Overview

  • Founded Date July 17, 1910
  • Sectors Animation
  • Posted Jobs 0
  • Viewed 8

Company Description

Symbolic Artificial Intelligence

In expert system, symbolic expert system (also known as classical synthetic intelligence or logic-based expert system) [1] [2] is the term for the collection of all approaches in artificial intelligence research that are based upon high-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI used tools such as logic programming, production guidelines, semantic webs and frames, and it established applications such as knowledge-based systems (in specific, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to influential ideas in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and constraints of official understanding and reasoning systems.

Symbolic AI was the dominant paradigm of AI research from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were persuaded that symbolic techniques would eventually prosper in producing a device with artificial 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 impractical expectations and pledges and was followed by the very first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) accompanied the increase of specialist systems, their promise of catching business knowledge, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later frustration. [8] Problems with problems in knowledge acquisition, preserving big knowledge bases, and brittleness in dealing with out-of-domain issues occurred. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on addressing underlying issues in dealing with uncertainty and in understanding acquisition. [10] Uncertainty was attended to with official methods such as covert Markov designs, Bayesian thinking, and statistical relational knowing. [11] [12] Symbolic device discovering resolved the understanding acquisition problem with contributions including Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive logic programming to discover relations. [13]

Neural networks, a subsymbolic approach, had been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt’s perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and work in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not deemed successful till about 2012: “Until Big Data became prevalent, the basic agreement in the Al community was that the so-called neural-network technique was hopeless. Systems simply didn’t work that well, compared to other techniques. … A revolution can be found in 2012, when a number of individuals, including a group of researchers dealing with Hinton, exercised a way to utilize the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next several years, deep knowing had spectacular success in dealing with vision, speech acknowledgment, speech synthesis, image generation, and device translation. However, because 2020, as intrinsic troubles with predisposition, explanation, comprehensibility, and effectiveness ended up being more obvious with deep knowing techniques; an increasing variety of AI scientists have required combining the best of both the symbolic and neural network techniques [17] [18] and attending to areas that both techniques have trouble with, such as common-sense thinking. [16]

A short 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 short article on the History of AI, with dates and titles varying a little for increased clarity.

The first AI summer season: irrational spirit, 1948-1966

Success at early attempts in AI occurred in three main locations: artificial neural networks, understanding representation, and heuristic search, adding to high expectations. This section sums up Kautz’s reprise of early AI history.

Approaches motivated by human or animal cognition or behavior

Cybernetic techniques tried to duplicate the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and steering, and 7 vacuum tubes for control, based on a preprogrammed neural net, was constructed as early as 1948. This work can be viewed as an early precursor to later work in neural networks, reinforcement learning, and located robotics. [20]

An essential early symbolic AI program was the Logic theorist, written by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it had the ability to show 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to develop 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 methods achieved excellent success at simulating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own style of research. Earlier approaches based on cybernetics or synthetic neural networks were deserted or pushed into the background.

Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of expert system, as well as cognitive science, operations research and management science. Their research group utilized the results of mental experiments to establish programs that simulated the strategies that people utilized to solve problems. [22] [23] This tradition, focused at Carnegie Mellon University would ultimately culminate in the advancement of the Soar architecture in the middle 1980s. [24] [25]

Heuristic search

In addition to the highly specialized domain-specific type of understanding that we will see later utilized in expert systems, early symbolic AI researchers found another more basic application of understanding. These were called heuristics, guidelines that guide a search in promising directions: “How can non-enumerative search be useful when the underlying problem is significantly tough? The method promoted by Simon and Newell is to employ heuristics: fast algorithms that may stop working on some inputs or output suboptimal solutions.” [26] Another crucial advance was to find a way to use 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 provided a basic frame for complete and optimal heuristically assisted search. A * is utilized as a subroutine within almost every AI algorithm today but is still no magic bullet; its warranty of completeness is purchased the cost of worst-case rapid time. [26]

Early work on knowledge representation and reasoning

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

Modeling formal reasoning with reasoning: the “neats”

Unlike Simon and Newell, John McCarthy felt that devices did not need to imitate the precise mechanisms of human idea, however might instead attempt to discover the essence of abstract reasoning and problem-solving with logic, [27] no matter whether people used the exact same algorithms. [a] His laboratory at Stanford (SAIL) concentrated on using formal logic to solve a broad variety of issues, including knowledge representation, planning and knowing. [31] Logic was also the focus of the work at the University of Edinburgh and in other places in Europe which resulted in the advancement of the programs language Prolog and the science of logic programs. [32] [33]

