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Symbolic Artificial Intelligence
In synthetic intelligence, symbolic expert system (also referred to as classical synthetic intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all methods in synthetic intelligence research study that are based on top-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 established applications such as knowledge-based systems (in particular, professional systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm resulted in seminal concepts in search, symbolic shows languages, agents, multi-agent systems, the semantic web, and the strengths and constraints of formal knowledge and reasoning systems.
Symbolic AI was the dominant paradigm of AI research study from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would ultimately prosper in producing a device with synthetic 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, led to unrealistic expectations and promises and was followed by the very first AI Winter as moneying dried up. [5] [6] A second boom (1969-1986) accompanied the increase of expert systems, their guarantee of capturing business expertise, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later dissatisfaction. [8] Problems with troubles in understanding acquisition, maintaining large knowledge bases, and brittleness in managing out-of-domain issues . Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers focused on addressing hidden issues in dealing with unpredictability and in understanding acquisition. [10] Uncertainty was resolved with formal techniques such as surprise Markov designs, Bayesian thinking, and statistical relational learning. [11] [12] Symbolic device learning attended to the understanding acquisition problem with contributions including Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive reasoning programs to find out relations. [13]
Neural networks, a subsymbolic approach, had actually been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not considered as successful till about 2012: “Until Big Data became prevalent, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn’t work that well, compared to other methods. … A transformation came in 2012, when a number of people, including a group of scientists working with Hinton, worked out a way to utilize the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next numerous years, deep learning had spectacular success in handling vision, speech acknowledgment, speech synthesis, image generation, and maker translation. However, considering that 2020, as intrinsic difficulties with predisposition, explanation, comprehensibility, and toughness became more evident with deep knowing techniques; an increasing number of AI scientists have called for combining the very best of both the symbolic and neural network methods [17] [18] and dealing with areas that both methods have difficulty with, such as sensible reasoning. [16]
A brief history of symbolic AI to today day follows listed below. Period and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia post on the History of AI, with dates and titles differing slightly for increased clearness.
The first AI summertime: unreasonable spirit, 1948-1966
Success at early efforts in AI took place in three primary areas: synthetic neural networks, knowledge representation, and heuristic search, adding to high expectations. This section summarizes Kautz’s reprise of early AI history.
Approaches motivated by human or animal cognition or habits
Cybernetic approaches tried to duplicate the feedback loops 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 net, was developed as early as 1948. This work can be viewed as an early precursor to later operate in neural networks, reinforcement learning, and located robotics. [20]
An important early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to 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 issue solver, GPS (General Problem Solver). GPS solved problems represented with official operators via state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic approaches achieved great success at imitating intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in 4 institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own design of research study. Earlier approaches based upon cybernetics or synthetic neural networks were deserted or pressed into the background.
Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the structures of the field of synthetic intelligence, along with cognitive science, operations research and management science. Their research team used the outcomes of mental experiments to develop programs that simulated the strategies that people utilized to resolve issues. [22] [23] This custom, focused 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 extremely specialized domain-specific kinds of understanding that we will see later on utilized in specialist systems, early symbolic AI scientists discovered another more basic application of understanding. These were called heuristics, general rules that assist a search in appealing directions: “How can non-enumerative search be practical when the underlying issue is significantly tough? The technique promoted by Simon and Newell is to utilize heuristics: fast algorithms that might fail on some inputs or output suboptimal options.” [26] Another essential advance was to discover a method to use these heuristics that ensures an option will be discovered, if there is one, not standing up to the occasional fallibility of heuristics: “The A * algorithm offered a general frame for complete and ideal heuristically assisted search. A * is used as a subroutine within almost every AI algorithm today but is still no magic bullet; its warranty of efficiency is purchased at the cost of worst-case rapid time. [26]
Early deal with knowledge representation and reasoning
Early work covered both applications of official reasoning stressing first-order reasoning, along with attempts to deal with sensible thinking in a less formal way.
