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Symbolic Artificial Intelligence

In expert system, symbolic synthetic intelligence (likewise called classical artificial intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all techniques in artificial intelligence research that are based upon (human-readable) representations of issues, reasoning and search. [3] Symbolic AI utilized tools such as logic programming, production guidelines, semantic webs and frames, and it developed applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm resulted in critical ideas in search, symbolic shows languages, representatives, multi-agent systems, the semantic web, and the strengths and limitations of official knowledge and reasoning systems.

Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the mid-1990s. [4] Researchers in the 1960s and the 1970s were persuaded that symbolic approaches would ultimately succeed in developing a maker with synthetic general intelligence and considered this the supreme goal of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, resulted in unrealistic expectations and promises and was followed by the very first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) took place with the increase of expert systems, their guarantee of catching business expertise, and an enthusiastic corporate embrace. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later dissatisfaction. [8] Problems with difficulties in understanding acquisition, preserving big knowledge bases, and brittleness in managing out-of-domain issues arose. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists concentrated on resolving underlying issues in managing unpredictability and in knowledge acquisition. [10] Uncertainty was addressed with official approaches such as concealed Markov models, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic maker learning dealt with the understanding acquisition problem with contributions consisting of Version Space, Valiant’s PAC knowing, Quinlan’s ID3 decision-tree learning, case-based knowing, and inductive logic shows to learn relations. [13]

Neural networks, a subsymbolic approach, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work 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 consensus in the Al neighborhood was that the so-called neural-network technique was helpless. Systems simply didn’t work that well, compared to other approaches. … A transformation came in 2012, when a variety of individuals, consisting of a group of researchers dealing with Hinton, worked out a way to use the power of GPUs to immensely increase the power of neural networks.” [16] Over the next numerous years, deep learning had magnificent success in managing vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, given that 2020, as intrinsic troubles with bias, description, comprehensibility, and toughness became more obvious with deep learning approaches; an increasing number of AI scientists have required integrating the very best of both the symbolic and neural network techniques [17] [18] and attending to locations that both approaches have problem with, such as sensible thinking. [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 article on the History of AI, with dates and titles differing somewhat for increased clarity.

The very first AI summer season: irrational enthusiasm, 1948-1966

Success at early attempts in AI occurred in three main locations: synthetic neural networks, knowledge representation, and heuristic search, adding to high expectations. This area summarizes Kautz’s reprise of early AI history.

Approaches influenced by human or animal cognition or habits

Cybernetic approaches tried to replicate the feedback loops between animals and their environments. A robotic turtle, with sensing units, motors for driving and guiding, and 7 vacuum tubes for control, based upon a preprogrammed neural net, was developed as early as 1948. This work can be viewed as an early precursor to later work in neural networks, support knowing, and positioned 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 prove 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to create a domain-independent problem solver, GPS (General Problem Solver). GPS solved problems represented with formal operators by means of state-space search using means-ends analysis. [21]

During the 1960s, symbolic techniques accomplished fantastic success at simulating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Each one developed its own design of research study. Earlier techniques based on cybernetics or synthetic neural networks were abandoned or pushed into the background.

Herbert Simon and Allen Newell studied human problem-solving abilities and tried to formalize them, and their work laid the structures of the field of expert system, along with cognitive science, operations research study and management science. Their research group utilized the results of mental experiments to develop programs that simulated the techniques that individuals used to resolve problems. [22] [23] This custom, focused 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 type of knowledge that we will see later on used in expert systems, early symbolic AI researchers discovered another more basic application of knowledge. These were called heuristics, guidelines that direct a search in promising directions: “How can non-enumerative search be practical when the underlying problem is greatly hard? The technique promoted by Simon and Newell is to utilize heuristics: quick algorithms that might fail on some inputs or output suboptimal options.” [26] Another crucial advance was to discover a method to apply these heuristics that ensures an option will be found, if there is one, not enduring the periodic fallibility of heuristics: “The A * algorithm provided a basic frame for complete and ideal 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 at the cost of worst-case rapid time. [26]

Early deal with knowledge representation and reasoning

Early work covered both applications of formal reasoning highlighting first-order logic, together with attempts to manage common-sense thinking in a less official way.

Modeling official reasoning with logic: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not require to simulate the precise mechanisms of human idea, however might rather look for the essence of abstract thinking and analytical with reasoning, [27] despite whether individuals used the same algorithms. [a] His laboratory at Stanford (SAIL) focused on using formal reasoning to resolve a wide range of issues, including understanding representation, preparation and knowing. [31] Logic was also the focus of the work at the University of Edinburgh and somewhere else in Europe which resulted in the advancement of the programs language Prolog and the science of logic programs. [32] [33]

Modeling implicit common-sense understanding with frames and scripts: the “scruffies”

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] found that resolving difficult issues in vision and natural language processing required ad hoc solutions-they argued that no simple and basic concept (like reasoning) would capture all the elements of intelligent habits. Roger Schank described their “anti-logic” approaches as “shabby” (rather than the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “shabby” AI, since they need to be built by hand, one complicated concept at a time. [38] [39] [40]

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

The first AI winter was a shock:

During the very first AI summer season, numerous individuals thought that maker intelligence might be attained in simply a couple of years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research to utilize AI to fix problems of national security; in specific, to automate the translation of Russian to English for intelligence operations and to produce self-governing tanks for the battlefield. Researchers had actually begun to realize that accomplishing AI was going to be much more difficult than was supposed a years previously, but a mix of hubris and disingenuousness led numerous university and think-tank scientists to accept funding with pledges of deliverables that they ought to have understood they could not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been produced, and a remarkable backlash set in. New DARPA leadership canceled existing AI funding programs.

