers to accept financing with guarantees of deliverables that they should have understood they could not satisfy. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had actually been developed, and a remarkable backlash set in. New DARPA leadership canceled existing AI financing programs.
Beyond the United States, the most fertile ground for AI research was the United Kingdom. The AI winter in the UK was stimulated on not a lot by dissatisfied military leaders as by rival academics who saw AI scientists as charlatans and a drain on research funding. A professor of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the country. The report specified that all of the problems being worked on in AI would be better managed 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 surge. [41]
The 2nd AI summer: understanding is power, 1978-1987
Knowledge-based systems
As restrictions with weak, domain-independent approaches ended up being increasingly more obvious, [42] researchers from all three customs started to develop knowledge into AI applications. [43] [7] The knowledge revolution was driven by the realization that understanding underlies high-performance, domain-specific AI applications.
Edward Feigenbaum stated:
– “In the knowledge lies the power.” [44]
to describe that high efficiency in a specific domain needs 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 carry out a complex task well, it must understand an excellent deal about the world in which it operates.
( 2) A possible extension of that concept, called the Breadth Hypothesis: there are two extra abilities needed for intelligent behavior in unforeseen situations: falling back on increasingly general understanding, and analogizing to specific however remote knowledge. [45]
Success with expert systems
This “knowledge revolution” resulted in the development and deployment of expert systems (introduced by Edward Feigenbaum), the very first commercially successful type of AI software. [46] [47] [48]
Key expert systems were:
DENDRAL, which found the structure of organic particles from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested more lab tests, when needed – by interpreting laboratory results, patient history, and doctor observations. “With about 450 rules, MYCIN had the ability to carry out as well as some specialists, and considerably much better than junior physicians.” [49] INTERNIST and CADUCEUS which dealt with internal medication diagnosis. Internist tried to record the competence of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS might eventually detect approximately 1000 various diseases.
– GUIDON, which demonstrated how a knowledge base developed for specialist problem solving 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 reduced the time to about 90 minutes. [9]
DENDRAL is considered the very first expert system that relied on knowledge-intensive problem-solving. It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
One of individuals at Stanford thinking about computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I wanted an induction “sandbox”, he said, “I have just 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 started the DENDRAL Project: I was great at heuristic search approaches, and he had an algorithm that was proficient at creating the chemical issue 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 likewise one of the world’s most appreciated mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We started to add to their knowledge, developing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL a growing number of knowledge. The more you did that, the smarter the program became. We had excellent outcomes.
The generalization was: in the understanding lies the power. That was the big idea. In my career that is the substantial, “Ah ha!,” and it wasn’t the method AI was being done formerly. Sounds simple, but it’s probably AI’s most powerful generalization. [51]
The other expert systems discussed above followed DENDRAL. MYCIN exhibits the timeless expert system architecture of a knowledge-base of rules combined to a symbolic reasoning mechanism, consisting of using certainty aspects to handle uncertainty. GUIDON shows how an explicit knowledge base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a specific sort of knowledge-based application. Clancey showed that it was not enough simply to utilize MYCIN’s rules for direction, however that he also needed to add guidelines for discussion management and trainee modeling. [50] XCON is considerable because of the millions of dollars it saved DEC, which triggered the specialist system boom where most all major corporations in the US had professional systems groups, to catch business expertise, maintain it, and automate it:
By 1988, DEC’s AI group had 40 specialist 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 utilizing or investigating expert systems. [49]
Chess professional understanding was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the aid 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 skilled systems
A crucial component of the system architecture for all specialist systems is the knowledge base, which shops realities and rules for analytical. [53] The simplest technique for a professional system understanding base is simply 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 guidelines to make reductions and to determine what extra details it needs, i.e. what questions to ask, utilizing human-readable symbols. For instance, OPS5, CLIPS and their successors Jess and Drools run in this fashion.
Expert systems can operate in either a forward chaining – from evidence to conclusions – or backwards chaining – from objectives to needed information and prerequisites – manner. More sophisticated knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve issues and monitoring the success of problem-solving strategies.