Modeling formal reasoning with logic: the “neats”
Unlike Simon and Newell, John McCarthy felt that devices did not require to replicate the exact mechanisms of human thought, however could rather attempt to find the essence of abstract reasoning and analytical with logic, [27] despite whether people used the same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on using formal reasoning to resolve a variety of problems, including understanding representation, planning and learning. [31] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the advancement of the programming language Prolog and the science of reasoning programming. [32] [33]
Modeling implicit sensible understanding with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that solving tough issues in vision and natural language processing required ad hoc solutions-they argued that no simple and basic principle (like logic) would catch all the aspects of smart behavior. Roger Schank described their “anti-logic” techniques as “scruffy” (instead of the “neat” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, given that they need to be constructed by hand, one complex idea at a time. [38] [39] [40]
The first AI winter: crushed dreams, 1967-1977
The first AI winter was a shock:
During the first AI summer season, numerous people thought that device intelligence might be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to utilize AI to solve issues of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battlefield. Researchers had begun to recognize that achieving AI was going to be much more difficult than was expected a decade previously, however a combination of hubris and disingenuousness led many university and think-tank researchers to accept financing with pledges of deliverables that they should have known they might not satisfy. By the mid-1960s neither useful natural language translation systems nor self-governing tanks had been developed, and a significant backlash set in. New DARPA leadership canceled existing AI funding programs.
Outside of the United States, the most fertile ground for AI research was the UK. The AI winter in the United Kingdom was spurred on not a lot by dissatisfied military leaders as by rival academics who viewed AI scientists as charlatans and a drain on research study funding. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the nation. The report specified that all of the issues being dealt with in AI would be much better dealt with by researchers from other disciplines-such as applied mathematics. The report likewise declared that AI successes on toy issues could never ever scale to real-world applications due to combinatorial explosion. [41]
The second AI summer: knowledge is power, 1978-1987
Knowledge-based systems
As limitations with weak, domain-independent techniques ended up being more and more obvious, [42] scientists from all 3 customs started to develop understanding into AI applications. [43] [7] The knowledge transformation was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– “In the understanding lies the power.” [44]
to describe that high performance in a particular domain needs both general and extremely domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to perform a complex job well, it must understand a good deal about the world in which it operates.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are two additional abilities required for intelligent habits in unanticipated situations: falling back on increasingly general knowledge, and analogizing to specific however far-flung knowledge. [45]
Success with professional systems
This “understanding transformation” resulted in the development and deployment of specialist systems (presented 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 particles from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and recommended additional lab tests, when needed – by translating lab results, client history, and physician observations. “With about 450 rules, MYCIN had the ability to perform in addition to some specialists, and substantially much better than junior physicians.” [49] INTERNIST and CADUCEUS which took on internal medication medical diagnosis. Internist attempted to catch the proficiency of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might eventually diagnose as much as 1000 different illness.
– GUIDON, which demonstrated how an understanding base built for specialist problem fixing might be repurposed for mentor. [50] XCON, to configure VAX computers, a then tiresome procedure that could use up to 90 days. XCON minimized the time to about 90 minutes. [9]
DENDRAL is considered the very first professional system that count on knowledge-intensive analytical. It is explained listed 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 genes. When I informed him I desired an induction “sandbox”, he stated, “I have just 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 started the DENDRAL Project: I was proficient at heuristic search methods, and he had an algorithm that was good at generating 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 one of the world’s most respected mass spectrometrists. Carl and his postdocs were world-class specialists in mass spectrometry. We began to include to their knowledge, creating understanding of engineering as we went along. These experiments amounted to titrating DENDRAL increasingly more understanding. The more you did that, the smarter the program ended up being. We had great results.
The generalization was: in the knowledge lies the power. That was the huge idea. In my career that is the substantial, “Ah ha!,” and it wasn’t the way AI was being done previously. Sounds simple, however it’s most likely AI’s most effective generalization. [51]
The other expert systems discussed above followed DENDRAL. MYCIN exhibits the timeless expert system architecture of a knowledge-base of guidelines coupled to a symbolic thinking system, consisting of using certainty elements to deal with uncertainty. GUIDON demonstrates how an explicit knowledge base can be repurposed for a second application, tutoring, and is an example of a smart tutoring system, a particular kind of knowledge-based application. Clancey revealed that it was not adequate merely to utilize MYCIN’s guidelines for instruction, but that he likewise needed to add rules for discussion management and trainee modeling. [50] XCON is significant since of the millions of dollars it conserved DEC, which activated the specialist system boom where most all major corporations in the US had expert systems groups, to capture business proficiency, protect it, and automate it:
By 1988, DEC’s AI group had 40 professional systems released, with more on the way. DuPont had 100 in usage and 500 in advancement. Nearly every major U.S. corporation had its own Al group and was either utilizing or examining professional systems. [49]
Chess specialist knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the aid of symbolic AI, to win in a video game of chess versus the world champion at that time, Garry Kasparov. [52]
Architecture of knowledge-based and professional systems
An essential part of the system architecture for all specialist systems is the understanding base, which shops truths and rules for analytical. [53] The simplest technique for an expert system knowledge base is just a collection or network of production guidelines. Production rules link signs in a relationship comparable to an If-Then declaration. The specialist system processes the rules to make deductions and to identify what additional details it needs, i.e. what questions to ask, using human-readable signs. For example, OPS5, CLIPS and their successors Jess and Drools run in this style.