Beyond the United States, the most fertile ground for AI research was the United Kingdom. The AI winter season in the United Kingdom was stimulated on not a lot by disappointed military leaders as by rival academics who saw AI researchers as charlatans and a drain on research study funding. A professor of used mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research study in the nation. The report stated that all of the issues being dealt with in AI would be much better dealt with by researchers from other disciplines-such as used mathematics. The report likewise declared that AI successes on toy problems could never scale to real-world applications due to combinatorial explosion. [41]

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

Knowledge-based systems

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

Edward Feigenbaum stated:

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

( 1) The Knowledge Principle: if a program is to perform an intricate task well, it should know a lot about the world in which it runs.
( 2) A plausible extension of that principle, called the Breadth Hypothesis: there are two extra abilities necessary for intelligent habits in unexpected circumstances: falling back on significantly basic knowledge, and analogizing to particular but far-flung knowledge. [45]

Success with specialist systems

This “understanding transformation” caused the development and implementation of specialist systems (presented by Edward Feigenbaum), the first commercially effective form of AI software. [46] [47] [48]

Key specialist systems were:

DENDRAL, which discovered the structure of organic particles from their chemical formula and mass spectrometer readings.
MYCIN, which detected bacteremia – and suggested more lab tests, when required – by analyzing lab outcomes, patient history, and medical professional observations. “With about 450 guidelines, MYCIN had the ability to perform in addition to some professionals, and considerably better than junior doctors.” [49] INTERNIST and CADUCEUS which tackled internal medicine diagnosis. Internist attempted to capture the expertise of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS could ultimately diagnose approximately 1000 different illness.
– GUIDON, which demonstrated how a knowledge base constructed for expert problem resolving might be repurposed for mentor. [50] XCON, to set up VAX computers, a then laborious procedure that might take up to 90 days. XCON reduced the time to about 90 minutes. [9]
DENDRAL is thought about the first expert system that relied 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 the people at Stanford thinking about computer-based designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I desired an induction “sandbox”, he said, “I have just the one for you.” His lab was doing mass spectrometry of amino acids. The question was: how do you go from looking at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was good at heuristic search approaches, and he had an algorithm that was excellent at generating the chemical issue space.

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 experts in mass spectrometry. We began to contribute to their understanding, developing knowledge of engineering as we went along. These experiments totaled up to titrating DENDRAL more and more understanding. The more you did that, the smarter the program became. We had excellent outcomes.

The generalization was: in the knowledge lies the power. That was the big concept. In my career that is the huge, “Ah ha!,” and it wasn’t the way AI was being done formerly. Sounds easy, however it’s most likely AI’s most effective generalization. [51]

The other specialist systems discussed above came after DENDRAL. MYCIN exhibits the timeless specialist system architecture of a knowledge-base of rules combined to a symbolic thinking mechanism, including making use of certainty elements to deal with unpredictability. GUIDON shows how an explicit understanding base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a specific kind of knowledge-based application. Clancey revealed that it was not enough just to use MYCIN’s rules for direction, however that he likewise required to add rules for dialogue management and trainee modeling. [50] XCON is substantial because of the millions of dollars it conserved DEC, which activated the professional system boom where most all significant corporations in the US had expert systems groups, to record business proficiency, maintain it, and automate it:

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

Chess professional knowledge was encoded in Deep Blue. In 1996, this permitted IBM’s Deep Blue, with the help of symbolic AI, to win in a video game of chess against the world champion at that time, Garry Kasparov. [52]

Architecture of knowledge-based and professional systems

A key part of the system architecture for all specialist systems is the knowledge base, which shops facts and rules for problem-solving. [53] The most basic method for a skilled system knowledge base is simply a collection or network of production rules. Production guidelines connect symbols in a relationship similar to an If-Then declaration. The expert system processes the guidelines to make deductions and to identify what extra info it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools run in this style.

Expert systems can run in either a forward chaining – from evidence to conclusions – or backwards chaining – from objectives to needed data and requirements – manner. More innovative knowledge-based systems, such as Soar can also carry out meta-level reasoning, that is thinking about their own reasoning in regards to deciding how to resolve problems and keeping an eye on the success of problem-solving techniques.

Blackboard systems are a second sort of knowledge-based or professional system architecture. They design a community of professionals incrementally contributing, where they can, to resolve an issue. The problem is represented in numerous levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they recognize they can contribute. Potential analytical actions are represented on a program that is upgraded as the problem scenario changes. A controller decides how helpful each contribution is, and who must make the next analytical action. One example, the BB1 blackboard architecture [54] was initially motivated by studies of how human beings plan to perform several jobs in a trip. [55] A development of BB1 was to apply the very same chalkboard model to solving its control issue, i.e., its controller performed meta-level thinking with understanding sources that monitored how well a strategy or the analytical was continuing and might change from one method to another as conditions – such as objectives or times – altered. BB1 has been applied in numerous domains: building and construction site preparation, intelligent tutoring systems, and real-time patient tracking.

The second AI winter, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP makers particularly targeted to speed up the development of AI applications and research. In addition, a number of artificial intelligence business, such as Teknowledge and Inference Corporation, were selling skilled system shells, training, and consulting to corporations.

Unfortunately, the AI boom did not last and Kautz finest explains the 2nd AI winter season that followed:

Many factors can be offered for the arrival of the 2nd AI winter. The hardware business failed when far more economical basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the market. Many industrial deployments of specialist systems were stopped when they proved too costly to keep. Medical professional systems never captured on for numerous reasons: the trouble in keeping them approximately date; the obstacle for medical experts to discover how to use a bewildering range of various specialist systems for different medical conditions; and possibly most crucially, the hesitation of medical professionals to trust a computer-made medical diagnosis over their gut instinct, even for particular domains where the expert systems could outperform a typical physician. Venture capital cash deserted AI almost overnight. The world AI conference IJCAI hosted an enormous and lavish trade program and countless nonacademic participants in 1987 in Vancouver; the main AI conference the list below year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]

Adding in more extensive structures, 1993-2011

Uncertain reasoning

Both statistical approaches and extensions to reasoning were tried.

