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Symbolic Expert System
In synthetic intelligence, symbolic synthetic intelligence (also called classical synthetic intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all methods in expert system research study that are based on high-level symbolic (human-readable) representations of issues, reasoning and search. [3] Symbolic AI used tools such as logic shows, production guidelines, semantic webs and frames, and it developed applications such as knowledge-based systems (in particular, skilled systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm caused critical concepts in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and limitations of formal understanding and reasoning systems.
Symbolic AI was the dominant paradigm of AI research study from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic methods would eventually be successful in creating a device with synthetic general intelligence and considered this the supreme objective of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and promises and was followed by the very first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) accompanied the increase of expert systems, their of catching corporate knowledge, and an enthusiastic business accept. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later frustration. [8] Problems with problems in understanding acquisition, keeping large knowledge bases, and brittleness in dealing with out-of-domain issues developed. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on dealing with hidden issues in handling uncertainty and in knowledge acquisition. [10] Uncertainty was addressed with formal methods such as hidden Markov models, Bayesian reasoning, and statistical relational knowing. [11] [12] Symbolic device learning resolved 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 effective till about 2012: “Until Big Data ended up being prevalent, the general agreement in the Al neighborhood was that the so-called neural-network technique was helpless. Systems just didn’t work that well, compared to other methods. … A revolution can be found in 2012, when a variety of individuals, including a group of researchers working with Hinton, worked out a method to utilize the power of GPUs to enormously increase the power of neural networks.” [16] Over the next a number of years, deep knowing had spectacular success in handling vision, speech acknowledgment, speech synthesis, image generation, and maker translation. However, since 2020, as fundamental difficulties with bias, description, comprehensibility, and robustness became more evident with deep knowing techniques; an increasing number of AI researchers have actually called for combining the very best of both the symbolic and neural network methods [17] [18] and attending to locations that both approaches have trouble with, such as common-sense reasoning. [16]
A brief history of symbolic AI to today day follows 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 summer: unreasonable enthusiasm, 1948-1966
Success at early attempts in AI took place in three primary areas: synthetic neural networks, understanding representation, and heuristic search, adding to high expectations. This section summarizes Kautz’s reprise of early AI history.
Approaches inspired by human or animal cognition or behavior
Cybernetic techniques attempted to replicate the feedback loops 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 internet, was constructed as early as 1948. This work can be viewed as an early precursor to later work in neural networks, support knowing, and situated robotics. [20]
An essential 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 prove 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to develop a domain-independent issue solver, GPS (General Problem Solver). GPS resolved issues represented with formal operators via state-space search utilizing means-ends analysis. [21]
During the 1960s, symbolic techniques accomplished excellent success at mimicing intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Every one developed its own style of research study. Earlier methods based upon cybernetics or synthetic neural networks were deserted or pushed into the background.
Herbert Simon and Allen Newell studied human analytical abilities and tried to formalize them, and their work laid the structures of the field of expert system, in addition to cognitive science, operations research and management science. Their research study team utilized the outcomes of psychological experiments to develop programs that simulated the strategies that individuals used to resolve problems. [22] [23] This tradition, centered at Carnegie Mellon University would eventually culminate in the advancement of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the highly specialized domain-specific kinds of knowledge that we will see later on utilized in expert systems, early symbolic AI scientists discovered another more general application of understanding. These were called heuristics, guidelines of thumb that assist a search in appealing instructions: “How can non-enumerative search be practical when the underlying issue is exponentially hard? The technique advocated by Simon and Newell is to use heuristics: fast algorithms that may fail on some inputs or output suboptimal solutions.” [26] Another essential advance was to discover a method to apply these heuristics that ensures a service will be found, if there is one, not holding up against the periodic fallibility of heuristics: “The A * algorithm offered a general frame for complete and optimum heuristically assisted search. A * is used as a subroutine within practically every AI algorithm today however is still no magic bullet; its assurance of completeness is purchased the expense of worst-case exponential time. [26]
Early deal with knowledge representation and reasoning
Early work covered both applications of formal reasoning emphasizing first-order reasoning, in addition to attempts to manage sensible thinking in a less official way.