Expert systems can run in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and requirements – way. Advanced knowledge-based systems, such as Soar can likewise carry out meta-level thinking, that is thinking about their own thinking in regards to deciding how to resolve problems and keeping track of the success of problem-solving techniques.
Blackboard systems are a second kind of knowledge-based or skilled system architecture. They model a neighborhood of professionals incrementally contributing, where they can, to fix an issue. The issue is represented in multiple levels of abstraction or alternate views. The professionals (knowledge sources) volunteer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is upgraded as the issue scenario changes. A controller decides how beneficial each contribution is, and who should make the next analytical action. One example, the BB1 blackboard architecture [54] was initially influenced by studies of how people plan to carry out multiple tasks in a journey. [55] A development of BB1 was to apply the same blackboard design to fixing its control problem, i.e., its controller performed meta-level reasoning with knowledge sources that kept an eye on how well a strategy or the analytical was proceeding and might change from one strategy to another as conditions – such as goals or times – altered. BB1 has actually been applied in several domains: building website preparation, intelligent tutoring systems, and real-time patient monitoring.
The second AI winter, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP makers particularly targeted to speed up the advancement of AI applications and research study. In addition, numerous expert system business, 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 describes the 2nd AI winter that followed:
Many factors can be used for the arrival of the 2nd AI winter. The hardware companies failed when much more cost-effective general Unix workstations from Sun together with good compilers for LISP and Prolog came onto the market. Many commercial implementations of expert systems were discontinued when they showed too pricey to maintain. Medical professional systems never captured on for numerous reasons: the problem in keeping them up to date; the difficulty for physician to find out how to utilize a bewildering range of various professional systems for different medical conditions; and possibly most crucially, the hesitation of doctors to trust a computer-made medical diagnosis over their gut impulse, even for particular domains where the expert systems might outshine a typical medical professional. Equity capital money deserted AI practically over night. The world AI conference IJCAI hosted a huge and extravagant trade convention and thousands of nonacademic attendees in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a little and strictly scholastic affair. [9]
Adding in more strenuous structures, 1993-2011
Uncertain reasoning
Both statistical methods and extensions to reasoning were attempted.
One analytical approach, concealed Markov designs, had actually currently been popularized in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized making use of Bayesian Networks as a sound but efficient method of dealing with unpredictable thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian approaches were used successfully in expert systems. [57] Even later on, in the 1990s, statistical relational knowing, an approach that integrates probability with sensible formulas, permitted likelihood to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order reasoning to assistance were also tried. For example, non-monotonic reasoning could be used with fact upkeep systems. A reality upkeep system tracked presumptions and justifications for all reasonings. It allowed inferences to be withdrawn when presumptions were learnt to be incorrect or a contradiction was obtained. Explanations might be attended to a reasoning by discussing which guidelines were used to produce it and then continuing through underlying reasonings and rules all the method back to root presumptions. [58] Lofti Zadeh had actually presented a different sort of extension to handle the representation of ambiguity. For example, in choosing how “heavy” or “high” a guy is, there is regularly no clear “yes” or “no” answer, and a predicate for heavy or tall would instead return values in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy logic further supplied a way for propagating combinations of these values through rational formulas. [59]
Artificial intelligence
Symbolic maker finding out methods were examined to attend to the knowledge acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to produce possible rule hypotheses to check versus spectra. Domain and task knowledge minimized the variety of candidates tested to a workable size. Feigenbaum described Meta-DENDRAL as
… the conclusion of my imagine the early to mid-1960s relating to theory formation. The conception was that you had a problem solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of knowledge to steer and prune the search. That understanding got in there due to the fact that we talked to people. But how did individuals get the understanding? By looking at thousands of spectra. So we wanted a program that would look at thousands of spectra and infer the knowledge of mass spectrometry that DENDRAL might use to solve specific hypothesis formation problems. We did it. We were even able to release new understanding of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, really did it. We had the ability to do something that had actually been a dream: to have a computer system program come up with a brand-new and publishable piece of science. [51]
In contrast to the knowledge-intensive technique of Meta-DENDRAL, Ross Quinlan developed a domain-independent technique to statistical category, decision tree knowing, beginning initially with ID3 [60] and after that later extending its abilities to C4.5. [61] The decision trees produced are glass box, interpretable classifiers, with human-interpretable classification rules.