Blackboard systems are a second kind of knowledge-based or expert system architecture. They design a neighborhood of specialists incrementally contributing, where they can, to solve an issue. The problem is represented in several levels of abstraction or alternate views. The experts (knowledge sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is updated as the problem scenario changes. A controller chooses how useful each contribution is, and who need to make the next analytical action. One example, the BB1 chalkboard architecture [54] was initially motivated by studies of how human beings prepare to carry out numerous tasks in a journey. [55] An innovation of BB1 was to apply the very same chalkboard design to fixing its control issue, i.e., its controller performed meta-level thinking with knowledge sources that kept track of how well a strategy or the analytical was continuing and could switch from one technique to another as conditions – such as goals or times – changed. BB1 has actually been applied in numerous domains: building site preparation, intelligent tutoring systems, and real-time patient tracking.
The second AI winter, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP machines specifically targeted to accelerate the advancement of AI applications and research study. In addition, numerous expert system companies, such as Teknowledge and Inference Corporation, were offering skilled system shells, training, and speaking with to corporations.
Unfortunately, the AI boom did not last and Kautz best explains the second AI winter season that followed:
Many reasons can be offered for the arrival of the second AI winter season. The hardware business stopped working when much more cost-effective basic Unix workstations from Sun together with good compilers for LISP and Prolog came onto the marketplace. Many commercial deployments of expert systems were discontinued when they proved too pricey to keep. Medical specialist systems never captured on for a number of factors: the difficulty in keeping them up to date; the challenge for doctor to find out how to utilize an overwelming variety of different professional systems for various medical conditions; and maybe most crucially, the unwillingness of doctors to rely on a computer-made medical diagnosis over their gut instinct, even for particular domains where the specialist systems could surpass an average doctor. Venture capital money deserted AI almost over night. The world AI conference IJCAI hosted a huge and luxurious trade show and thousands of nonacademic guests in 1987 in Vancouver; the primary AI conference the following year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]
Including more rigorous structures, 1993-2011
Uncertain thinking
Both analytical approaches and extensions to logic were attempted.
One analytical method, hidden Markov models, had actually currently been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl popularized the use of Bayesian Networks as a noise but effective method of dealing with uncertain reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were applied successfully in specialist systems. [57] Even later on, in the 1990s, statistical relational learning, an approach that combines possibility with sensible solutions, allowed likelihood to be combined with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to assistance were also attempted. For instance, non-monotonic reasoning might be utilized with truth upkeep systems. A reality upkeep system tracked assumptions and reasons for all inferences. It permitted reasonings to be withdrawn when assumptions were discovered to be inaccurate or a contradiction was derived. Explanations could be offered a reasoning by explaining which rules were applied to develop it and after that continuing through underlying inferences and rules all the way back to root assumptions. [58] Lofti Zadeh had actually presented a different type of extension to deal with the representation of vagueness. For instance, in choosing how “heavy” or “high” a guy is, there is frequently no clear “yes” or “no” answer, and a predicate for heavy or high would rather return values between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy logic even more provided a means for propagating combinations of these values through logical solutions. [59]
Machine learning
Symbolic device discovering methods were examined to deal with the understanding acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test technique to generate plausible guideline hypotheses to evaluate versus spectra. Domain and job understanding reduced the number of candidates tested to a workable size. Feigenbaum explained Meta-DENDRAL as
… the conclusion 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 used layers of knowledge to guide and prune the search. That understanding acted since we interviewed individuals. But how did individuals get the understanding? By taking a look at thousands of spectra. So we desired a program that would look at countless spectra and infer the knowledge of mass spectrometry that DENDRAL could use to solve individual hypothesis development problems. We did it. We were even able to release brand-new understanding 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 actually been a dream: to have a computer system program created a brand-new and publishable piece of science. [51]
In contrast to the knowledge-intensive method of Meta-DENDRAL, Ross Quinlan developed a domain-independent method to statistical category, choice tree learning, beginning first with ID3 [60] and then later on extending its abilities to C4.5. [61] The decision trees developed are glass box, interpretable classifiers, with human-interpretable category rules.