One analytical method, hidden Markov models, had actually already been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized using 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 approaches were used successfully in specialist systems. [57] Even later, in the 1990s, analytical relational learning, a method that combines likelihood with rational formulas, enabled likelihood to be integrated with first-order reasoning, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order reasoning to support were likewise tried. For instance, non-monotonic thinking might be used with fact upkeep systems. A reality maintenance system tracked assumptions and justifications for all inferences. It permitted inferences to be withdrawn when presumptions were discovered to be inaccurate or a contradiction was derived. Explanations could be attended to an inference by explaining which rules were used to create it and after that continuing through underlying inferences and rules all the method back to root presumptions. [58] Lofti Zadeh had introduced a various type of extension to handle the representation of ambiguity. For example, in deciding how “heavy” or “tall” a man is, there is frequently no clear “yes” or “no” response, and a predicate for heavy or high would rather return values between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy logic further offered a means for propagating mixes of these values through rational solutions. [59]

Machine knowing

Symbolic machine learning methods were investigated to attend to the knowledge acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test strategy to generate possible rule hypotheses to test against spectra. Domain and task understanding reduced the number of prospects tested to a manageable size. Feigenbaum explained Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s relating 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 utilized layers of understanding to guide and prune the search. That understanding got in there due to the fact that we interviewed people. But how did individuals get the knowledge? By looking 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 utilize to resolve individual hypothesis formation issues. We did it. We were even able to release new knowledge of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, in fact did it. We had the ability to do something that had been a dream: to have a computer system program created a brand-new and publishable piece of science. [51]

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

Advances were made in comprehending machine knowing theory, too. Tom Mitchell presented variation area knowing which describes knowing as an explore an area of hypotheses, with upper, more general, and lower, more specific, limits encompassing all practical hypotheses constant with the examples seen so far. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of artificial intelligence. [63]

Symbolic machine discovering encompassed more than finding out by example. E.g., John Anderson offered a cognitive model of human knowing where ability practice leads to a collection of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a trainee may find out to use “Supplementary angles are 2 angles whose procedures sum 180 degrees” as a number of various procedural rules. E.g., one guideline may state that if X and Y are additional and you understand X, then Y will be 180 – X. He called his approach “knowledge collection”. ACT-R has been used successfully to design aspects of human cognition, such as learning and retention. ACT-R is likewise utilized in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer system programming, and algebra to school kids. [64]

Inductive logic programs was another method to discovering that allowed logic programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) might synthesize Prolog programs from examples. [65] John R. Koza applied hereditary algorithms to program synthesis to create genetic shows, which he utilized to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more basic method to program synthesis that synthesizes a practical program in the course of proving its specifications to be right. [66]

As an option to reasoning, Roger Schank presented case-based reasoning (CBR). The CBR approach described in his book, Dynamic Memory, [67] focuses first on keeping in mind key analytical cases for future use and generalizing them where proper. When faced with a new issue, CBR retrieves the most similar previous case and adapts it to the specifics of the present issue. [68] Another option to reasoning, hereditary algorithms and genetic programming are based upon an evolutionary model of knowing, where sets of guidelines are encoded into populations, the guidelines govern the habits of people, and selection of the fittest prunes out sets of inappropriate guidelines over lots of generations. [69]

Symbolic device learning was used to discovering ideas, rules, heuristics, and analytical. Approaches, besides those above, consist of:

1. Learning from direction or advice-i.e., taking human guideline, positioned as advice, and identifying how to operationalize it in specific circumstances. For example, in a game of Hearts, finding out exactly how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter expert (SME) feedback throughout training. When problem-solving stops working, querying the specialist to either discover a new prototype for problem-solving or to learn a new description regarding exactly why one prototype is more appropriate than another. For instance, the program Protos found out to detect tinnitus cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing problem services based upon similar problems seen in the past, and then modifying their services to fit a new situation or domain. [72] [73] 4. Apprentice knowing systems-learning novel services to problems by observing human problem-solving. Domain understanding explains why novel solutions are right and how the option can be generalized. LEAP discovered how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., producing tasks to perform experiments and after that discovering from the results. Doug Lenat’s Eurisko, for example, learned heuristics to beat human players at the Traveller role-playing video game for 2 years in a row. [75] 6. Learning macro-operators-i.e., searching for useful macro-operators to be gained from sequences of standard problem-solving actions. Good macro-operators simplify problem-solving by enabling problems to be fixed at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the increase of deep knowing, the symbolic AI technique has actually been compared to deep knowing as complementary “… with parallels having actually been drawn lot of times by AI scientists in between Kahneman’s research on human reasoning and choice making – shown in his book Thinking, Fast and Slow – and the so-called “AI systems 1 and 2″, which would in principle be modelled by deep learning and symbolic thinking, respectively.” In this view, symbolic thinking is more apt for deliberative reasoning, planning, and description while deep learning is more apt for fast pattern acknowledgment in perceptual applications with noisy data. [17] [18]

Neuro-symbolic AI: integrating neural and symbolic techniques

Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI efficient in thinking, discovering, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the effective building and construction of rich computational cognitive designs requires the combination of sound symbolic reasoning and efficient (device) knowing models. Gary Marcus, similarly, argues that: “We can not construct abundant cognitive models in an appropriate, automated way without the triumvirate of hybrid architecture, abundant anticipation, and sophisticated techniques for thinking.”, [79] and in specific: “To develop a robust, knowledge-driven method to AI we must have the machinery of symbol-manipulation in our toolkit. Excessive of helpful understanding is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge reliably is the device of symbol control. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based on a need to resolve the two kinds of thinking gone over in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 components, System 1 and System 2. System 1 is quick, automated, user-friendly and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind utilized for pattern recognition while System 2 is far much better suited for planning, deduction, and deliberative thinking. In this view, deep knowing finest models the first type of thinking while symbolic reasoning finest models the 2nd kind and both are required.