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

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] discovered that resolving difficult problems in vision and natural language processing needed advertisement hoc solutions-they argued that no simple and general concept (like reasoning) would capture all the elements of smart behavior. Roger Schank explained their “anti-logic” methods as “shabby” (as opposed to the “neat” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, because they should be constructed by hand, one complex principle at a time. [38] [39] [40]

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

The first AI winter season was a shock:

During the first AI summertime, lots of people thought that device intelligence could be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to utilize AI to resolve problems of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to create self-governing tanks for the battlefield. Researchers had actually begun to understand that achieving AI was going to be much more difficult than was supposed a years previously, however a combination of hubris and disingenuousness led numerous university and think-tank researchers to accept funding with promises of deliverables that they ought to have understood they might not satisfy. By the mid-1960s neither helpful natural language translation systems nor autonomous tanks had been produced, and a significant backlash embeded in. New DARPA leadership canceled existing AI funding programs.

Outside of the United States, the most fertile ground for AI research was the UK. The AI winter in the UK was spurred on not a lot 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 assess the state of AI research in the nation. The report stated that all of the issues being dealt with in AI would be better handled by scientists from other disciplines-such as applied mathematics. The report likewise declared that AI successes on toy issues might never scale to real-world applications due to combinatorial surge. [41]

The second AI summer season: knowledge is power, 1978-1987

Knowledge-based systems

As restrictions with weak, domain-independent methods ended up being increasingly more apparent, [42] researchers from all three traditions started to build understanding into AI applications. [43] [7] The understanding transformation was driven by the awareness 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 requires both general and extremely domain-specific understanding. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:

( 1) The Knowledge Principle: if a program is to carry out a complicated task well, it needs to know a lot about the world in which it operates.
( 2) A possible extension of that principle, called the Breadth Hypothesis: there are two additional capabilities needed for intelligent habits in unforeseen scenarios: drawing on significantly general knowledge, and analogizing to particular but distant knowledge. [45]

Success with specialist systems

This “understanding revolution” led to the advancement and release of expert systems (introduced by Edward Feigenbaum), the first commercially successful kind of AI software application. [46] [47] [48]

Key expert systems were:

DENDRAL, which found the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and suggested additional lab tests, when required – by translating lab outcomes, client history, and physician observations. “With about 450 guidelines, MYCIN was able to perform as well as some experts, and substantially much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which tackled internal medication medical diagnosis. Internist tried to capture the know-how of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS might ultimately detect approximately 1000 different illness.
– GUIDON, which demonstrated how a knowledge base developed for professional issue resolving could be repurposed for mentor. [50] XCON, to configure VAX computer systems, a then laborious process that could take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that count on knowledge-intensive problem-solving. It is explained listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

Among the individuals at Stanford thinking about computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I told him I wanted an induction “sandbox”, he said, “I have just the one for you.” His lab was doing mass spectrometry of amino acids. The concern was: how do you go from taking a look at the spectrum of an amino acid to the chemical structure of the amino acid? That’s how we began the DENDRAL Project: I was good at heuristic search approaches, and he had an algorithm that was good at creating the chemical issue space.

We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, inventor of the chemical behind the contraceptive pill, and also among the world’s most appreciated mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We started to contribute to their knowledge, developing knowledge of engineering as we went along. These experiments totaled up to titrating DENDRAL a growing number of knowledge. The more you did that, the smarter the program ended up being. We had extremely great results.

The generalization was: in the knowledge lies the power. That was the huge idea. In my profession that is the huge, “Ah ha!,” and it wasn’t the method AI was being done formerly. Sounds easy, but it’s most likely AI’s most powerful generalization. [51]

The other professional systems pointed out above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of rules coupled to a symbolic thinking system, including using certainty aspects to deal with unpredictability. GUIDON demonstrates how an explicit knowledge base can be repurposed for a 2nd application, tutoring, and is an example of an intelligent tutoring system, a particular kind of knowledge-based application. Clancey showed that it was not adequate merely to utilize MYCIN’s guidelines for direction, however that he likewise needed to include rules for discussion management and trainee modeling. [50] XCON is substantial due to the fact that of the countless dollars it saved DEC, which set off the professional system boom where most all major corporations in the US had expert systems groups, to capture corporate proficiency, protect it, and automate it:

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

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

Architecture of knowledge-based and skilled systems

A key element of the system architecture for all specialist systems is the understanding base, which shops facts and guidelines for problem-solving. [53] The simplest method for a professional system understanding base is merely a collection or network of production guidelines. Production guidelines connect symbols in a relationship comparable to an If-Then declaration. The professional system processes the guidelines to make reductions and to determine what additional details it needs, i.e. what questions to ask, utilizing human-readable symbols. For example, OPS5, CLIPS and their followers Jess and Drools operate in this style.