Advances were made in understanding artificial intelligence theory, too. Tom Mitchell introduced version area knowing which describes learning as an explore a space of hypotheses, with upper, more basic, and lower, more particular, boundaries encompassing all feasible hypotheses constant with the examples seen so far. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of machine knowing. [63]
Symbolic maker finding out included more than learning by example. E.g., John Anderson offered a cognitive design of human learning where skill practice results in a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a trainee may discover to use “Supplementary angles are two angles whose steps sum 180 degrees” as several different procedural rules. E.g., one guideline may state that if X and Y are extra and you understand X, then Y will be 180 – X. He called his technique “understanding collection”. ACT-R has been used successfully to model elements of human cognition, such as finding out and retention. ACT-R is likewise used in intelligent tutoring systems, called cognitive tutors, to effectively teach geometry, computer shows, and algebra to school kids. [64]
Inductive reasoning programs was another technique to finding out that enabled logic programs to be manufactured 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 develop hereditary programming, which he used to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic method to program synthesis that synthesizes a practical program in the course of proving its specs to be proper. [66]
As an alternative to logic, Roger Schank introduced case-based reasoning (CBR). The CBR method laid out in his book, Dynamic Memory, [67] focuses initially on remembering crucial problem-solving cases for future usage and generalizing them where appropriate. When confronted with a brand-new issue, CBR recovers the most comparable previous case and adapts it to the specifics of the present problem. [68] Another alternative to reasoning, genetic algorithms and genetic shows are based upon an evolutionary design of learning, where sets of guidelines are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of unsuitable rules over numerous generations. [69]
Symbolic artificial intelligence was applied to finding out ideas, rules, heuristics, and analytical. Approaches, other than those above, include:
1. Learning from direction or advice-i.e., taking human direction, postured as advice, and determining how to operationalize it in particular situations. For example, in a video game of Hearts, finding out exactly how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter expert (SME) feedback throughout training. When analytical stops working, querying the professional to either learn a new exemplar for problem-solving or to find out a brand-new explanation regarding exactly why one exemplar is more appropriate than another. For example, the program Protos discovered to identify tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing issue options based upon similar problems seen in the past, and after that modifying their solutions to fit a new circumstance or domain. [72] [73] 4. Apprentice knowing systems-learning unique solutions to problems by observing human problem-solving. Domain understanding describes why unique solutions are correct and how the solution can be generalized. LEAP discovered how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., producing jobs to carry out experiments and after that gaining from the results. Doug Lenat’s Eurisko, for instance, learned heuristics to beat human players at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., browsing for beneficial macro-operators to be gained from sequences of fundamental problem-solving actions. Good macro-operators simplify problem-solving by allowing problems to be resolved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the increase of deep knowing, the symbolic AI technique has actually been compared to deep knowing as complementary “… with parallels having actually been drawn sometimes by AI scientists in between Kahneman’s research study on human reasoning and decision making – reflected in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in principle be designed by deep knowing and symbolic thinking, respectively.” In this view, symbolic reasoning is more apt for deliberative reasoning, preparation, and description while deep knowing is more apt for quick pattern acknowledgment in affective applications with noisy data. [17] [18]
Neuro-symbolic AI: incorporating neural and symbolic approaches
Neuro-symbolic AI attempts to incorporate neural and symbolic architectures in a manner that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of thinking, discovering, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the reliable building of abundant computational cognitive models demands the combination of sound symbolic reasoning and effective (machine) knowing models. Gary Marcus, likewise, argues that: “We can not construct rich cognitive models in a sufficient, automatic method without the triune of hybrid architecture, abundant anticipation, and advanced strategies for reasoning.”, [79] and in specific: “To construct 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 equipment that we understand of that can control such abstract knowledge dependably is the device of sign adjustment. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based upon a need to resolve the 2 sort of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having 2 parts, System 1 and System 2. System 1 is fast, automated, instinctive and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, reduction, and deliberative thinking. In this view, deep knowing best designs the first type of thinking while symbolic thinking best designs the 2nd kind and both are needed.