Garcez and Lamb describe research in this area as being ongoing for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic thinking has actually been held every year considering that 2005, see http://www.neural-symbolic.org/ for information.

In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:

The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively little research community over the last twenty years and has actually yielded numerous significant results. Over the last decade, neural symbolic systems have actually been shown efficient in conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in response to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed 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 used to a variety of problems in the areas of bioinformatics, control engineering, software application confirmation and adaptation, visual intelligence, ontology knowing, and computer system games. [78]

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

– Symbolic Neural symbolic-is the current method of numerous neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic methods are utilized to call neural strategies. In this case the symbolic technique is Monte Carlo tree search and the neural techniques learn how to evaluate video game positions.
– Neural|Symbolic-uses a neural architecture to interpret affective data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to create or label training data that is consequently learned by a deep knowing model, e.g., to train a neural model for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to develop or label examples.
– Neural _ Symbolic -utilizes a neural web that is created 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] -allows a neural model to straight call a symbolic thinking engine, e.g., to carry out an action or assess a state.

Many key research questions stay, such as:

– What is the finest way to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible knowledge be discovered and reasoned about?
– How can abstract knowledge that is hard to encode realistically be handled?

Techniques and contributions

This area supplies an overview of strategies and contributions in a total context causing many other, more comprehensive posts in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history area.

AI shows languages

The crucial AI shows language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest programs language after FORTRAN and was developed in 1958 by John McCarthy. LISP offered the first read-eval-print loop to support quick program development. Compiled functions could be easily combined with translated functions. Program tracing, stepping, and breakpoints were likewise supplied, along with the capability to change values or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, implying that the compiler itself was originally written in LISP and after that ran interpretively to assemble the compiler code.

Other key developments originated by LISP that have actually infected other programs languages include:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

Programs were themselves information structures that other programs might operate on, enabling the easy meaning of higher-level languages.

In contrast to the US, in Europe the crucial AI programs language throughout that very same period was Prolog. Prolog provided an integrated store of truths and provisions that might be queried by a read-eval-print loop. The shop might act as an understanding base and the clauses could serve as rules or a limited type of logic. As a subset of first-order reasoning Prolog was based upon Horn clauses with a closed-world assumption-any truths not understood were considered false-and a special name assumption for primitive terms-e.g., the identifier barack_obama was thought about to refer to precisely one item. Backtracking and unification are integrated to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of logic programming, which was created by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more detail see the area on the origins of Prolog in the PLANNER article.

Prolog is also a kind of declarative programs. The reasoning clauses that explain programs are straight interpreted to run the programs defined. No explicit series of actions is required, as is the case with vital shows languages.

Japan championed Prolog for its Fifth Generation Project, planning to build unique hardware for high efficiency. Similarly, LISP devices were constructed to run LISP, however as the second AI boom turned to bust these business could not compete with brand-new workstations that might now run LISP or Prolog natively at comparable speeds. See the history section for more information.

Smalltalk was another influential AI programming language. For example, it introduced metaclasses and, together with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the present standard Lisp dialect. CLOS is a Lisp-based object-oriented system that permits numerous inheritance, in addition to incremental extensions to both classes and metaclasses, hence providing a run-time meta-object protocol. [88]

For other AI programming languages see this list of programs languages for expert system. Currently, Python, a multi-paradigm programming language, is the most popular programs language, partially due to its comprehensive bundle library that supports data science, natural language processing, and deep learning. Python consists of a read-eval-print loop, functional components such as higher-order functions, and object-oriented shows that consists of metaclasses.

Search

Search occurs in numerous kinds of issue fixing, including preparation, constraint complete satisfaction, and playing video 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 clause learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and reasoning

Multiple various approaches to represent understanding and after that reason with those representations have actually been investigated. Below is a fast overview of approaches to understanding representation and automated reasoning.

Knowledge representation

Semantic networks, conceptual graphs, frames, and reasoning are all approaches to modeling understanding such as domain understanding, analytical understanding, and the semantic significance of language. Ontologies design essential ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can likewise be considered as an ontology. YAGO incorporates WordNet as part of its ontology, to line up facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.

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

First-order logic is more basic than description logic. The automated theorem provers gone over below can prove theorems in first-order reasoning. Horn stipulation logic is more limited than first-order logic and is utilized in logic programming languages such as Prolog. Extensions to first-order reasoning consist of temporal reasoning, to manage time; epistemic reasoning, to factor about agent knowledge; modal reasoning, to manage possibility and necessity; and probabilistic logics to handle logic and probability 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 design checker. ACL2 is a theorem prover that can manage proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise called Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have a specific understanding base, typically of guidelines, to enhance reusability across domains by separating procedural code and domain knowledge. A different reasoning engine procedures rules and includes, deletes, or modifies a knowledge shop.

Forward chaining reasoning engines are the most common, and are seen in CLIPS and OPS5. Backward chaining takes place in Prolog, where a more limited sensible representation is utilized, Horn Clauses. Pattern-matching, specifically unification, is utilized in Prolog.

A more flexible sort of problem-solving happens when thinking about what to do next occurs, instead of simply selecting among the available actions. This kind of meta-level thinking is utilized in Soar and in the BB1 chalkboard architecture.