Expert systems can run in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to required information and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also carry out meta-level thinking, that is reasoning about their own reasoning in terms of deciding how to solve issues and keeping track of the success of analytical techniques.

Blackboard systems are a 2nd sort of knowledge-based or skilled system architecture. They design a neighborhood of professionals incrementally contributing, where they can, to resolve a problem. The problem is represented in multiple levels of abstraction or alternate views. The specialists (knowledge sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on a program that is updated as the issue scenario changes. A controller decides how beneficial each contribution is, and who must make the next problem-solving action. One example, the BB1 blackboard architecture [54] was initially inspired by studies of how people prepare to perform multiple jobs in a trip. [55] A development of BB1 was to use the very same chalkboard design to solving its control issue, i.e., its controller performed meta-level reasoning with knowledge sources that kept track of how well a strategy or the analytical was proceeding and could change from one strategy to another as conditions – such as goals or times – changed. BB1 has actually been applied in several domains: building and construction website planning, intelligent tutoring systems, and real-time client tracking.

The 2nd AI winter, 1988-1993

At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were offering LISP makers specifically targeted to accelerate the advancement of AI applications and research study. In addition, numerous expert system companies, such as Teknowledge and Inference Corporation, were selling skilled system shells, training, and seeking advice from to corporations.

Unfortunately, the AI boom did not last and Kautz best describes the second AI winter that followed:

Many factors can be provided for the arrival of the second AI winter. The hardware business failed when a lot more affordable basic Unix workstations from Sun together with excellent compilers for LISP and Prolog came onto the market. Many industrial deployments of expert systems were terminated when they proved too expensive to preserve. Medical specialist systems never captured on for numerous reasons: the trouble in keeping them up to date; the challenge for medical specialists to find out how to use an overwelming variety of different expert systems for various medical conditions; and perhaps most crucially, the unwillingness of physicians to rely on a computer-made diagnosis over their gut instinct, even for specific domains where the specialist systems might outshine an average physician. Equity capital cash deserted AI almost overnight. The world AI conference IJCAI hosted a huge and lavish trade convention and thousands of nonacademic participants in 1987 in Vancouver; the main AI conference the following year, AAAI 1988 in St. Paul, was a little and strictly academic affair. [9]

Including more extensive structures, 1993-2011

Uncertain thinking

Both statistical approaches and extensions to reasoning were attempted.

One statistical technique, hidden Markov designs, had actually currently been promoted in the 1980s for speech recognition work. [11] Subsequently, in 1988, Judea Pearl popularized making use of Bayesian Networks as a sound but efficient method of managing uncertain thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian methods were applied successfully in specialist systems. [57] Even later, in the 1990s, analytical relational learning, a technique that combines probability with rational solutions, enabled probability 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 logic to support were likewise attempted. For instance, non-monotonic reasoning could be used with truth maintenance systems. A fact upkeep system tracked assumptions and validations for all inferences. It enabled inferences to be withdrawn when presumptions were discovered to be incorrect or a contradiction was derived. Explanations might be offered a reasoning by describing which rules were used to create it and after that continuing through underlying inferences and guidelines all the method back to root assumptions. [58] Lofti Zadeh had actually introduced a different kind of extension to handle the representation of ambiguity. For instance, in choosing how “heavy” or “tall” a man is, there is frequently no clear “yes” or “no” response, and a predicate for heavy or high would instead return values in between 0 and 1. Those worths represented to what degree the predicates held true. His fuzzy reasoning even more provided a means for propagating mixes of these values through sensible solutions. [59]