Modeling formal thinking with reasoning: the “neats”
Unlike Simon and Newell, John McCarthy felt that machines did not need to replicate the precise systems of human idea, but might rather attempt to find the essence of abstract reasoning and analytical with reasoning, [27] despite whether people utilized the very same algorithms. [a] His lab at Stanford (SAIL) focused on utilizing formal reasoning to fix a wide array of problems, consisting of understanding representation, preparation and knowing. [31] Logic was also the focus of the work at the University of Edinburgh and in other places in Europe which caused the advancement of the shows language Prolog and the science of reasoning shows. [32] [33]
Modeling implicit sensible knowledge with frames and scripts: the “scruffies”
Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that resolving difficult problems in vision and natural language processing needed ad hoc solutions-they argued that no easy and basic concept (like logic) would capture all the elements of intelligent behavior. Roger Schank explained their “anti-logic” techniques as “shabby” (as opposed to the “neat” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, because they need to be developed by hand, one complex idea at a time. [38] [39] [40]
The first AI winter season: crushed dreams, 1967-1977
The first AI winter was a shock:
During the first AI summertime, many people believed that device intelligence could be achieved in just a couple of years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to utilize AI to solve problems of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battleground. Researchers had actually begun to recognize that attaining AI was going to be much harder than was expected a decade previously, however a mix of hubris and disingenuousness led numerous university and think-tank scientists to accept funding with promises of deliverables that they need to have known they could not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been produced, and a significant backlash set in. New DARPA leadership canceled existing AI financing programs.
Beyond the United States, the most fertile ground for AI research study was the United Kingdom. The AI winter season in the United Kingdom was spurred on not so much by disappointed military leaders as by rival academics who saw AI scientists as charlatans and a drain on research financing. A teacher of used mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research study in the country. The report mentioned that all of the issues being worked on in AI would be better handled by scientists from other disciplines-such as applied mathematics. The report likewise claimed that AI successes on toy issues might never scale to real-world applications due to combinatorial surge. [41]
The 2nd AI summertime: knowledge is power, 1978-1987
Knowledge-based systems
As restrictions with weak, domain-independent methods ended up being a growing number of obvious, [42] scientists from all three traditions started to construct knowledge into AI applications. [43] [7] The knowledge revolution 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 basic and highly 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 know a good deal about the world in which it runs.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are two additional abilities essential for smart habits in unforeseen scenarios: drawing on progressively basic knowledge, and analogizing to specific however remote knowledge. [45]
Success with expert systems
This “knowledge revolution” led to the development and release of expert systems (introduced by Edward Feigenbaum), the very first commercially effective kind of AI software application. [46] [47] [48]
Key expert systems were:
DENDRAL, which discovered the structure of organic molecules from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and recommended further lab tests, when needed – by translating laboratory outcomes, patient history, and physician observations. “With about 450 guidelines, MYCIN was able to perform along with some professionals, and significantly better than junior medical professionals.” [49] INTERNIST and CADUCEUS which tackled internal medication diagnosis. Internist tried to catch the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately detect up to 1000 different illness.
– GUIDON, which demonstrated how a knowledge base built for professional problem resolving might be repurposed for mentor. [50] XCON, to set up VAX computer systems, a then laborious procedure that might take up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is considered the first specialist system that relied on knowledge-intensive analytical. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
Among the people at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I informed him I desired an induction “sandbox”, he said, “I have simply the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was great at heuristic search methods, and he had an algorithm that was proficient at generating the chemical issue area.
We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, creator of the chemical behind the contraceptive pill, and also among the world’s most respected mass spectrometrists. Carl and his postdocs were first-rate experts in mass spectrometry. We started to include to their knowledge, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL more and more knowledge. The more you did that, the smarter the program became. We had excellent results.
The generalization was: in the understanding lies the power. That was the big concept. In my career that is the substantial, “Ah ha!,” and it wasn’t the way AI was being done formerly. Sounds simple, however it’s most likely AI’s most effective generalization. [51]
The other expert systems mentioned above followed DENDRAL. MYCIN exemplifies the traditional expert system architecture of a knowledge-base of rules combined to a symbolic thinking mechanism, consisting of making use of certainty factors to handle uncertainty. GUIDON shows how an explicit understanding base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a specific kind of knowledge-based application. Clancey revealed that it was not adequate merely to utilize MYCIN’s rules for guideline, but that he likewise needed to add guidelines for dialogue management and student modeling. [50] XCON is considerable due to the fact that of the countless dollars it conserved DEC, which activated the professional system boom where most all significant corporations in the US had skilled systems groups, to record business expertise, maintain it, and automate it:
By 1988, DEC’s AI group had 40 expert systems released, with more on the method. DuPont had 100 in use and 500 in development. Nearly every significant U.S. corporation had its own Al group and was either using or examining specialist systems. [49]
Chess 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 video game of chess against the world champ at that time, Garry Kasparov. [52]
Architecture of knowledge-based and professional systems
A key element of the system architecture for all expert systems is the knowledge base, which stores realities and rules for problem-solving. [53] The simplest approach for a skilled system understanding base is merely a collection or network of production guidelines. Production rules link signs in a relationship comparable to an If-Then statement. The expert system processes the rules to make deductions and to identify what extra info it needs, i.e. what concerns to ask, utilizing human-readable symbols. For example, OPS5, CLIPS and their followers Jess and Drools operate in this style.