Cognitive architectures such as ACT-R may have additional abilities, such as the capability to assemble often used knowledge into higher-level chunks.

Commonsense reasoning

Marvin Minsky initially proposed frames as a way of translating common visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for typical routines, such as dining out. Cyc has tried to catch useful common-sense understanding and has “micro-theories” to manage particular kinds of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about ignorant physics, such as what happens when we warm a liquid in a pot on the stove. We anticipate it to heat and perhaps boil over, even though we might not know its temperature, its boiling point, or other information, such as atmospheric 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 minimal sort of inference than first-order reasoning. They can streamline sets of spatiotemporal restrictions, such as those for RCC or Temporal Algebra, in addition to resolving other type of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programming can be used to solve scheduling issues, for example with constraint dealing with rules (CHR).

Automated planning

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

Natural language processing

Natural language processing focuses on treating language as data to perform tasks such as identifying topics without always understanding the designated significance. Natural language understanding, on the other hand, constructs a significance representation and utilizes that for further processing, such as addressing concerns.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all elements of natural language processing long managed by symbolic AI, but considering that enhanced by deep knowing approaches. In symbolic AI, discourse representation theory and first-order logic have been utilized to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis likewise offered vector representations of files. In the latter case, vector parts are interpretable as principles called by Wikipedia posts.

New deep knowing approaches based upon Transformer designs have actually now eclipsed these earlier symbolic AI methods and achieved modern 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 parts is opaque.

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 basic textbook on expert system is organized to reflect agent architectures of increasing elegance. [91] The sophistication of agents differs from simple reactive agents, to those with a model of the world and automated planning abilities, perhaps a BDI representative, i.e., one with beliefs, desires, and intentions – or additionally a support learning model learned gradually to choose actions – approximately 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 includes several agents that communicate among themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The representatives require not all have the same internal architecture. Advantages of multi-agent systems consist of the capability to divide work among the agents and to increase fault tolerance when representatives are lost. Research issues include how agents reach consensus, dispersed issue solving, multi-agent learning, multi-agent planning, and distributed constraint optimization.

Controversies developed from at an early stage in symbolic AI, both within the field-e.g., in between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and in between those who embraced AI however rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mainly from thinkers, on intellectual grounds, but also from funding companies, especially during the 2 AI winters.

The Frame Problem: knowledge representation obstacles for first-order reasoning

Limitations were found in utilizing basic first-order logic to reason about vibrant domains. Problems were discovered both with concerns to specifying the prerequisites for an action to be successful and in providing axioms for what did not alter after an action was performed.

McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Expert System.” [93] A simple example takes place in “showing that a person individual might enter discussion with another”, as an axiom asserting “if a person has a telephone he still has it after searching for a number in the telephone book” would be needed for the reduction to prosper. Similar axioms would be needed for other domain actions to define what did not change.

A similar issue, called the Qualification Problem, occurs in trying to enumerate the preconditions for an action to be successful. A boundless number of pathological conditions can be imagined, e.g., a banana in a tailpipe might avoid an automobile from operating properly.

McCarthy’s method to repair the frame issue was circumscription, a type of non-monotonic reasoning where reductions could be made from actions that require just specify what would alter while not needing to explicitly specify whatever that would not change. Other non-monotonic reasonings offered truth upkeep systems that modified beliefs causing contradictions.

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

Similar to the problems in handling dynamic domains, common-sense reasoning is also hard to record in formal thinking. Examples of sensible reasoning include implicit thinking about how people believe or general knowledge of daily events, items, and living animals. This type of understanding is taken for approved and not considered as noteworthy. Common-sense reasoning is an open location of research and challenging both for symbolic systems (e.g., Cyc has tried to catch key parts of this knowledge 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 bicycle).

McCarthy saw his Advice Taker as having sensible, however his definition of common-sense was different than the one above. [94] He specified a program as having good sense “if it automatically deduces for itself an adequately broad class of instant repercussions of anything it is told and what it currently knows. “

Connectionist AI: philosophical obstacles and sociological disputes

Connectionist approaches 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 methods, such as Transformers, GANs, and other operate in deep knowing.

Three philosophical positions [96] have been described among connectionists:

1. Implementationism-where connectionist architectures execute the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is rejected absolutely, and connectionist architectures underlie intelligence and are fully enough to discuss it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are considered as complementary and both are needed for intelligence

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

The third and last position I want to analyze here is what I call the moderate connectionist view, a more diverse view of the existing argument in between connectionism and symbolic AI. One of the researchers 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 defended hybrid (partly symbolic, partially connectionist) systems. He claimed that (at least) 2 type 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 models. But on the other hand, for other cognitive procedures (such as serial, deductive reasoning, and generative sign adjustment processes) the symbolic paradigm uses appropriate models, and not only “approximations” (contrary to what extreme connectionists would declare). [97]

Gary Marcus has declared that the animus in the deep knowing neighborhood versus symbolic techniques now may be more sociological than philosophical:

To believe that we can merely desert symbol-manipulation is to suspend disbelief.

And yet, for the most part, that’s how most present AI earnings. Hinton and numerous others have attempted difficult to eradicate signs entirely. The deep learning hope-seemingly grounded not so much in science, but in a sort of historic grudge-is that intelligent habits will emerge purely from the confluence of enormous data and deep knowing. Where classical computers and software resolve tasks by defining sets of symbol-manipulating rules devoted to particular jobs, such as editing a line in a word processor or carrying out an estimation in a spreadsheet, neural networks generally try to fix jobs by analytical approximation and learning from examples.

According to Marcus, Geoffrey Hinton and his associates have been emphatically “anti-symbolic”:

When deep knowing reemerged in 2012, it was with a type of take-no-prisoners attitude that has defined the majority 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 symbols to aether, one of science’s greatest mistakes.