Garcez and Lamb describe research study in this area as being ongoing for a minimum of the past twenty years, [83] dating from their 2002 book on neurosymbolic learning systems. [84] A series of workshops on neuro-symbolic reasoning has actually been held every year because 2005, see http://www.neural-symbolic.org/ for details.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The combination of the symbolic and connectionist paradigms of AI has actually been pursued by a fairly little research neighborhood over the last 2 decades and has yielded several significant results. Over the last decade, neural symbolic systems have been revealed efficient in conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were shown efficient in representing modal and temporal reasonings (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 variety of problems in the locations of bioinformatics, control engineering, software confirmation and adaptation, visual intelligence, ontology knowing, and video game. [78]
Approaches for integration are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:
– Symbolic Neural symbolic-is the existing method of numerous neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language designs. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural methods. In this case the symbolic technique is Monte Carlo tree search and the neural techniques discover how to examine video game positions.
– Neural|Symbolic-uses a neural architecture to translate perceptual data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to create or identify training data that is consequently discovered by a deep knowing design, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to develop or identify examples.
– Neural _ Symbolic -utilizes a neural internet 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 knowledge base rules and terms. Logic Tensor Networks [86] also fall into this category.
– Neural [Symbolic] -enables a neural model to straight call a symbolic thinking engine, e.g., to carry out an action or examine a state.
Many essential 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 drawn out from them?
– How should common-sense knowledge be discovered and reasoned about?
– How can abstract knowledge that is hard to encode logically be handled?
Techniques and contributions
This area offers an overview of methods and contributions in an overall context leading to numerous other, more comprehensive articles in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered previously in the history section.
AI shows languages
The crucial AI shows language in the US throughout the last symbolic AI boom duration was LISP. LISP is the 2nd oldest shows language after FORTRAN and was developed in 1958 by John McCarthy. LISP supplied the first read-eval-print loop to support quick program development. Compiled functions could be easily blended with interpreted functions. Program tracing, stepping, and breakpoints were likewise provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the very first self-hosting compiler, suggesting that the compiler itself was initially composed in LISP and then ran interpretively to put together the compiler code.
Other essential innovations originated by LISP that have actually infected 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 easy meaning of higher-level languages.
In contrast to the US, in Europe the essential AI programming language during that same period was Prolog. Prolog supplied a built-in store of truths and clauses that could be queried by a read-eval-print loop. The store could serve as an understanding base and the provisions could serve as rules or a restricted type of reasoning. As a subset of first-order logic Prolog was based upon Horn provisions with a closed-world assumption-any realities not understood were thought about false-and a special name presumption for primitive terms-e.g., the identifier barack_obama was considered to describe exactly one things. Backtracking and marriage are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the innovators of Prolog. Prolog is a kind of reasoning programs, which was developed by Robert Kowalski. Its history was also affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of techniques. For more detail see the area on the origins of Prolog in the PLANNER short article.
Prolog is also a type of declarative programs. The reasoning provisions that explain programs are straight analyzed to run the programs defined. No explicit series of actions is required, as is the case with crucial shows languages.
Japan promoted Prolog for its Fifth Generation Project, intending to build unique hardware for high efficiency. Similarly, LISP machines were built to run LISP, but as the 2nd AI boom turned to bust these companies might not take on brand-new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history area for more information.
Smalltalk was another influential AI shows language. For instance, it presented metaclasses and, along with Flavors and CommonLoops, affected the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the existing standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits several inheritance, in addition to incremental extensions to both classes and metaclasses, hence providing a run-time meta-object procedure. [88]
For other AI programs languages see this list of programming languages for expert system. Currently, Python, a multi-paradigm programs language, is the most popular shows language, partly due to its substantial plan library that supports data science, natural language processing, and deep learning. Python consists of a read-eval-print loop, functional aspects such as higher-order functions, and object-oriented programs that consists of metaclasses.