Expert systems can operate in either a forward chaining – from proof to conclusions – or backward chaining – from goals to required data and prerequisites – way. Advanced knowledge-based systems, such as Soar can also carry out meta-level thinking, that is reasoning about their own thinking in regards to deciding how to resolve issues and keeping track of the success of problem-solving strategies.
Blackboard systems are a 2nd sort of knowledge-based or expert system architecture. They model a neighborhood of experts incrementally contributing, where they can, to fix a problem. The problem is represented in numerous levels of abstraction or alternate views. The professionals (knowledge sources) offer their services whenever they acknowledge they can contribute. Potential problem-solving actions are represented on a program that is updated as the problem circumstance changes. A controller decides how useful each contribution is, and who need to make the next analytical action. One example, the BB1 blackboard architecture [54] was initially inspired by research studies of how humans plan to perform numerous tasks in a journey. [55] An innovation of BB1 was to use the same blackboard model to solving its control problem, i.e., its controller performed meta-level thinking with knowledge sources that monitored how well a strategy or the problem-solving was continuing and could switch from one strategy to another as conditions – such as goals or times – altered. BB1 has been applied in numerous domains: construction website planning, smart tutoring systems, and real-time client tracking.
The 2nd AI winter season, 1988-1993
At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines particularly targeted to accelerate the development of AI applications and research. In addition, several synthetic intelligence business, such as Teknowledge and Inference Corporation, were offering skilled system shells, training, and seeking advice from to corporations.
Unfortunately, the AI boom did not last and Kautz finest explains the 2nd AI winter season that followed:
Many reasons can be provided for the arrival of the second AI winter. The hardware business failed when a lot more cost-efficient general Unix workstations from Sun together with great compilers for LISP and Prolog came onto the market. Many industrial releases of specialist systems were stopped when they showed too pricey to preserve. Medical specialist systems never caught on for several factors: the difficulty in keeping them as much as date; the obstacle for medical professionals to learn how to use a bewildering variety of different expert systems for various medical conditions; and maybe most crucially, the hesitation of medical professionals to trust a computer-made medical diagnosis over their gut impulse, even for specific domains where the specialist systems could surpass a typical medical professional. Venture capital cash deserted AI practically over night. The world AI conference IJCAI hosted a huge and luxurious trade program and countless nonacademic participants in 1987 in Vancouver; the primary AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]
Including more rigorous structures, 1993-2011
Uncertain reasoning
Both statistical techniques and extensions to logic were tried.
One statistical technique, concealed Markov models, had actually already been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl promoted making use of Bayesian Networks as a noise but efficient method of dealing with uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were used effectively in professional systems. [57] Even later, in the 1990s, analytical relational learning, a method that integrates possibility with rational solutions, permitted possibility 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 attempted. For instance, non-monotonic reasoning could be utilized with reality upkeep systems. A fact maintenance system tracked assumptions and reasons for all reasonings. It allowed reasonings to be withdrawn when assumptions were discovered to be inaccurate or a contradiction was derived. Explanations could be attended to an inference by describing which rules were used to create it and after that continuing through underlying reasonings and rules all the method back to root presumptions. [58] Lofti Zadeh had introduced a different type of extension to handle the representation of ambiguity. For example, in deciding how “heavy” or “tall” a guy is, there is regularly no clear “yes” or “no” answer, and a predicate for heavy or tall would rather return values in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy reasoning further offered a way for propagating combinations of these worths through sensible formulas. [59]
Machine learning
Symbolic device discovering methods were investigated to deal with the knowledge acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test technique to produce possible guideline hypotheses to check against spectra. Domain and job knowledge lowered the variety of prospects checked to a workable size. Feigenbaum explained Meta-DENDRAL as
… the conclusion of my imagine the early to mid-1960s pertaining to 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 knowledge acted due to the fact that we interviewed people. But how did individuals get the knowledge? By taking a look at countless spectra. So we wanted a program that would take a look at countless spectra and presume the understanding of mass spectrometry that DENDRAL might use to fix private hypothesis formation problems. We did it. We were even able to release brand-new knowledge of mass spectrometry in the Journal of the American Chemical Society, giving credit just in a footnote that a program, Meta-DENDRAL, in fact did it. We were able to do something that had actually been a dream: to have a computer program come up with 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 technique to analytical category, choice tree learning, beginning first with ID3 [60] and then later on extending its abilities to C4.5. [61] The decision trees produced are glass box, interpretable classifiers, with human-interpretable classification guidelines.