Ever since, his anti-symbolic campaign has actually just increased in strength. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep knowing in among science’s essential journals, Nature. It closed with a direct attack on symbol manipulation, calling not for reconciliation however for straight-out replacement. Later, Hinton informed a gathering of European Union leaders that investing any additional money in symbol-manipulating techniques was “a big mistake,” likening it to purchasing internal combustion engines in the period of electrical automobiles. [98]

Part of these disagreements may be due to unclear terminology:

Turing award winner Judea Pearl uses a review of artificial intelligence which, unfortunately, conflates the terms device knowing and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the undertone of the term tends to be that of professional systems dispossessed of any capability to learn. Making use of the terms requires information. Machine knowing is not restricted to association rule mining, c.f. the body of work on symbolic ML and relational learning (the distinctions to deep learning being the choice of representation, localist logical rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not practically production guidelines composed by hand. A correct meaning of AI issues knowledge representation and reasoning, autonomous multi-agent systems, preparation and argumentation, as well as learning. [99]

Situated robotics: the world as a model

Another review of symbolic AI is the embodied cognition technique:

The embodied cognition method claims that it makes no sense to consider the brain individually: cognition happens within a body, which is embedded in an environment. We need to study the system as a whole; the brain’s operating exploits consistencies in its environment, consisting of the rest of its body. Under the embodied cognition method, robotics, vision, and other sensing units end up being central, not peripheral. [100]

Rodney Brooks invented behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this approach, is deemed an alternative to both symbolic AI and connectionist AI. His technique declined representations, either symbolic or dispersed, as not just unnecessary, however as damaging. Instead, he created the subsumption architecture, a layered architecture for embodied representatives. Each layer attains a various purpose and must function in the real world. For example, the very first robotic he describes in Intelligence Without Representation, has three layers. The bottom layer translates sonar sensors to avoid things. The middle layer triggers the robot to roam around when there are no barriers. The top layer triggers the robot to go to more remote places for more exploration. Each layer can briefly prevent or suppress a lower-level layer. He slammed AI researchers for specifying AI issues for their systems, when: “There is no clean division in between understanding (abstraction) and thinking in the genuine world.” [101] He called his robots “Creatures” and each layer was “composed of a fixed-topology network of basic finite state devices.” [102] In the Nouvelle AI approach, “First, it is essential to test the Creatures we develop in the real world; i.e., in the same world that we human beings live in. It is disastrous to fall under the temptation of checking them in a simplified world initially, even with the best objectives of later transferring activity to an unsimplified world.” [103] His emphasis on real-world screening remained in contrast to “Early work in AI focused on video games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, but has been slammed by the other methods. Symbolic AI has actually been criticized as disembodied, responsible to the credentials issue, and poor in dealing with the affective issues where deep discovering excels. In turn, connectionist AI has actually been slammed as improperly suited for deliberative detailed issue resolving, incorporating understanding, and managing preparation. Finally, Nouvelle AI masters reactive and real-world robotics domains but has actually been criticized for problems in incorporating learning and knowledge.

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

Overall, Dreyfus saw locations where AI did not have complete answers and stated that Al is for that reason impossible; we now see a number of these very same locations undergoing ongoing research study and development resulting in increased capability, not impossibility. [100]

Expert system.
Automated planning and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep knowing
First-order reasoning
GOFAI
History of expert system
Inductive reasoning programming
Knowledge-based systems
Knowledge representation and reasoning
Logic programming
Machine knowing
Model checking
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of synthetic intelligence
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational learning
Symbolic mathematics
YAGO ontology
WordNet