Search
Search develops in lots of type of issue fixing, consisting of preparation, restraint satisfaction, and playing games such as checkers, chess, and go. The finest known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause knowing, 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 different methods to represent knowledge and after that factor with those representations have actually been examined. Below is a quick summary of approaches to understanding representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and reasoning are all approaches to modeling understanding such as domain knowledge, problem-solving understanding, and the semantic meaning of language. Ontologies design essential ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can likewise be considered as an ontology. YAGO integrates WordNet as part of its ontology, to line up facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.
Description reasoning is a logic for automated classification of ontologies and for identifying inconsistent category information. OWL is a language utilized to represent ontologies with description reasoning. Protégé is an ontology editor that can read in OWL ontologies and then inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order logic is more general than description logic. The automated theorem provers talked about below can prove theorems in first-order reasoning. Horn stipulation reasoning is more restricted than first-order logic and is utilized in reasoning programming languages such as Prolog. Extensions to first-order logic consist of temporal logic, to deal with time; epistemic reasoning, to reason about representative knowledge; modal reasoning, to handle possibility and need; and probabilistic logics to handle reasoning and probability together.
Automatic theorem proving
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in combination with the Mace4 design checker. ACL2 is a theorem prover that can deal with proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise called Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit knowledge base, typically of rules, to boost reusability throughout domains by separating procedural code and domain understanding. A separate reasoning engine procedures 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 sensible representation is used, Horn Clauses. Pattern-matching, particularly marriage, is used in Prolog.
A more flexible sort of problem-solving occurs when thinking about what to do next takes place, instead of simply choosing among the offered actions. This type of meta-level reasoning is utilized in Soar and in the BB1 blackboard architecture.
Cognitive architectures such as ACT-R may have additional abilities, such as the ability to put together frequently used understanding into higher-level chunks.
Commonsense reasoning
Marvin Minsky initially proposed frames as a way of translating typical visual situations, such as an office, and Roger Schank extended this idea to scripts for typical regimens, such as eating in restaurants. Cyc has tried to capture beneficial sensible understanding and has “micro-theories” to deal with particular sort of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about naive physics, such as what happens when we heat a liquid in a pot on the range. We anticipate it to heat and possibly boil over, although we might not understand its temperature, its boiling point, or other information, such as air pressure.
Similarly, Allen’s temporal period algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be resolved with constraint solvers.
Constraints and constraint-based reasoning
Constraint solvers carry out a more minimal type of inference than first-order reasoning. They can simplify sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, along with fixing other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be utilized to resolve scheduling problems, for instance with restraint dealing with rules (CHR).
Automated preparation
The General Problem Solver (GPS) cast preparation as analytical used means-ends analysis to create plans. STRIPS took a various method, viewing preparation as theorem proving. Graphplan takes a least-commitment approach to preparation, instead of sequentially picking actions from an initial state, working forwards, or an objective state if working backwards. Satplan is an approach to preparing where a preparation issue is lowered to a Boolean satisfiability problem.
Natural language processing
Natural language processing concentrates on treating language as data to perform tasks such as determining topics without necessarily understanding the intended meaning. Natural language understanding, on the other hand, constructs a meaning representation and utilizes that for additional processing, such as addressing questions.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long managed by symbolic AI, however given that enhanced by deep learning approaches. In symbolic AI, discourse representation theory and first-order reasoning have been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also provided vector representations of documents. In the latter case, vector elements are interpretable as concepts called by Wikipedia articles.
New deep knowing techniques based upon Transformer designs have actually now eclipsed these earlier symbolic AI approaches and obtained cutting edge efficiency in natural language processing. However, Transformer designs are opaque 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 elements is nontransparent.
Agents and multi-agent systems
Agents are autonomous systems embedded in an environment they view and act on in some sense. Russell and Norvig’s basic book on artificial intelligence is arranged to show representative architectures of increasing sophistication. [91] The elegance of agents varies from basic reactive representatives, to those with a design of the world and automated planning capabilities, potentially a BDI agent, i.e., one with beliefs, desires, and objectives – or alternatively a support learning model learned gradually to choose actions – up to a mix of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep learning for understanding. [92]
On the other hand, a multi-agent system includes several agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The agents require not all have the very same internal architecture. Advantages of multi-agent systems consist of the capability to divide work amongst the agents and to increase fault tolerance when agents are lost. Research issues include how representatives reach agreement, dispersed problem resolving, multi-agent knowing, multi-agent preparation, and distributed restriction optimization.