Machine learning

Symbolic maker learning approaches were investigated to address the knowledge acquisition traffic jam. Among the earliest is Meta-DENDRAL. Meta-DENDRAL used a generate-and-test strategy to produce possible rule hypotheses to check versus spectra. Domain and job understanding reduced the number of candidates checked to a manageable size. Feigenbaum described 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 steer and prune the search. That understanding acted because we talked to people. But how did individuals get the understanding? By looking at thousands of spectra. So we desired a program that would look at countless spectra and presume the knowledge of mass spectrometry that DENDRAL could use to resolve private hypothesis formation issues. We did it. We were even able to publish new understanding of mass spectrometry in the Journal of the American Chemical Society, offering credit only in a footnote that a program, Meta-DENDRAL, in fact did it. We had the ability to do something that had actually been a dream: to have a computer program come up with a new and publishable piece of science. [51]

In contrast to the knowledge-intensive technique of Meta-DENDRAL, Ross Quinlan invented a domain-independent method to analytical category, choice tree knowing, beginning initially with ID3 [60] and then later on extending its capabilities to C4.5. [61] The choice trees produced are glass box, interpretable classifiers, with human-interpretable category rules.

Advances were made in comprehending artificial intelligence theory, too. Tom Mitchell presented variation space knowing which describes learning as an explore an area of hypotheses, with upper, more basic, and lower, more particular, boundaries including all feasible hypotheses constant with the examples seen so far. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]

Symbolic machine finding out encompassed more than learning by example. E.g., John Anderson provided a cognitive model of human knowing where skill practice results in a compilation of rules from a declarative format to a procedural format with his ACT-R cognitive architecture. For example, a student might discover to use “Supplementary angles are two angles whose steps sum 180 degrees” as a number of different procedural guidelines. E.g., one guideline may say that if X and Y are extra and you know X, then Y will be 180 – X. He called his method “understanding compilation”. ACT-R has been used successfully to design aspects of human cognition, such as finding out and retention. ACT-R is likewise utilized in intelligent tutoring systems, called cognitive tutors, to effectively teach geometry, computer shows, and algebra to school children. [64]

Inductive reasoning programs was another technique to finding out that allowed reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could synthesize Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to develop genetic shows, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more general method to program synthesis that manufactures a functional program in the course of proving its requirements to be correct. [66]

As an option to logic, Roger Schank presented case-based thinking (CBR). The CBR approach outlined in his book, Dynamic Memory, [67] focuses first on keeping in mind essential problem-solving cases for future usage and generalizing them where proper. When confronted with a new problem, CBR recovers the most comparable previous case and adjusts it to the specifics of the current problem. [68] Another option to logic, hereditary algorithms and hereditary programming are based on an evolutionary model of learning, where sets of guidelines are encoded into populations, the guidelines govern the behavior of people, and selection of the fittest prunes out sets of unsuitable guidelines over many generations. [69]

Symbolic artificial intelligence was applied to learning principles, rules, heuristics, and analytical. Approaches, other than those above, include:

1. Learning from instruction or advice-i.e., taking human direction, posed as advice, and determining how to operationalize it in specific situations. For instance, in a game of Hearts, learning exactly how to play a hand to “prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter expert (SME) feedback during training. When problem-solving fails, querying the expert to either find out a new exemplar for problem-solving or to discover a new description regarding precisely why one exemplar is more appropriate than another. For instance, the program Protos learned to diagnose ringing in the ears cases by connecting with an audiologist. [71] 3. Learning by analogy-constructing issue solutions based upon similar issues seen in the past, and then modifying their solutions to fit a new situation or domain. [72] [73] 4. Apprentice knowing systems-learning unique services to issues by observing human analytical. Domain knowledge describes why novel solutions are right and how the service can be generalized. LEAP found out how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., producing jobs to perform experiments and then discovering from the outcomes. Doug Lenat’s Eurisko, for example, found out 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., searching for useful macro-operators to be gained from sequences of fundamental analytical actions. Good macro-operators simplify analytical by permitting problems to be fixed at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

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

Neuro-symbolic AI: integrating neural and symbolic techniques

Neuro-symbolic AI efforts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary fashion, in order to support robust AI capable of thinking, discovering, and cognitive modeling. As argued by Valiant [77] and many others, [78] the effective construction of abundant computational cognitive designs demands the mix of sound symbolic thinking and effective (device) knowing models. Gary Marcus, similarly, argues that: “We can not build abundant cognitive designs in an adequate, automated method without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated methods for thinking.”, [79] and in particular: “To construct a robust, knowledge-driven approach to AI we need to have the equipment of symbol-manipulation in our toolkit. Too much of helpful understanding is abstract to make do without tools that represent and manipulate abstraction, and to date, the only equipment that we understand of that can control such abstract understanding reliably is the device of symbol manipulation. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based on a requirement to address the two type of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having 2 parts, System 1 and System 2. System 1 is quickly, automated, intuitive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far better suited for preparation, deduction, and deliberative thinking. In this view, deep learning finest models the first sort of thinking while symbolic reasoning finest designs the 2nd kind and both are needed.