Advances were made in understanding machine learning theory, too. Tom Mitchell introduced variation area knowing which describes learning as a search through an area of hypotheses, with upper, more general, and lower, more specific, boundaries including all practical hypotheses constant with the examples seen up until now. [62] More officially, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]
Symbolic device finding out included more than learning by example. E.g., John Anderson offered a cognitive design of human knowing 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 may find out to use “Supplementary angles are two angles whose measures sum 180 degrees” as several various procedural rules. E.g., one guideline may say that if X and Y are supplementary and you know X, then Y will be 180 – X. He called his method “knowledge compilation”. ACT-R has actually been used successfully to model elements of human cognition, such as discovering and retention. ACT-R is also utilized in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer shows, and algebra to school children. [64]
Inductive logic shows was another technique to learning that allowed logic programs to be synthesized from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might synthesize Prolog programs from examples. [65] John R. Koza used hereditary algorithms to program synthesis to create genetic programs, which he utilized to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger supplied a more general approach to program synthesis that manufactures a practical program in the course of showing its specs to be right. [66]
As an option to reasoning, Roger Schank presented case-based reasoning (CBR). The CBR method described in his book, Dynamic Memory, [67] focuses initially on keeping in mind key analytical cases for future usage and generalizing them where suitable. When confronted with a brand-new issue, CBR recovers the most comparable previous case and adjusts it to the specifics of the present issue. [68] Another option to reasoning, genetic algorithms and genetic programs are based upon an evolutionary design of knowing, where sets of guidelines are encoded into populations, the rules govern the behavior of people, and selection of the fittest prunes out sets of unsuitable rules over lots of generations. [69]
Symbolic machine learning was applied to discovering principles, rules, heuristics, and problem-solving. Approaches, besides those above, include:
1. Learning from instruction or advice-i.e., taking human direction, impersonated suggestions, and identifying how to operationalize it in specific scenarios. For example, in a game of Hearts, learning exactly how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving efficiency by accepting subject-matter professional (SME) feedback during training. When analytical stops working, querying the expert to either find out a new exemplar for problem-solving or to find out a new description as to precisely why one exemplar is more relevant than another. For instance, the program Protos found out to identify tinnitus cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing issue options based on comparable problems seen in the past, and then modifying their services to fit a new situation or domain. [72] [73] 4. Apprentice knowing systems-learning unique solutions to problems by observing human problem-solving. Domain knowledge discusses why unique options are correct and how the solution can be generalized. LEAP learned how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating tasks to perform experiments and then discovering from the outcomes. Doug Lenat’s Eurisko, for instance, discovered heuristics to beat human gamers at the Traveller role-playing video game for two years in a row. [75] 6. Learning macro-operators-i.e., looking for beneficial macro-operators to be found out from series of basic analytical actions. Good macro-operators simplify problem-solving by enabling problems to be solved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now
With the increase of deep knowing, the symbolic AI method has actually been compared to deep knowing as complementary “… with parallels having actually been drawn lot of times by AI scientists between Kahneman’s research study on human reasoning and decision making – shown in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in principle be designed 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 acknowledgment in perceptual applications with noisy data. [17] [18]
Neuro-symbolic AI: incorporating neural and symbolic methods
Neuro-symbolic AI efforts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI efficient in thinking, finding out, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the efficient building of rich computational cognitive designs demands the combination of sound symbolic reasoning and efficient (device) knowing designs. Gary Marcus, similarly, argues that: “We can not build rich cognitive designs in an appropriate, automatic way without the set of three of hybrid architecture, abundant prior understanding, and sophisticated methods for thinking.”, [79] and in specific: “To build a robust, knowledge-driven approach to AI we must have the machinery of symbol-manipulation in our toolkit. Excessive of helpful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only equipment that we understand of that can manipulate such abstract understanding 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 thinking gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two parts, System 1 and System 2. System 1 is fast, automated, user-friendly and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind utilized for pattern recognition while System 2 is far better fit for preparation, reduction, and deliberative thinking. In this view, deep learning best designs the very first sort of believing while symbolic thinking best designs the second kind and both are required.