Controversies arose from early 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 accepted AI however rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were primarily from philosophers, on intellectual grounds, but likewise from funding firms, specifically during the 2 AI winter seasons.
The Frame Problem: understanding representation difficulties for first-order reasoning
Limitations were discovered in utilizing basic first-order logic to reason about vibrant domains. Problems were discovered both with regards to identifying the prerequisites for an action to succeed and in offering 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 takes place in “proving that one person could get into discussion with another”, as an axiom asserting “if a person has a telephone he still has it after searching for a number in the telephone directory” would be required for the deduction to succeed. Similar axioms would be needed for other domain actions to define what did not change.
A comparable issue, called the Qualification Problem, happens in trying to mention the preconditions for an action to be successful. A boundless number of pathological conditions can be pictured, e.g., a banana in a tailpipe could prevent a cars and truck from operating correctly.
McCarthy’s technique to repair the frame issue was circumscription, a sort of non-monotonic logic where deductions might be made from actions that require only define what would alter while not having to clearly define everything that would not change. Other non-monotonic logics offered reality upkeep systems that revised beliefs causing contradictions.
Other methods of managing more open-ended domains included probabilistic reasoning systems and device learning to learn brand-new concepts and rules. McCarthy’s Advice Taker can be seen as a motivation here, as it might integrate brand-new knowledge supplied by a human in the type of assertions or guidelines. For example, speculative symbolic maker discovering systems explored the ability to take top-level natural language recommendations and to translate it into domain-specific actionable rules.
Similar to the problems in managing vibrant domains, sensible thinking is also tough to record in formal reasoning. Examples of common-sense reasoning consist of implicit reasoning about how individuals think or general knowledge of daily events, things, and living animals. This sort of knowledge is taken for granted and not deemed noteworthy. Common-sense reasoning is an open area of research and challenging both for symbolic systems (e.g., Cyc has tried to capture crucial parts of this understanding 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 strolling a bike).
McCarthy viewed his Advice Taker as having sensible, however 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 a sufficiently wide class of immediate effects of anything it is informed and what it currently knows. “
Connectionist AI: philosophical obstacles and sociological disputes
Connectionist techniques include earlier deal with neural networks, [95] such as perceptrons; operate in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s more sophisticated approaches, such as Transformers, GANs, and other operate in deep knowing.
Three philosophical positions [96] have been detailed amongst connectionists:
1. Implementationism-where connectionist architectures carry out the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected 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 required for intelligence
Olazaran, in his sociological history of the controversies within the neural network community, explained the moderate connectionism view as essentially compatible with current research in neuro-symbolic hybrids:
The 3rd and last position I wish to analyze here is what I call the moderate connectionist view, a more eclectic view of the existing dispute in between connectionism and symbolic AI. One of the researchers who has actually elaborated this position most clearly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark defended hybrid (partially symbolic, partially connectionist) systems. He claimed that (a minimum of) two type of theories are needed in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern acknowledgment) connectionism has advantages over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive thinking, and generative symbol manipulation procedures) the symbolic paradigm offers adequate designs, and not only “approximations” (contrary to what extreme connectionists would declare). [97]
Gary Marcus has declared that the animus in the deep learning neighborhood versus symbolic methods now may be more sociological than philosophical:
To think that we can just abandon symbol-manipulation is to suspend shock.
And yet, for the most part, that’s how most current AI proceeds. Hinton and numerous others have striven to eliminate symbols entirely. The deep learning hope-seemingly grounded not a lot in science, however in a sort of historical grudge-is that intelligent habits will emerge simply from the confluence of enormous data and deep knowing. Where classical computers and software application solve jobs by defining sets of symbol-manipulating rules committed to specific jobs, such as modifying a line in a word processor or carrying out an estimation in a spreadsheet, neural networks normally attempt to resolve jobs by analytical approximation and finding out 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 mindset that has actually identified most of the last years. By 2015, his hostility toward all things symbols had actually totally crystallized. He offered a talk at an AI workshop at Stanford comparing signs to aether, one of science’s biggest errors.