Notes

^ McCarthy as soon as stated: “This is AI, so we do not care if it’s emotionally real”. [4] McCarthy reiterated his position in 2006 at the AI@50 conference where he said “Expert system is not, by meaning, simulation of human intelligence”. [28] Pamela McCorduck composes that there are “2 significant branches of expert system: one aimed at producing smart behavior regardless of how it was accomplished, and the other intended at modeling smart procedures found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig composed “Aeronautical engineering texts do not specify the goal of their field as making ‘makers that fly so precisely like pigeons that they can deceive even other pigeons.'” [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing 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.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Artificial Intelligence”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep learning with symbolic expert system: representing 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 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.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). “On the thresholds of understanding”. Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications: 291-300. doi:10.1109/ AIIA.1988.13308. S2CID 11778085.
^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). “An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
^ “The fascination with AI: what is expert system?”. IONOS Digitalguide. Retrieved 2021-12-02.
^ Hayes-Roth, Murray & Adelman 2015.
^ Hayes-Roth, Barbara (1985 ). “A chalkboard architecture for control”. Artificial Intelligence. 26 (3 ): 251-321. doi:10.1016/ 0004-3702( 85 )90063-3.
^ Hayes-Roth, Barbara (1980 ). Human Planning Processes. RAND.
^ Pearl 1988.
^ Spiegelhalter et al. 1993.
^ Russell & Norvig 2021, pp. 335-337.
^ Russell & Norvig 2021, p. 459.
^ Quinlan, J. Ross. “Chapter 15: Learning Efficient Classification Procedures and their Application to Chess End Games”. In Michalski, Carbonell & Mitchell (1983 ).
^ Quinlan, J. Ross (1992-10-15). C4.5: Programs for Machine Learning (1st ed.). San Mateo, Calif: Morgan Kaufmann. ISBN 978-1-55860-238-0.
^ Mitchell, Tom M.; Utgoff, Paul E.; Banerji, Ranan. “Chapter 6: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics”. In Michalski, Carbonell & Mitchell (1983 ).
^ Valiant, L. G. (1984-11-05). “A theory of the learnable”. Communications of the ACM. 27 (11 ): 1134-1142. doi:10.1145/ 1968.1972. ISSN 0001-0782. S2CID 12837541.
^ Koedinger, K. R.; Anderson, J. R.; Hadley, W. H.; Mark, M. A.; others (1997 ). “Intelligent tutoring goes to school in the huge city”. International Journal of Artificial Intelligence in Education (IJAIED). 8: 30-43. Retrieved 2012-08-18.
^ Shapiro, Ehud Y (1981 ). “The Model Inference System”. Proceedings of the 7th international joint conference on Expert system. IJCAI. Vol. 2. p. 1064.
^ Manna, Zohar; Waldinger, Richard (1980-01-01). “A Deductive Approach to Program Synthesis”. ACM Trans. Program. Lang. Syst. 2 (1 ): 90-121. doi:10.1145/ 357084.357090. S2CID 14770735.
^ Schank, Roger C. (1983-01-28). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge Cambridgeshire: New York: Cambridge University Press. ISBN 978-0-521-27029-8.
^ Hammond, Kristian J. (1989-04-11). Case-Based Planning: Viewing Planning as a Memory Task. Boston: Academic Press. ISBN 978-0-12-322060-8.
^ Koza, John R. (1992-12-11). Genetic Programming: On the Programming of Computers by Means of Natural Selection (1st ed.). Cambridge, Mass: A Bradford Book. ISBN 978-0-262-11170-6.
^ Mostow, David Jack. “Chapter 12: Machine Transformation of Advice into a Heuristic Search Procedure”. In Michalski, Carbonell & Mitchell (1983 ).
^ 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.
^ Carbonell, Jaime. “Chapter 14: Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition”. In Michalski, Carbonell & Mitchell (1986 ), pp. 371-392.
^ Mitchell, Tom; Mabadevan, Sridbar; Steinberg, Louis. “Chapter 10: LEAP: A Knowing Apprentice for VLSI Design”. In Kodratoff & Michalski (1990 ), pp. 271-289.
^ Lenat, Douglas. “Chapter 9: The Role of Heuristics in Learning by Discovery: Three Case Studies”. In Michalski, Carbonell & Mitchell (1983 ), pp. 243-306.
^ Korf, Richard E. (1985 ). Learning to Solve Problems by Searching for Macro-Operators. Research Notes in Artificial Intelligence. Pitman Publishing. ISBN 0-273-08690-1.
^ Valiant 2008.
^ a b Garcez et al. 2015.
^ Marcus 2020, p. 44.
^ 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.
^ Serafini, Luciano; Garcez, Artur d’Avila (2016 ), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
^ a b Garcez, Artur d’Avila; Lamb, Luis C.; Gabbay, Dov M. (2009 ). Neural-Symbolic Cognitive Reasoning (1st ed.). Berlin-Heidelberg: Springer. Bibcode:2009 nscr.book … D. doi:10.1007/ 978-3-540-73246-4. ISBN 978-3-540-73245-7. S2CID 14002173.
^ Kiczales, Gregor; Rivieres, Jim des; Bobrow, Daniel G. (1991-07-30). The Art of the Metaobject Protocol (1st ed.). Cambridge, Mass: The MIT Press. ISBN 978-0-262-61074-2.
^ Motik, Boris; Shearer, Rob; Horrocks, Ian (2009-10-28). “Hypertableau Reasoning for Description Logics”. Journal of Artificial Intelligence Research. 36: 165-228. arXiv:1401.3485. doi:10.1613/ jair.2811. ISSN 1076-9757. S2CID 190609.
^ Kuipers, Benjamin (1994 ). Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press. ISBN 978-0-262-51540-5.
^ Russell & Norvig 2021.
^ Leo de Penning, Artur S. d’Avila Garcez, Luís C. Lamb, John-Jules Ch. Meyer: “A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning.” IJCAI 2011: 1653-1658.
^ McCarthy & Hayes 1969.
^ McCarthy 1959.
^ Nilsson 1998, p. 7.
^ Olazaran 1993, pp. 411-416.
^ Olazaran 1993, pp. 415-416.
^ Marcus 2020, p. 20.
^ Garcez & Lamb 2020, p. 8.
^ a b Russell & Norvig 2021, p. 982.
^ Brooks 1991, p. 143.
^ Brooks 1991, p. 151.
^ Brooks 1991, p. 150.
^ Brooks 1991, p. 142.
References