Advances were made in understanding device knowing theory, too. Tom Mitchell presented version area learning which explains knowing as a search through a space of hypotheses, with upper, more general, and lower, more particular, borders encompassing all viable hypotheses constant with the examples seen up until now. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of maker learning. [63]
Symbolic device learning encompassed more than discovering by example. E.g., John Anderson supplied a cognitive model of human learning where ability practice leads to a compilation of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a trainee might find out to use “Supplementary angles are 2 angles whose steps sum 180 degrees” as numerous various procedural guidelines. E.g., one rule may say that if X and Y are additional and you understand X, then Y will be 180 – X. He called his approach “understanding collection”. ACT-R has actually been used successfully to design aspects of human cognition, such as learning and retention. ACT-R is also used in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer system programming, and algebra to school children. [64]
Inductive logic shows was another technique to learning that permitted logic programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could manufacture Prolog programs from examples. [65] John R. Koza used hereditary algorithms to program synthesis to produce hereditary programs, which he utilized to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic technique to program synthesis that manufactures a practical program in the course of proving its requirements to be proper. [66]
As an option to logic, Roger Schank presented case-based thinking (CBR). The CBR method detailed in his book, Dynamic Memory, [67] focuses initially on remembering key problem-solving cases for future use and generalizing them where suitable. When confronted with a brand-new issue, CBR retrieves the most comparable previous case and adapts it to the specifics of the present problem. [68] Another option to logic, hereditary algorithms and genetic programs are based on an evolutionary model of learning, where sets of rules are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of unsuitable guidelines over numerous generations. [69]
Symbolic maker knowing was applied to learning principles, rules, heuristics, and problem-solving. Approaches, besides those above, include:
1. Learning from guideline or advice-i.e., taking human guideline, impersonated guidance, and determining how to operationalize it in specific circumstances. For instance, in a game of Hearts, finding out 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 analytical fails, querying the expert to either learn a brand-new exemplar for analytical or to learn a new explanation regarding exactly why one exemplar is more relevant than another. For example, the program Protos learned to identify ringing in the ears cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing problem services based on similar problems seen in the past, and after that modifying their solutions to fit a new circumstance or domain. [72] [73] 4. Apprentice learning systems-learning unique services to problems by observing human analytical. Domain knowledge describes why unique services are proper and how the solution can be generalized. LEAP found out how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing tasks to perform experiments and after that discovering from the results. Doug Lenat’s Eurisko, for example, learned heuristics to beat human gamers at the Traveller role-playing game for 2 years in a row. [75] 6. Learning macro-operators-i.e., looking for beneficial macro-operators to be gained from series of standard analytical actions. Good macro-operators streamline problem-solving by permitting problems to be fixed 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 been drawn sometimes by AI scientists between Kahneman’s research study on human thinking 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 modelled by deep knowing and symbolic thinking, respectively.” In this view, symbolic thinking is more apt for deliberative reasoning, planning, and explanation while deep knowing is more apt for fast pattern recognition in affective applications with loud information. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic techniques
Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary style, in order to support robust AI capable of thinking, learning, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the effective building of rich computational cognitive models demands the mix of sound symbolic reasoning and effective (machine) learning designs. Gary Marcus, similarly, argues that: “We can not build abundant cognitive designs in an adequate, automated method without the set of three of hybrid architecture, rich anticipation, and advanced techniques for reasoning.”, [79] and in specific: “To develop a robust, knowledge-driven method to AI we must have the equipment of symbol-manipulation in our toolkit. Excessive of beneficial knowledge is abstract to make do without tools that represent and control abstraction, and to date, the only equipment that we know of that can manipulate such abstract understanding reliably is the device of symbol adjustment. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based upon a requirement to deal with the two kinds of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two components, System 1 and System 2. System 1 is quick, automatic, intuitive and unconscious. System 2 is slower, step-by-step, and specific. System 1 is the kind used for pattern acknowledgment while System 2 is far much better suited for planning, deduction, and deliberative thinking. In this view, deep knowing finest models the very first sort of believing while symbolic reasoning best models the second kind and both are needed.