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

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

The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a fairly small research study neighborhood over the last twenty years and has actually yielded numerous considerable results. Over the last years, neural symbolic systems have been revealed efficient in getting rid of the so-called propositional fixation of neural networks, as McCarthy (1988) put it in action to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were shown efficient in representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and pieces of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have actually been used to a variety of issues in the areas of bioinformatics, control engineering, software confirmation and adjustment, visual intelligence, ontology learning, and computer system games. [78]

Approaches for combination are varied. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:

– Symbolic Neural symbolic-is the existing technique of numerous neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language designs. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic techniques are utilized to call neural methods. In this case the symbolic approach is Monte Carlo tree search and the neural strategies discover how to assess game positions.
– Neural|Symbolic-uses a neural architecture to interpret affective information as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to generate or identify training data that is subsequently discovered by a deep learning model, e.g., to train a neural design for symbolic computation by utilizing a Macsyma-like symbolic mathematics system to create or label examples.
– Neural _ Symbolic -uses a neural net that is created from symbolic guidelines. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR evidence tree produced from understanding base rules and terms. Logic Tensor Networks [86] also fall under this category.
– Neural [Symbolic] -allows a neural model to straight call a symbolic thinking engine, e.g., to carry out an action or examine a state.

Many key research concerns stay, such as:

– What is the very best way to incorporate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should common-sense understanding be discovered and reasoned about?
– How can abstract knowledge that is tough to encode rationally be dealt with?

Techniques and contributions

This section offers an introduction of methods and contributions in a total context resulting in numerous other, more detailed short articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered previously in the history section.

AI shows languages

The essential AI shows language in the US during the last symbolic AI boom period was LISP. LISP is the 2nd oldest shows language after FORTRAN and was developed in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program advancement. Compiled functions could be easily blended with analyzed functions. Program tracing, stepping, and breakpoints were likewise provided, along with the capability to alter values or functions and continue from breakpoints or mistakes. It had the very first self-hosting compiler, implying that the compiler itself was originally written in LISP and then ran interpretively to put together the compiler code.

Other crucial developments originated by LISP that have actually spread out to other programming languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the crucial AI programming language during that same duration was Prolog. Prolog offered an integrated shop of realities and provisions that could be queried by a read-eval-print loop. The shop could function as an understanding base and the stipulations could serve as guidelines or a restricted kind of reasoning. As a subset of first-order reasoning Prolog was based on Horn stipulations with a closed-world assumption-any realities not known were considered false-and a distinct name assumption for primitive terms-e.g., the identifier barack_obama was thought about to describe exactly one things. Backtracking and unification are integrated to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of reasoning programming, which was developed by Robert Kowalski. Its history was likewise influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more information see the section on the origins of Prolog in the PLANNER post.

Prolog is also a kind of declarative shows. The reasoning stipulations that explain programs are directly analyzed to run the programs defined. No specific series of actions is needed, as holds true with crucial programming languages.

Japan championed Prolog for its Fifth Generation Project, planning to build unique hardware for high efficiency. Similarly, LISP makers were constructed to run LISP, but as the second AI boom turned to bust these companies might not take on new workstations that could now run LISP or Prolog natively at similar speeds. See the history area for more information.

Smalltalk was another influential AI programs language. For example, it presented metaclasses and, along with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the current standard Lisp dialect. CLOS is a Lisp-based object-oriented system that allows multiple inheritance, in addition to incremental extensions to both classes and metaclasses, hence providing a run-time meta-object procedure. [88]

For other AI programs languages see this list of programs languages for expert system. Currently, Python, a multi-paradigm programming language, is the most popular programming language, partly due to its substantial bundle library that supports information science, natural language processing, and deep knowing. Python includes a read-eval-print loop, practical elements such as higher-order functions, and object-oriented programs that includes metaclasses.

Search

Search emerges in many type of issue solving, including preparation, restriction fulfillment, and playing games such as checkers, chess, and go. The finest known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause 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 approaches to represent knowledge and then factor with those representations have actually been investigated. Below is a fast introduction of techniques to understanding representation and automated thinking.