Garcez and Lamb describe research study in this location as being continuous for at least the past twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking has been held every year given that 2005, see http://www.neural-symbolic.org/ for information.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The integration of the symbolic and connectionist paradigms of AI has been pursued by a reasonably little research study neighborhood over the last 2 decades and has actually yielded numerous considerable outcomes. Over the last decade, neural symbolic systems have been revealed capable of getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see likewise (Hinton, 1990). Neural networks were shown efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and pieces 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 problems in the areas of bioinformatics, control engineering, software verification and adaptation, visual intelligence, ontology knowing, and computer video games. [78]
Approaches for combination are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, along with some examples, follows:
– Symbolic Neural symbolic-is the present approach of lots of neural models in natural language processing, where words or subword tokens are both the ultimate input and output of big language designs. Examples include BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural techniques. In this case the symbolic method is Monte Carlo tree search and the neural strategies learn how to assess game positions.
– Neural|Symbolic-uses a neural architecture to analyze perceptual information as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to produce or identify training data that is consequently found out by a deep knowing model, e.g., to train a neural model for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to develop or label examples.
– Neural _ Symbolic -utilizes a neural net that is created from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree produced from knowledge base rules and terms. Logic Tensor Networks [86] likewise fall into this category.
– Neural [Symbolic] -enables a neural model to straight call a symbolic reasoning engine, e.g., to carry out an action or examine a state.
Many crucial research questions stay, such as:
– What is the best way to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and drawn out from them?
– How should sensible understanding be learned and reasoned about?
– How can abstract understanding that is difficult to encode logically be managed?
Techniques and contributions
This section supplies an overview of methods and contributions in a total context resulting in lots of other, more in-depth short articles in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history area.
AI shows languages
The key AI programming language in the US during the last symbolic AI boom duration was LISP. LISP is the second earliest programs language after FORTRAN and was produced in 1958 by John McCarthy. LISP supplied the very first read-eval-print loop to support rapid program development. Compiled functions might be freely combined with interpreted functions. Program tracing, stepping, and breakpoints were likewise supplied, together with the capability to alter worths or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, indicating that the compiler itself was originally composed in LISP and after that ran interpretively to put together the compiler code.
Other crucial developments pioneered by LISP that have spread out to other programs languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs could operate on, allowing the simple meaning of higher-level languages.
In contrast to the US, in Europe the key AI programs language during that exact same duration was Prolog. Prolog offered a built-in shop of truths and provisions that might be queried by a read-eval-print loop. The store could act as a knowledge base and the provisions might function as rules or a limited form of logic. As a subset of first-order reasoning Prolog was based on Horn stipulations with a closed-world assumption-any facts not understood were considered false-and a distinct name assumption for primitive terms-e.g., the identifier barack_obama was considered to refer to precisely one object. Backtracking and marriage are integrated to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a kind of logic programs, which was invented by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of techniques. For more information see the area on the origins of Prolog in the PLANNER article.
Prolog is likewise a kind of declarative shows. The reasoning clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as holds true with essential programs languages.
Japan championed Prolog for its Fifth Generation Project, intending to develop unique hardware for high efficiency. Similarly, LISP makers were built to run LISP, but as the 2nd AI boom turned to bust these companies might not contend with brand-new workstations that could now run LISP or Prolog natively at comparable speeds. See the history area for more information.
Smalltalk was another prominent AI programs 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 present basic Lisp dialect. CLOS is a Lisp-based object-oriented system that enables multiple inheritance, in addition to incremental extensions to both classes and metaclasses, hence offering a run-time meta-object protocol. [88]
For other AI programming languages see this list of programming languages for synthetic intelligence. Currently, Python, a multi-paradigm programs language, is the most popular shows language, partially due to its extensive bundle library that supports data science, natural language processing, and deep knowing. Python consists of a read-eval-print loop, practical elements such as higher-order functions, and object-oriented programming that consists of metaclasses.