…
Since then, his anti-symbolic campaign has actually only 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 sign manipulation, calling not for reconciliation however for straight-out replacement. Later, Hinton told an event of European Union leaders that investing any further cash in symbol-manipulating approaches was “a big error,” comparing it to purchasing internal combustion engines in the period of electric vehicles. [98]
Part of these conflicts may be due to unclear terminology:
Turing award winner Judea Pearl offers a review of maker knowing which, sadly, conflates the terms machine learning and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the undertone of the term tends to be that of specialist systems dispossessed of any ability to learn. Making use of the terminology requires explanation. Artificial intelligence is not restricted to association guideline mining, c.f. the body of work on symbolic ML and relational learning (the distinctions to deep knowing being the option of representation, localist logical instead of distributed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not just about production guidelines written by hand. An appropriate definition of AI issues knowledge representation and thinking, autonomous multi-agent systems, preparation and argumentation, as well as learning. [99]
Situated robotics: the world as a design
Another review of symbolic AI is the embodied cognition technique:
The embodied cognition technique declares that it makes no sense to think about the brain separately: cognition occurs within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s working exploits consistencies in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units end up being central, not peripheral. [100]
Rodney Brooks created behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this method, is seen as an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or dispersed, as not only unneeded, however as harmful. Instead, he created the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a different purpose and must operate in the real life. For example, the first robotic he describes in Intelligence Without Representation, has three layers. The bottom layer translates sonar sensors to avoid things. The middle layer triggers the robotic to roam around when there are no barriers. The leading layer causes the robotic to go to more remote locations for further expedition. Each layer can temporarily hinder or suppress a lower-level layer. He slammed AI scientists for specifying AI issues for their systems, when: “There is no tidy division in between understanding (abstraction) and reasoning in the real world.” [101] He called his robotics “Creatures” and each layer was “composed of a fixed-topology network of basic finite state machines.” [102] In the Nouvelle AI approach, “First, it is essential to check the Creatures we build in the real world; i.e., in the same world that we humans occupy. It is dreadful to fall under the temptation of checking them in a simplified world initially, even with the very best objectives of later transferring activity to an unsimplified world.” [103] His focus on real-world screening remained in contrast to “Early operate in AI concentrated on games, geometrical issues, symbolic algebra, theorem proving, and other official systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has benefits, however has actually been slammed by the other methods. Symbolic AI has been criticized as disembodied, accountable to the certification problem, and bad in handling the perceptual issues where deep finding out excels. In turn, connectionist AI has been criticized as badly suited for deliberative step-by-step problem resolving, integrating understanding, and dealing with planning. Finally, Nouvelle AI masters reactive and real-world robotics domains however has actually been criticized for difficulties in integrating knowing and understanding.
Hybrid AIs including one or more of these techniques are presently viewed as the path forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw locations where AI did not have complete responses and stated that Al is for that reason impossible; we now see much of these very same locations going through continued research study and advancement causing increased ability, not impossibility. [100]
Artificial intelligence.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint programming
Deep learning
First-order logic
GOFAI
History of artificial intelligence
Inductive logic programming
Knowledge-based systems
Knowledge representation and thinking
Logic shows
Machine knowing
Model checking
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy once said: “This is AI, so we don’t care if it’s mentally genuine”. [4] McCarthy repeated his position in 2006 at the AI@50 conference where he said “Expert system is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of expert system: one targeted at producing smart habits regardless of how it was achieved, and the other intended at modeling intelligent procedures found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the objective of their field as making ‘devices that fly so precisely like pigeons that they can deceive even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep learning with symbolic synthetic intelligence: representing objects and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. 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.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). “On the thresholds 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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^ 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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^ Garcez et al. 2002.
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