Knowledge representation

Semantic networks, conceptual graphs, frames, and logic are all techniques to modeling knowledge such as domain knowledge, analytical knowledge, and the semantic meaning of language. Ontologies model crucial 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 utilized for any domain while WordNet is a lexical resource that can also be deemed an ontology. YAGO integrates WordNet as part of its ontology, to align truths extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.

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

First-order logic is more basic than description logic. The automated theorem provers talked about below can show theorems in first-order reasoning. Horn provision logic is more restricted than first-order reasoning and is used in reasoning programming languages such as Prolog. Extensions to first-order reasoning include temporal logic, to handle time; epistemic logic, to factor about agent knowledge; modal reasoning, to deal with possibility and requirement; and probabilistic reasonings to deal with reasoning and likelihood together.

Automatic theorem proving

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 handle evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise referred to as Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have an explicit understanding base, usually of guidelines, to enhance reusability across domains by separating procedural code and domain understanding. A different inference engine procedures rules and adds, deletes, or customizes a knowledge shop.

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

A more versatile kind of problem-solving happens when reasoning about what to do next takes place, instead of simply picking among the readily available actions. This type of meta-level thinking is utilized in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R may have extra capabilities, such as the ability to put together frequently used understanding into higher-level portions.

Commonsense reasoning

Marvin Minsky initially proposed frames as a method of interpreting common visual situations, such as an office, and Roger Schank extended this concept to scripts for typical routines, such as dining out. Cyc has actually tried to catch useful common-sense understanding and has “micro-theories” to deal with specific kinds of domain-specific reasoning.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human reasoning about naive physics, such as what takes place when we heat a liquid in a pot on the range. We anticipate it to heat and potentially boil over, even though we might not know its temperature level, its boiling point, or other details, such as air pressure.

Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of thinking about spatial relationships. Both can be solved with restriction solvers.

Constraints and constraint-based reasoning

Constraint solvers carry out a more restricted kind of reasoning than first-order reasoning. They can streamline sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, along with fixing other sort of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning shows can be used to fix scheduling problems, for example with restraint dealing with rules (CHR).

Automated planning

The General Problem Solver (GPS) cast preparation as analytical used means-ends analysis to create strategies. STRIPS took a different method, viewing preparation as theorem proving. Graphplan takes a least-commitment approach to planning, rather than sequentially choosing actions from a preliminary state, working forwards, or an objective state if working in reverse. Satplan is a method to preparing where a preparation problem is decreased to a Boolean satisfiability issue.

Natural language processing

Natural language processing concentrates on treating language as information to perform tasks such as recognizing subjects without necessarily understanding the desired meaning. Natural language understanding, in contrast, constructs a significance representation and utilizes that for additional processing, such as responding to concerns.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all elements of natural language processing long dealt with by symbolic AI, but because improved by deep learning approaches. In symbolic AI, discourse representation theory and first-order logic have actually been utilized to represent sentence significances. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts called by Wikipedia short articles.

New deep knowing techniques based upon Transformer models have actually now eclipsed these earlier symbolic AI methods and obtained state-of-the-art efficiency in natural language processing. However, Transformer models are nontransparent and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector elements is opaque.

Agents and multi-agent systems

Agents are autonomous systems embedded in an environment they view and act on in some sense. Russell and Norvig’s standard book on expert system is organized to reflect representative architectures of increasing sophistication. [91] The sophistication of representatives differs from simple reactive agents, to those with a design of the world and automated preparation capabilities, potentially a BDI representative, i.e., one with beliefs, desires, and objectives – or additionally a support discovering model found out gradually to pick actions – approximately a combination of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep learning for perception. [92]

On the other hand, a multi-agent system includes multiple agents that interact among themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The representatives need not all have the same internal architecture. Advantages of multi-agent systems include the capability to divide work among the representatives and to increase fault tolerance when agents are lost. Research problems include how representatives reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and dispersed restraint optimization.

Controversies emerged from at an early stage in symbolic AI, both within the field-e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who accepted AI however turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from exterior of the field were mostly from theorists, on intellectual premises, but also from funding firms, particularly during the two AI winters.