Search
Search develops in many kinds of problem solving, including planning, constraint fulfillment, and playing video games such as checkers, chess, and go. The best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven stipulation learning, and the DPLL algorithm. For adversarial search when playing games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and reasoning
Multiple different methods to represent knowledge and after that factor with those representations have actually been examined. Below is a fast summary of methods to understanding representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and logic are all approaches to modeling knowledge such as domain knowledge, problem-solving understanding, and the semantic significance of language. Ontologies model key concepts and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology. YAGO includes WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being utilized.
Description logic is a logic for automated category of ontologies and for identifying irregular category data. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can check out in OWL ontologies and then inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order logic is more basic than description logic. The automated theorem provers gone over below can prove theorems in first-order reasoning. Horn clause logic is more limited than first-order reasoning and is utilized in reasoning shows languages such as Prolog. Extensions to first-order logic consist of temporal reasoning, to deal with time; epistemic logic, to factor about agent knowledge; modal logic, to manage possibility and necessity; and probabilistic logics to handle reasoning and probability together.
Automatic theorem showing
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be utilized in conjunction with the Mace4 design checker. ACL2 is a theorem prover that can deal with evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, also called Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have an explicit knowledge base, generally of rules, to enhance reusability across domains by separating procedural code and domain understanding. A different inference engine procedures guidelines and adds, deletes, or modifies an understanding shop.
Forward chaining reasoning engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is utilized, Horn Clauses. Pattern-matching, specifically unification, is used in Prolog.
A more versatile kind of analytical takes place when thinking about what to do next occurs, instead of simply picking among the offered actions. This type of meta-level thinking is used in Soar and in the BB1 chalkboard architecture.
Cognitive architectures such as ACT-R might have extra capabilities, such as the ability to put together often used knowledge into higher-level portions.
Commonsense reasoning
Marvin Minsky first proposed frames as a way of translating common visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has actually tried to catch beneficial common-sense knowledge and has “micro-theories” to deal with particular sort of domain-specific thinking.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human reasoning about naive physics, such as what occurs when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we might not know its temperature level, its boiling point, or other information, such as climatic pressure.
Similarly, Allen’s temporal period algebra is a simplification of thinking 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 thinking
Constraint solvers carry out a more limited kind of reasoning than first-order reasoning. They can streamline sets of spatiotemporal constraints, 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 programs can be utilized to fix scheduling problems, for example with constraint managing guidelines (CHR).
Automated preparation
The General Problem Solver (GPS) cast planning as analytical used means-ends analysis to produce strategies. STRIPS took a different method, viewing preparation as theorem proving. Graphplan takes a least-commitment technique to planning, rather than sequentially choosing actions from an initial state, working forwards, or a goal state if working backwards. Satplan is a technique to preparing where a planning problem is reduced to a Boolean satisfiability problem.
Natural language processing
Natural language processing focuses on treating language as data to perform jobs such as identifying subjects without necessarily comprehending the intended significance. Natural language understanding, on the other hand, constructs a significance representation and utilizes that for additional processing, such as answering concerns.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long managed by symbolic AI, but given that enhanced by deep knowing techniques. In symbolic AI, discourse representation theory and first-order logic have actually been utilized to represent sentence significances. Latent semantic analysis (LSA) and specific semantic analysis also offered vector representations of documents. In the latter case, vector elements are interpretable as ideas named by Wikipedia short articles.
New deep learning methods based upon Transformer designs have actually now eclipsed these earlier symbolic AI techniques and achieved advanced 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 meaning of the vector elements is nontransparent.
Agents and multi-agent systems
Agents are autonomous systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s basic book on expert system is organized to reflect representative architectures of increasing sophistication. [91] The sophistication of agents varies from basic reactive agents, to those with a model of the world and automated preparation abilities, perhaps a BDI agent, i.e., one with beliefs, desires, and objectives – or additionally a reinforcement discovering design found out with time to select actions – up to a combination of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep learning for understanding. [92]
On the other hand, a multi-agent system includes numerous agents that communicate among themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). The representatives require not all have the exact same internal architecture. Advantages of multi-agent systems consist of the capability to divide work among the representatives and to increase fault tolerance when representatives are lost. Research issues consist of how agents reach consensus, dispersed issue solving, multi-agent knowing, multi-agent preparation, and distributed restraint optimization.
Controversies emerged from at an early stage in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who welcomed AI however turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were mainly from theorists, on intellectual premises, however likewise from funding companies, particularly during the two AI winters.
The Frame Problem: knowledge representation challenges for first-order reasoning
Limitations were found in using basic first-order reasoning to reason about vibrant domains. Problems were found both with regards to enumerating the preconditions for an action to succeed and in supplying axioms for what did not alter after an action was carried out.