Garcez and Lamb explain research study in this area as being continuous for a minimum of the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has actually been held every year given that 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 integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively small research community over the last 2 years and has actually yielded numerous considerable outcomes. Over the last years, neural symbolic systems have been revealed capable of overcoming 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 capable of 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 actually been applied to a variety of issues in the areas of bioinformatics, control engineering, software application verification and adjustment, visual intelligence, ontology learning, and video game. [78]
Approaches for integration are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:
– Symbolic Neural symbolic-is the 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 consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural strategies. In this case the symbolic approach is Monte Carlo tree search and the neural methods discover how to assess game positions.
– Neural|Symbolic-uses a neural architecture to translate perceptual data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to create or identify training data that is consequently discovered by a deep knowing model, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to create or identify examples.
– Neural _ Symbolic -uses a neural net that is produced from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree produced from knowledge base guidelines and terms. Logic Tensor Networks [86] likewise fall into this category.
– Neural [Symbolic] -enables a neural model to directly call a symbolic thinking engine, e.g., to perform an action or evaluate a state.
Many key research questions stay, such as:
– What is the very best method to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should common-sense understanding be found out and reasoned about?
– How can abstract understanding that is tough to encode logically be managed?
Techniques and contributions
This section provides a summary of strategies and contributions in a total context resulting in many other, more detailed articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history area.
AI shows languages
The essential AI shows language in the US during the last symbolic AI boom period was LISP. LISP is the second earliest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP offered the first read-eval-print loop to support quick program development. Compiled functions could be freely combined with interpreted functions. Program tracing, stepping, and breakpoints were also provided, together with the ability 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 after that ran interpretively to assemble the compiler code.
Other crucial developments originated by LISP that have actually infected other shows languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs might operate on, allowing the easy meaning of higher-level languages.
In contrast to the US, in Europe the key AI programming language throughout that same period was Prolog. Prolog supplied an integrated store of realities and provisions that could be queried by a read-eval-print loop. The store could function as an understanding base and the provisions could function as guidelines or a limited form of reasoning. As a subset of first-order reasoning Prolog was based upon Horn stipulations with a closed-world assumption-any facts not known were considered false-and a special name presumption for primitive terms-e.g., the identifier barack_obama was thought about to describe precisely one item. Backtracking and unification are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of logic 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 methods. For more detail see the section on the origins of Prolog in the PLANNER post.
Prolog is also a kind of declarative shows. The logic clauses that explain programs are directly translated to run the programs specified. No explicit series of actions is required, as holds true with crucial programs languages.
Japan championed Prolog for its Fifth Generation Project, planning to develop unique hardware for high performance. Similarly, LISP makers were constructed to run LISP, but as the second AI boom turned to bust these business could not take on brand-new workstations that could now run LISP or Prolog natively at comparable speeds. See the history area for more information.
Smalltalk was another influential AI shows language. For example, 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 current standard Lisp dialect. CLOS is a Lisp-based object-oriented system that allows several inheritance, in addition to incremental extensions to both classes and metaclasses, thus supplying a run-time meta-object procedure. [88]
For other AI programs languages see this list of programming languages for artificial intelligence. Currently, Python, a multi-paradigm programming language, is the most popular programming language, partially due to its extensive bundle library that supports information science, natural language processing, and deep knowing. Python includes a read-eval-print loop, practical aspects such as higher-order functions, and object-oriented programs that includes metaclasses.
Search
Search occurs in lots of sort of problem solving, consisting of preparation, constraint complete satisfaction, and playing games such as checkers, chess, and go. The very best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven stipulation learning, and the DPLL algorithm. For adversarial search when playing games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and reasoning
Multiple various methods to represent knowledge and then reason with those representations have been examined. Below is a quick overview of methods to understanding representation and automated thinking.