The Frame Problem: understanding representation difficulties for first-order logic

Limitations were found in using simple first-order reasoning to reason about dynamic domains. Problems were found both with concerns to identifying the preconditions for an action to be successful and in offering axioms for what did not change 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] A basic example takes place in “proving that a person individual might enter conversation with another”, as an axiom asserting “if a person has a telephone he still has it after looking up a number in the telephone directory” would be required for the reduction to be successful. Similar axioms would be needed for other domain actions to specify what did not alter.

A comparable issue, called the Qualification Problem, happens in trying to identify the preconditions for an action to succeed. An infinite variety of pathological conditions can be envisioned, e.g., a banana in a tailpipe could prevent an automobile from running correctly.

McCarthy’s method to repair the frame issue was circumscription, a kind of non-monotonic logic where reductions could be made from actions that require just define what would alter while not needing to clearly define everything that would not alter. Other non-monotonic logics supplied reality maintenance systems that revised beliefs leading to contradictions.

Other methods of dealing with more open-ended domains consisted of probabilistic reasoning systems and maker learning to discover new ideas and guidelines. McCarthy’s Advice Taker can be deemed a motivation here, as it could integrate new knowledge supplied by a human in the type of assertions or rules. For example, experimental symbolic maker discovering systems explored the capability to take high-level natural language recommendations and to analyze it into domain-specific actionable guidelines.

Similar to the issues in dealing with vibrant domains, sensible thinking is likewise tough to capture in official thinking. Examples of sensible reasoning consist of implicit reasoning about how people think or general knowledge of daily events, items, and living animals. This sort of understanding is considered given and not seen as noteworthy. Common-sense thinking is an open location of research study and challenging both for symbolic systems (e.g., Cyc has tried to record key parts of this knowledge over more than a decade) and neural systems (e.g., self-driving automobiles that do not know not to drive into cones or not to strike pedestrians strolling a bike).

McCarthy viewed his Advice Taker as having common-sense, however his definition of sensible was different than the one above. [94] He specified a program as having good sense “if it instantly deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it currently knows. “

Connectionist AI: philosophical challenges and sociological conflicts

Connectionist methods include earlier deal with neural networks, [95] such as perceptrons; work in the mid to late 80s, such as Danny Hillis’s Connection Machine and Yann LeCun’s advances in convolutional neural networks; to today’s advanced approaches, such as Transformers, GANs, and other operate in deep knowing.

Three philosophical positions [96] have actually been outlined amongst connectionists:

1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined completely, and connectionist architectures underlie intelligence and are fully adequate to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are needed for intelligence

Olazaran, in his sociological history of the debates within the neural network community, described the moderate connectionism deem essentially suitable with present research in neuro-symbolic hybrids:

The 3rd and last position I would like to examine here is what I call the moderate connectionist view, a more diverse view of the current dispute between connectionism and symbolic AI. One of the scientists who has elaborated this position most explicitly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark defended hybrid (partially symbolic, partly connectionist) systems. He claimed that (a minimum of) 2 kinds of theories are required in order to study and design cognition. On the one hand, for some information-processing tasks (such as pattern acknowledgment) connectionism has advantages over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative sign control procedures) the symbolic paradigm provides sufficient models, and not just “approximations” (contrary to what extreme connectionists would claim). [97]

Gary Marcus has claimed that the animus in the deep learning neighborhood versus symbolic techniques now might be more sociological than philosophical:

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

And yet, for the a lot of part, that’s how most present AI earnings. Hinton and lots of 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 intelligent habits will emerge purely from the confluence of huge data and deep learning. Where classical computer systems and software solve jobs by specifying sets of symbol-manipulating rules dedicated to particular jobs, such as modifying a line in a word processor or performing a calculation in a spreadsheet, neural networks typically try to resolve tasks by analytical approximation and learning from examples.

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

When deep knowing reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized the majority of the last years. By 2015, his hostility towards all things symbols had completely crystallized. He lectured at an AI workshop at Stanford comparing signs to aether, among science’s biggest mistakes.