McCarthy and Hayes introduced the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] An easy example takes place in “proving that one individual might enter into conversation with another”, as an axiom asserting “if an individual has a telephone he still has it after looking up a number in the telephone directory” would be needed for the reduction to succeed. Similar axioms would be needed for other domain actions to specify what did not alter.
A comparable problem, called the Qualification Problem, occurs in attempting to mention the prerequisites for an action to prosper. A limitless variety of pathological conditions can be imagined, e.g., a banana in a tailpipe might prevent a car from operating properly.
McCarthy’s technique to fix the frame problem was circumscription, a sort of non-monotonic reasoning where reductions could be made from actions that require just define what would alter while not needing to explicitly define whatever that would not change. Other non-monotonic reasonings supplied fact maintenance systems that modified beliefs resulting in contradictions.
Other ways of handling more open-ended domains included probabilistic thinking systems and maker knowing to find out brand-new concepts and rules. McCarthy’s Advice Taker can be deemed an inspiration here, as it might incorporate new understanding supplied by a human in the type of assertions or guidelines. For instance, experimental symbolic device finding out systems explored the ability to take high-level natural language advice and to translate it into domain-specific actionable guidelines.
Similar to the issues in handling dynamic domains, sensible reasoning is also challenging to catch in official thinking. Examples of common-sense reasoning consist of implicit thinking about how people believe or general knowledge of daily events, objects, and living animals. This sort of knowledge is considered given and not seen as noteworthy. Common-sense reasoning is an open location of research and challenging both for symbolic systems (e.g., Cyc has actually attempted to record crucial parts of this understanding over more than a decade) and neural systems (e.g., self-driving vehicles that do not understand not to drive into cones or not to hit pedestrians walking a bike).
McCarthy saw his Advice Taker as having sensible, but his definition of sensible was various than the one above. [94] He defined a program as having sound judgment “if it immediately deduces for itself a sufficiently large class of immediate effects of anything it is told and what it already understands. “
Connectionist AI: philosophical challenges and sociological disputes
Connectionist techniques include earlier work on neural networks, [95] such as perceptrons; work in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s advanced approaches, such as Transformers, GANs, and other operate in deep learning.
Three philosophical positions [96] have been described among connectionists:
1. Implementationism-where connectionist architectures implement the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down totally, and connectionist architectures underlie intelligence and are totally sufficient to explain 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 debates within the neural network community, described the moderate connectionism view as basically compatible with present research study in neuro-symbolic hybrids:
The third and last position I wish to examine here is what I call the moderate connectionist view, a more diverse view of the present dispute in between connectionism and symbolic AI. Among the researchers who has elaborated this position most explicitly is Andy Clark, a thinker from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partly symbolic, partially connectionist) systems. He declared that (a minimum of) 2 sort of theories are required in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern recognition) connectionism has benefits over symbolic designs. But on the other hand, for other cognitive processes (such as serial, deductive thinking, and generative symbol adjustment processes) the symbolic paradigm offers adequate models, and not just “approximations” (contrary to what extreme connectionists would claim). [97]
Gary Marcus has actually claimed that the animus in the deep knowing community versus symbolic methods now may be more sociological than philosophical:
To believe that we can merely desert symbol-manipulation is to suspend shock.
And yet, for the many part, that’s how most present AI earnings. Hinton and many others have striven to banish symbols completely. The deep learning hope-seemingly grounded not so much in science, however in a sort of historic grudge-is that smart behavior will emerge simply from the confluence of enormous information and deep knowing. Where classical computers and software resolve jobs by defining sets of symbol-manipulating rules devoted to specific tasks, such as editing a line in a word processor or performing an estimation in a spreadsheet, neural networks normally try to solve tasks by analytical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his colleagues have been emphatically “anti-symbolic”:
When deep knowing reemerged in 2012, it was with a kind of take-no-prisoners mindset that has characterized the majority of the last years. By 2015, his hostility towards all things symbols had actually fully taken shape. He lectured at an AI workshop at Stanford comparing signs to aether, among science’s biggest errors.