Knowledge representation
Semantic networks, conceptual graphs, frames, and logic are all techniques to modeling understanding such as domain understanding, problem-solving understanding, and the semantic significance of language. Ontologies model crucial 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 viewed as an ontology. YAGO incorporates WordNet as part of its ontology, to align realities drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.
Description logic is a reasoning for automated category of ontologies and for discovering irregular 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 after that examine consistency with deductive classifiers such as such as HermiT. [89]
First-order reasoning is more basic than description logic. The automated theorem provers discussed below can prove theorems in first-order logic. Horn stipulation reasoning 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 reasoning, to manage time; epistemic reasoning, to reason about agent understanding; modal reasoning, to handle possibility and need; and probabilistic reasonings to handle reasoning and possibility together.
Automatic theorem showing
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be used in conjunction with the Mace4 model checker. ACL2 is a theorem prover that can deal with evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, also known as Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit understanding base, normally of rules, to boost reusability across domains by separating procedural code and domain understanding. A different reasoning engine procedures rules and adds, deletes, or modifies an understanding store.
Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more restricted rational representation is used, Horn Clauses. Pattern-matching, particularly marriage, is used in Prolog.
A more flexible kind of problem-solving takes place when reasoning about what to do next takes place, instead of merely picking one of the available actions. This type of meta-level thinking is utilized in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R may have additional capabilities, such as the capability to assemble regularly utilized knowledge into higher-level pieces.
Commonsense reasoning
Marvin Minsky first proposed frames as a way of analyzing typical visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for typical regimens, such as dining out. Cyc has actually attempted to capture useful common-sense understanding and has “micro-theories” to manage specific type of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human reasoning about ignorant physics, such as what occurs when we heat up a liquid in a pot on the stove. We expect it to heat and perhaps boil over, despite the fact that we might not know its temperature, its boiling point, or other information, such as atmospheric pressure.
Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. Both can be fixed with restriction solvers.
Constraints and constraint-based reasoning
Constraint solvers carry out a more restricted sort of inference than first-order logic. They can streamline sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, together with resolving other kinds of puzzle issues, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programs can be used to resolve scheduling issues, for example with restriction managing rules (CHR).
Automated planning
The General Problem Solver (GPS) cast preparation as analytical utilized means-ends analysis to produce plans. STRIPS took a various technique, seeing preparation as theorem proving. Graphplan takes a least-commitment approach to preparation, rather than sequentially choosing actions from a preliminary state, working forwards, or a goal state if working backwards. Satplan is a method to preparing where a preparation issue is reduced to a Boolean satisfiability problem.
Natural language processing
Natural language processing concentrates on treating language as information to carry out jobs such as recognizing subjects without always understanding the designated significance. Natural language understanding, on the other hand, constructs a meaning representation and utilizes that for further processing, such as answering questions.
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, however since enhanced by deep knowing methods. In symbolic AI, discourse representation theory and first-order logic have been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also supplied vector representations of documents. In the latter case, vector components are interpretable as concepts called by Wikipedia short articles.
New deep learning methods based upon Transformer designs have actually now eclipsed these earlier symbolic AI methods and attained state-of-the-art efficiency in natural language processing. However, Transformer designs are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the significance of the vector components is nontransparent.
Agents and multi-agent systems
Agents are self-governing systems embedded in an environment they perceive and act on in some sense. Russell and Norvig’s standard textbook on artificial intelligence is arranged to reflect agent architectures of increasing elegance. [91] The sophistication of agents differs from easy reactive agents, to those with a model of the world and automated preparation capabilities, potentially a BDI representative, i.e., one with beliefs, desires, and intents – or additionally a reinforcement finding out model discovered over time to select actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep knowing for understanding. [92]
On the other hand, a multi-agent system consists of multiple representatives that interact 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 capability to divide work among the agents and to increase fault tolerance when representatives are lost. Research issues include how agents reach consensus, dispersed issue fixing, multi-agent knowing, multi-agent planning, and dispersed restraint optimization.
Controversies developed from early in symbolic AI, both within the field-e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who embraced AI however rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were primarily from thinkers, on intellectual grounds, however likewise from funding companies, especially throughout the 2 AI winter seasons.