Brooks, Rodney A. (1991 ). “Intelligence without representation”. Expert system. 47 (1 ): 139-159. doi:10.1016/ 0004-3702( 91 )90053-M. ISSN 0004-3702. S2CID 207507849. Retrieved 2022-09-13.
Clancey, William (1987 ). Knowledge-Based Tutoring: The GUIDON Program (MIT Press Series in Expert System) (Hardcover ed.).
Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
Dreyfus, Hubert L (1981 ). “From micro-worlds to understanding representation: AI at an impasse” (PDF). Mind Design. MIT Press, Cambridge, MA: 161-204.
Garcez, Artur S. d’Avila; Broda, Krysia; Gabbay, Dov M.; Gabbay, Augustus de Morgan Professor of Logic Dov M. (2002 ). Neural-Symbolic Learning Systems: Foundations and Applications. Springer Science & Business Media. ISBN 978-1-85233-512-0.
Garcez, Artur; Besold, Tarek; De Raedt, Luc; Földiák, Peter; Hitzler, Pascal; Icard, Thomas; Kühnberger, Kai-Uwe; Lamb, Luís; Miikkulainen, Risto; Silver, Daniel (2015 ). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. AAI Spring Symposium – Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches. Stanford, CA: AAAI Press. doi:10.13140/ 2.1.1779.4243.
Garcez, Artur d’Avila; Gori, Marco; Lamb, Luis C.; Serafini, Luciano; Spranger, Michael; Tran, Son N. (2019 ), Neural-Symbolic Computing: An Efficient Methodology for Principled Integration of Machine Learning and Reasoning, arXiv:1905.06088.
Garcez, Artur d’Avila; Lamb, Luis C. (2020 ), Neurosymbolic AI: The 3rd Wave, arXiv:2012.05876.
Haugeland, John (1985 ), Expert System: The Very Idea, Cambridge, Mass: MIT Press, ISBN 0-262-08153-9.
Hayes-Roth, Frederick; Murray, William; Adelman, Leonard (2015 ). “Expert systems”. AccessScience. doi:10.1036/ 1097-8542.248550.
Honavar, Vasant; Uhr, Leonard (1994 ). Symbolic Expert System, Connectionist Networks & Beyond (Technical report). Iowa State University Digital Repository, Computer Technology Technical Reports. 76. p. 6.
Honavar, Vasant (1995 ). Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy. The Springer International Series In Engineering and Computer Technology. Springer US. pp. 351-388. doi:10.1007/ 978-0-585-29599-2_11.
Howe, J. (November 1994). “Artificial Intelligence at Edinburgh University: a Viewpoint”. Archived from the initial on 15 May 2007. Retrieved 30 August 2007.
Kautz, Henry (2020-02-11). The Third AI Summer, Henry Kautz, AAAI 2020 Robert S. Engelmore Memorial Award Lecture. Retrieved 2022-07-06.
Kautz, Henry (2022 ). “The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture”. AI Magazine. 43 (1 ): 93-104. doi:10.1609/ aimag.v43i1.19122. ISSN 2371-9621. S2CID 248213051. Retrieved 2022-07-12.
Kodratoff, Yves; Michalski, Ryszard, eds. (1990 ). Machine Learning: an Artificial Intelligence Approach. Vol. III. San Mateo, Calif.: Morgan Kaufman. ISBN 0-934613-09-5. OCLC 893488404.
Kolata, G. (1982 ). “How can computer systems get sound judgment?”. Science. 217 (4566 ): 1237-1238. Bibcode:1982 Sci … 217.1237 K. doi:10.1126/ science.217.4566.1237. PMID 17837639.
Maker, Meg Houston (2006 ). “AI@50: AI Past, Present, Future”. Dartmouth College. Archived from the original on 3 January 2007. Retrieved 16 October 2008.
Marcus, Gary; Davis, Ernest (2019 ). Rebooting AI: Building Artificial Intelligence We Can Trust. New York: Pantheon Books. ISBN 9781524748258. OCLC 1083223029.
Marcus, Gary (2020 ), The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence, arXiv:2002.06177.
McCarthy, John (1959 ). PROGRAMS WITH SOUND JUDGMENT. Symposium on Mechanization of Thought Processes. NATIONAL PHYSICAL LABORATORY, TEDDINGTON, UK. p. 8.
McCarthy, John; Hayes, Patrick (1969 ). “Some Philosophical Problems From the Standpoint of Expert System”. Machine Intelligence 4. B. Meltzer, Donald Michie (eds.): 463-502.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1983 ). Machine Learning: an Expert System Approach. Vol. I. Palo Alto, Calif.: Tioga Publishing Company. ISBN 0-935382-05-4. OCLC 9262069.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1986 ). Artificial intelligence: an Artificial Intelligence Approach. Vol. II. Los Altos, Calif.: Morgan Kaufman. ISBN 0-934613-00-1.
Newell, Allen; Simon, Herbert A. (1972 ). Human Problem Solving (1st ed.). Englewood Cliffs, New Jersey: Prentice Hall. ISBN 0-13-445403-0.
Newell, Allen; Simon, H. A. (1976 ). “Computer Science as Empirical Inquiry: Symbols and Search”. Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ). Expert system: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the initial on 26 July 2020. Retrieved 18 November 2019.
Olazaran, Mikel (1993-01-01), “A Sociological History of the Neural Network Controversy”, in Yovits, Marshall C. (ed.), Advances in Computers Volume 37, vol. 37, Elsevier, pp. 335-425, doi:10.1016/ S0065-2458( 08 )60408-8, ISBN 9780120121373, recovered 2023-10-31.
Pearl, J. (1988 ). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California: Morgan Kaufmann. ISBN 978-1-55860-479-7. OCLC 249625842.
Russell, Stuart J.; Norvig, Peter (2021 ). Expert system: A Modern Approach (fourth ed.). Hoboken: Pearson. ISBN 978-0-13-461099-3. LCCN 20190474.
Rossi, Francesca (2022-07-06). “AAAI2022: Thinking Fast and Slow in AI (AAAI 2022 Invited Talk)”. Retrieved 2022-07-06.
Selman, Bart (2022-07-06). “AAAI2022: Presidential Address: The State of AI”. Retrieved 2022-07-06.
Serafini, Luciano; Garcez, Artur d’Avila (2016-07-07), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
Spiegelhalter, David J.; Dawid, A. Philip; Lauritzen, Steffen; Cowell, Robert G. (1993 ). “Bayesian analysis in professional systems”. Statistical Science. 8 (3 ).
Turing, A. M. (1950 ). “I.-Computing Machinery and Intelligence”. Mind. LIX (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 0026-4423. Retrieved 2022-09-14.
Valiant, Leslie G (2008 ). “Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence”. In Hariharan, R.; Mukund, M.; Vinay, V. (eds.). Foundations of Software Technology and Theoretical Computer Technology (Bangalore). pp. 415-422.
Xifan Yao; Jiajun Zhou; Jiangming Zhang; Claudio R. Boer (2017 ). From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Expert System and Further On.