…
Since then, his anti-symbolic project has only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in among 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 a gathering of European Union leaders that investing any more money in symbol-manipulating techniques was “a substantial error,” likening it to buying internal combustion engines in the period of electric automobiles. [98]
Part of these disputes might be due to uncertain terminology:
Turing award winner Judea Pearl provides a critique of device knowing which, regrettably, conflates the terms artificial intelligence and deep learning. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of professional systems dispossessed of any ability to find out. The use of the terminology is in requirement of information. Artificial intelligence is not confined to association rule mining, c.f. the body of work on symbolic ML and relational knowing (the differences to deep learning being the choice of representation, localist rational rather than distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not simply about production rules written by hand. A correct definition of AI issues knowledge representation and reasoning, self-governing multi-agent systems, planning and argumentation, along with learning. [99]
Situated robotics: the world as a design
Another review of symbolic AI is the embodied cognition approach:
The embodied cognition technique declares that it makes no sense to consider the brain individually: cognition takes location within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s operating exploits consistencies in its environment, consisting of the rest of its body. Under the embodied cognition technique, robotics, vision, and other sensing units end up being main, not peripheral. [100]
Rodney Brooks created behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this method, is viewed as an alternative to both symbolic AI and connectionist AI. His approach turned down representations, either symbolic or distributed, as not only unnecessary, however as destructive. Instead, he produced the subsumption architecture, a layered architecture for embodied representatives. Each layer attains a various function and needs to operate in the genuine world. For instance, the first robotic he explains in Intelligence Without Representation, has 3 layers. The bottom layer translates sonar sensing units to prevent objects. The middle layer triggers the robot to roam around when there are no challenges. The top layer causes the robotic to go to more far-off locations for more expedition. Each layer can momentarily inhibit or suppress a lower-level layer. He criticized AI scientists for specifying AI issues for their systems, when: “There is no clean department between understanding (abstraction) and reasoning in the real life.” [101] He called his robotics “Creatures” and each layer was “made up of a fixed-topology network of basic finite state machines.” [102] In the Nouvelle AI method, “First, it is extremely essential to test the Creatures we integrate in the real life; i.e., in the exact same world that we humans live in. It is devastating to fall under the temptation of evaluating them in a streamlined world first, even with the finest objectives of later moving activity to an unsimplified world.” [103] His focus on real-world testing was in contrast to “Early operate in AI focused on games, geometrical issues, symbolic algebra, theorem proving, and other formal 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, but has been slammed by the other techniques. Symbolic AI has been slammed as disembodied, accountable to the certification problem, and poor in managing the perceptual issues where deep discovering excels. In turn, connectionist AI has been slammed as improperly suited for deliberative step-by-step issue solving, including understanding, and dealing with preparation. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in integrating knowing and understanding.
Hybrid AIs integrating one or more of these approaches are currently considered as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have complete answers and said that Al is therefore difficult; we now see numerous of these same areas undergoing ongoing research and development causing increased ability, not impossibility. [100]
Artificial intelligence.
Automated planning and scheduling
Automated theorem proving
Belief modification
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programs
Deep knowing
First-order logic
GOFAI
History of expert system
Inductive reasoning programming
Knowledge-based systems
Knowledge representation and reasoning
Logic shows
Artificial intelligence
Model checking
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 knowing
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as said: “This is AI, so we do not care if it’s mentally real”. [4] McCarthy reiterated 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 composes that there are “2 major branches of synthetic intelligence: one focused on producing smart behavior no matter how it was achieved, and the other targeted at modeling intelligent procedures found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not define the goal of their field as making ‘machines that fly so exactly like pigeons that they can deceive even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep learning with symbolic expert system: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep learning with symbolic expert system: representing items and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). “Learning representations by back-propagating mistakes”. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). “Backpropagation Applied to Handwritten Zip Code Recognition”. Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. “Thinking Fast and Slow in AI”. AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. “AAAI Presidential Address: The State of AI“. AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). “On the limits of understanding”. Proceedings of the International Workshop on Expert System for Industrial Applications: 291-300. doi:10.1109/ AIIA.1988.13308. S2CID 11778085.
^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
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^ 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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^ Valiant 2008.
^ a b Garcez et al. 2015.
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^ Marcus 2020, p. 17.
^ a b Rossi 2022.
^ a b Selman 2022.
^ Garcez & Lamb 2020, p. 2.
^ Garcez et al. 2002.
^ Rocktäschel, Tim; Riedel, Sebastian (2016 ). “Learning Knowledge Base Inference with Neural Theorem Provers”. Proceedings of the 5th Workshop on Automated Knowledge Base Construction. San Diego, CA: Association for Computational Linguistics. pp. 45-50. doi:10.18653/ v1/W16 -1309. Retrieved 2022-08-06.
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