The Frame Problem: understanding representation obstacles for first-order logic
Limitations were found in utilizing easy first-order logic to factor about vibrant domains. Problems were found both with concerns to specifying the preconditions for an action to prosper and in providing axioms for what did not change after an action was performed.
McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] A simple example occurs in “showing that one person could enter discussion with another”, as an axiom asserting “if an individual has a telephone he still has it after searching for a number in the telephone book” would be required for the reduction to prosper. Similar axioms would be required for other domain actions to define what did not alter.
A comparable issue, called the Qualification Problem, happens in trying to identify the preconditions for an action to be successful. A boundless number of pathological conditions can be pictured, e.g., a banana in a tailpipe might prevent an automobile from running properly.
McCarthy’s technique to fix the frame problem was circumscription, a kind of non-monotonic reasoning where deductions might be made from actions that need just specify what would alter while not needing to explicitly specify everything that would not change. Other non-monotonic logics offered fact maintenance systems that modified beliefs leading to contradictions.
Other methods of handling more open-ended domains included probabilistic reasoning systems and machine knowing to discover new ideas and guidelines. McCarthy’s Advice Taker can be considered as a motivation here, as it could integrate brand-new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic device finding out systems checked out the capability to take top-level natural language suggestions and to translate it into domain-specific actionable guidelines.
Similar to the issues in handling vibrant domains, common-sense reasoning is also hard to capture in formal thinking. Examples of sensible reasoning consist of implicit reasoning about how individuals believe or basic understanding of everyday occasions, items, and living creatures. This sort of knowledge is considered approved and not viewed as noteworthy. Common-sense reasoning is an open area of research study and challenging both for symbolic systems (e.g., Cyc has tried to capture essential parts of this understanding over more than a decade) and neural systems (e.g., self-driving cars that do not know not to drive into cones or not to strike pedestrians strolling a bicycle).
McCarthy viewed his Advice Taker as having sensible, however his meaning of common-sense was various than the one above. [94] He specified a program as having good sense “if it immediately deduces for itself an adequately broad class of immediate consequences of anything it is informed and what it already understands. “
Connectionist AI: philosophical obstacles and sociological disputes
Connectionist methods consist of earlier work on 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 advanced approaches, such as Transformers, GANs, and other operate in deep knowing.
Three philosophical positions [96] have actually been detailed amongst connectionists:
1. Implementationism-where connectionist architectures carry out the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down totally, and connectionist architectures underlie intelligence and are completely enough to describe it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are needed for intelligence
Olazaran, in his sociological history of the controversies within the neural network community, explained the moderate connectionism consider as basically compatible with existing research study in neuro-symbolic hybrids:
The 3rd and last position I wish to analyze here is what I call the moderate connectionist view, a more diverse view of the existing dispute between connectionism and symbolic AI. Among the scientists who has actually elaborated this position most clearly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partially symbolic, partly connectionist) systems. He declared that (a minimum of) two kinds of theories are required in order to study and design cognition. On the one hand, for some information-processing tasks (such as pattern acknowledgment) connectionism has benefits over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive thinking, and generative symbol manipulation processes) the symbolic paradigm provides sufficient models, and not just “approximations” (contrary to what extreme connectionists would claim). [97]
Gary Marcus has actually declared that the animus in the deep knowing neighborhood versus symbolic methods now might be more sociological than philosophical:
To think that we can just desert symbol-manipulation is to suspend shock.
And yet, for the many part, that’s how most present AI earnings. Hinton and numerous others have actually striven to eliminate symbols completely. The deep learning hope-seemingly grounded not a lot in science, but in a sort of historic grudge-is that intelligent behavior will emerge simply from the confluence of massive information and deep learning. Where classical computers and software application solve tasks by specifying sets of symbol-manipulating rules dedicated to particular tasks, such as modifying a line in a word processor or performing a calculation in a spreadsheet, neural networks usually try to fix tasks by analytical approximation and learning from examples.
According to Marcus, Geoffrey Hinton and his colleagues have actually been emphatically “anti-symbolic”:
When deep learning reemerged in 2012, it was with a sort of take-no-prisoners attitude that has actually defined most of the last decade. By 2015, his hostility towards all things symbols had actually completely taken shape. He lectured at an AI workshop at Stanford comparing signs to aether, one of science’s greatest mistakes.