Since then, his anti-symbolic project has actually only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in among science’s crucial journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation however for outright replacement. Later, Hinton informed a gathering of European Union leaders that investing any more money in symbol-manipulating techniques was “a big error,” comparing it to investing in internal combustion engines in the age of electrical cars. [98]

Part of these conflicts may be due to unclear terms:

Turing award winner Judea Pearl uses a review of device learning which, unfortunately, conflates the terms maker learning and deep knowing. Similarly, when Geoffrey Hinton refers to symbolic AI, the connotation of the term tends to be that of specialist systems dispossessed of any ability to learn. Using the terminology needs explanation. Machine learning is not restricted to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep knowing being the option of representation, localist sensible rather than dispersed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not almost production guidelines composed by hand. A correct meaning of AI concerns understanding representation and reasoning, autonomous multi-agent systems, preparation and argumentation, in addition to knowing. [99]

Situated robotics: the world as a model

Another review of symbolic AI is the embodied cognition method:

The embodied cognition technique claims that it makes no sense to think about the brain separately: 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 regularities in its environment, including the rest of its body. Under the embodied cognition method, robotics, vision, and other sensors become main, not peripheral. [100]

Rodney Brooks created 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 approach rejected representations, either symbolic or distributed, as not only unnecessary, but as harmful. Instead, he developed the subsumption architecture, a layered architecture for embodied representatives. Each layer accomplishes a various purpose and should function in the real world. For example, the very first robotic he explains in Intelligence Without Representation, has 3 layers. The bottom layer translates sonar sensing units to avoid things. The middle layer triggers the robotic to roam around when there are no challenges. The leading layer causes the robotic to go to more remote places for additional expedition. Each layer can temporarily hinder or reduce a lower-level layer. He criticized AI scientists for specifying AI issues for their systems, when: “There is no clean division in between perception (abstraction) and reasoning in the genuine world.” [101] He called his robotics “Creatures” and each layer was “made up of a fixed-topology network of basic finite state makers.” [102] In the Nouvelle AI approach, “First, it is vitally important to check the Creatures we integrate in the real life; i.e., in the exact same world that we humans occupy. It is disastrous to fall under the temptation of checking them in a simplified world initially, even with the very best intents of later moving activity to an unsimplified world.” [103] His emphasis on real-world testing remained in contrast to “Early operate in AI concentrated on video games, geometrical issues, symbolic algebra, theorem proving, and other formal systems” [104] and the use of the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, however has been slammed by the other approaches. Symbolic AI has actually been criticized as disembodied, accountable to the certification problem, and poor in handling the perceptual issues where deep finding out excels. In turn, connectionist AI has been criticized as improperly suited for deliberative detailed problem resolving, incorporating knowledge, and dealing with preparation. Finally, Nouvelle AI masters reactive and real-world robotics domains however has been criticized for problems in integrating learning and understanding.

Hybrid AIs incorporating one or more of these techniques are currently seen as the path forward. [19] [81] [82] Russell and Norvig conclude that:

Overall, Dreyfus saw areas where AI did not have complete responses and said that Al is for that reason impossible; we now see a number of these very same areas going through ongoing research study and advancement resulting in increased ability, not impossibility. [100]

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

Notes

^ McCarthy as soon as stated: “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 “Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 major branches of synthetic intelligence: one aimed at producing intelligent behavior no matter how it was achieved, and the other targeted at modeling smart processes found in nature, especially human ones.”, [29] Stuart Russell and Peter Norvig wrote “Aeronautical engineering texts do not define the objective of their field as making ‘devices that fly so precisely like pigeons that they can trick even other pigeons.'” [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing with symbolic expert system: representing items and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “Reconciling deep knowing 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. 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 Postal 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 knowledge”. 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.
^ “The fascination with AI: what is synthetic intelligence?”. 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 Artificial Intelligence (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 big city”. International Journal of Expert System in Education (IJAIED). 8: 30-43. Retrieved 2012-08-18.
^ Shapiro, Ehud Y (1981 ). “The Model Inference System”. Proceedings of the 7th global joint conference on Artificial intelligence. 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 Learning 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 Expert System. 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 Expert System 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 a deadlock” (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: A Reliable 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 original 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 computers 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 Expert System We Can Trust. New York City: Pantheon Books. ISBN 9781524748258. OCLC 1083223029.
Marcus, Gary (2020 ), The Next Decade in AI: Four Steps Towards Robust Expert system, arXiv:2002.06177.
McCarthy, John (1959 ). PROGRAMS WITH GOOD SENSE. 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 (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1983 ). Machine Learning: an Artificial Intelligence 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 ). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original 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, obtained 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 (4th 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 specialist systems”. Statistical Science. 8 (3 ).
Turing, A. M. (1950 ). “I. 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 Expert System”. In Hariharan, R.; Mukund, M.; Vinay, V. (eds.). Foundations of Software Technology and Theoretical Computer Science (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.