…
Since then, his anti-symbolic campaign has only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in one of science’s most crucial journals, Nature. It closed with a direct attack on symbol adjustment, calling not for reconciliation however for outright replacement. Later, Hinton told an event of European Union leaders that investing any more money in symbol-manipulating techniques was “a big error,” likening it to buying internal combustion engines in the period of electric vehicles. [98]
Part of these disputes may be because of unclear terms:
Turing award winner Judea Pearl offers a review of maker learning which, regrettably, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of expert systems dispossessed of any ability to discover. The use of the terms requires explanation. Machine knowing is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep learning being the choice of representation, localist logical instead of distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not practically production rules composed by hand. A proper meaning of AI concerns knowledge representation and reasoning, autonomous multi-agent systems, planning and argumentation, along with knowing. [99]
Situated robotics: the world as a design
Another critique of symbolic AI is the embodied cognition approach:
The embodied cognition method declares that it makes no sense to consider the brain independently: cognition occurs within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s functioning exploits consistencies in its environment, including the rest of its body. Under the embodied cognition method, robotics, vision, and other sensors become central, not peripheral. [100]
Rodney Brooks developed behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this technique, is viewed as an alternative to both symbolic AI and connectionist AI. His approach rejected representations, either symbolic or distributed, as not only unnecessary, however as detrimental. Instead, he produced the subsumption architecture, a layered architecture for embodied agents. Each layer attains a different function and needs to function in the real life. For example, the very first robotic he describes in Intelligence Without Representation, has 3 layers. The bottom layer translates sonar sensors to avoid items. The middle layer causes the robot to wander around when there are no challenges. The top layer triggers the robotic to go to more distant locations for additional expedition. Each layer can momentarily hinder or reduce a lower-level layer. He slammed AI researchers for defining AI problems for their systems, when: “There is no clean division in between perception (abstraction) and reasoning in the real life.” [101] He called his robots “Creatures” and each layer was “made up of a fixed-topology network of easy finite state machines.” [102] In the Nouvelle AI approach, “First, it is essential to evaluate the Creatures we construct in the real life; i.e., in the exact same world that we people occupy. It is dreadful to fall under the temptation of evaluating them in a simplified world first, even with the very best objectives of later transferring activity to an unsimplified world.” [103] His emphasis on real-world screening was in contrast to “Early operate in AI concentrated on video games, geometrical problems, symbolic algebra, theorem proving, and other 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 actually been criticized by the other methods. Symbolic AI has actually been criticized as disembodied, accountable to the qualification issue, and bad in handling the perceptual issues where deep learning excels. In turn, connectionist AI has been criticized as improperly suited for deliberative step-by-step issue solving, integrating knowledge, and handling preparation. Finally, Nouvelle AI stands out in reactive and real-world robotics domains but has actually been slammed for difficulties in incorporating learning and understanding.
Hybrid AIs incorporating one or more of these approaches are currently seen as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have complete responses and stated that Al is for that reason difficult; we now see numerous of these very same areas undergoing continued research study and development resulting in increased capability, not impossibility. [100]
Artificial intelligence.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep learning
First-order reasoning
GOFAI
History of expert system
Inductive reasoning programs
Knowledge-based systems
Knowledge representation and thinking
Logic programs
Artificial intelligence
Model monitoring
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of synthetic intelligence
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy once stated: “This is AI, so we do not care if it’s emotionally real”. [4] McCarthy restated his position in 2006 at the AI@50 conference where he stated “Expert system is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 significant branches of expert system: one intended at producing intelligent behavior regardless of how it was achieved, and the other aimed at modeling smart processes found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the objective of their field as making ‘makers that fly so precisely like pigeons that they can fool even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep learning with symbolic artificial intelligence: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep learning with symbolic synthetic intelligence: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). “Learning representations by back-propagating errors”. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). “Backpropagation Applied to Handwritten Zip Code Recognition”. Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. “Thinking Fast and Slow in AI”. AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. “AAAI Presidential Address: The State of AI”. AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
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^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
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^ 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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^ Marcus 2020, p. 17.
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^ a b Selman 2022.
^ Garcez & Lamb 2020, p. 2.
^ Garcez et al. 2002.
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