Symbolic Expert System

In expert system, symbolic synthetic intelligence (also understood as classical artificial intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all approaches in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. [3] Symbolic AI utilized tools such as logic shows, production guidelines, semantic internet 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 preparation and scheduling systems. The Symbolic AI paradigm resulted in critical concepts in search, symbolic programs languages, representatives, multi-agent systems, the semantic web, and the strengths and constraints of formal understanding and thinking 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 convinced that symbolic techniques would ultimately succeed in creating a device with artificial general intelligence and considered this the ultimate goal of their field. [citation required] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, led to unrealistic expectations and promises and was followed by the first AI Winter as funding dried up. [5] [6] A 2nd boom (1969-1986) took place with the increase of professional systems, their pledge of recording business proficiency, and an enthusiastic business welcome. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed again by later frustration. [8] Problems with problems in understanding acquisition, keeping big understanding bases, and brittleness in dealing with out-of-domain problems developed. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI scientists focused on attending to underlying problems in handling uncertainty and in knowledge acquisition. [10] Uncertainty was addressed with official techniques such as concealed Markov models, Bayesian thinking, and analytical relational knowing. [11] [12] Symbolic device discovering addressed the knowledge 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 method, had actually 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 operate 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 consensus in the Al community was that the so-called neural-network method was helpless. Systems simply didn’t work that well, compared to other techniques. … A revolution was available in 2012, when a number of individuals, consisting of a team of researchers working with Hinton, exercised a way to use the power of GPUs to tremendously increase the power of neural networks.” [16] Over the next several years, deep knowing had incredible success in managing vision, speech acknowledgment, speech synthesis, image generation, and machine translation. However, considering that 2020, as intrinsic difficulties with predisposition, description, coherence, and robustness became more evident with deep knowing methods; an increasing variety of AI scientists have actually required combining the very best of both the symbolic and neural network methods [17] [18] and attending to locations that both approaches have trouble with, such as 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 a little for increased clearness.

The very first AI summertime: unreasonable exuberance, 1948-1966

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

Approaches influenced by human or animal cognition or habits

Cybernetic techniques tried to reproduce the feedback loops in between animals and their environments. A robotic turtle, with sensors, motors for driving and guiding, and seven vacuum tubes for control, based upon a preprogrammed neural web, was developed as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and positioned robotics. [20]

An important early symbolic AI program was the Logic theorist, written by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to show 38 elementary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to develop a domain-independent problem solver, GPS (General Problem Solver). GPS fixed issues represented with official operators by means of state-space search utilizing means-ends analysis. [21]

During the 1960s, symbolic approaches achieved great success at replicating intelligent behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was focused in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later on) University of Edinburgh. Every one established its own style of research study. Earlier methods based on cybernetics or synthetic neural networks were deserted or pressed into the background.

Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the structures of the field of artificial intelligence, in addition to cognitive science, operations research and management science. Their research study group utilized the results of mental experiments to develop programs that simulated the strategies that people used to resolve problems. [22] [23] This custom, centered at Carnegie Mellon University would ultimately culminate in the advancement of the Soar architecture in the center 1980s. [24] [25]

Heuristic search

In addition to the extremely specialized domain-specific sort of knowledge that we will see later on utilized in specialist systems, early symbolic AI scientists found another more general application of knowledge. These were called heuristics, rules of thumb that direct a search in promising instructions: “How can non-enumerative search be practical when the underlying problem is exponentially tough? The method promoted by Simon and Newell is to employ heuristics: fast algorithms that may fail on some inputs or output suboptimal solutions.” [26] Another important advance was to find a method to apply these heuristics that guarantees an option will be found, if there is one, not withstanding the occasional fallibility of heuristics: “The A * algorithm supplied a basic frame for total and ideal heuristically assisted search. A * is utilized as a subroutine within almost every AI algorithm today however is still no magic bullet; its guarantee of efficiency is purchased the expense of worst-case exponential time. [26]

Early work on understanding representation and thinking

Early work covered both applications of formal thinking emphasizing first-order logic, in addition to attempts to manage sensible reasoning in a less formal manner.

Modeling formal reasoning with logic: the “neats”

Unlike Simon and Newell, John McCarthy felt that makers did not need to mimic the precise systems of human idea, however could rather try to find the essence of abstract reasoning and analytical with logic, [27] despite whether individuals utilized the exact same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official reasoning to fix a wide range of issues, consisting of understanding representation, preparation and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and somewhere else in Europe which resulted in the development of the shows language Prolog and the science of reasoning 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] found that resolving hard problems in vision and natural language processing required ad hoc solutions-they argued that no easy and basic concept (like reasoning) would catch all the aspects of intelligent habits. Roger Schank described their “anti-logic” approaches as “scruffy” (instead of the “cool” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, considering that they should be developed by hand, one complex concept at a time. [38] [39] [40]

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

The first AI winter was a shock:

During the very first AI summer, lots of people thought that machine intelligence might be achieved in simply a few years. The Defense Advance Research Projects Agency (DARPA) released programs to support AI research study to use AI to resolve issues of nationwide security; in particular, to automate the translation of Russian to English for intelligence operations and to produce self-governing tanks for the battleground. Researchers had actually started to recognize that attaining AI was going to be much harder than was supposed a decade previously, however a mix of hubris and disingenuousness led lots of university and think-tank researchers to accept financing with guarantees of deliverables that they should have known they might not fulfill. By the mid-1960s neither beneficial natural language translation systems nor autonomous tanks had been created, and a significant reaction embeded in. New DARPA leadership canceled existing AI financing programs.

Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter in the United Kingdom was spurred on not so much by disappointed military leaders as by rival academics who saw AI scientists as charlatans and a drain on research financing. A professor of applied mathematics, Sir James Lighthill, was commissioned by Parliament to examine the state of AI research in the country. The report specified that all of the problems being worked on in AI would be much better handled by researchers from other disciplines-such as used mathematics. The report likewise claimed that AI successes on toy problems could never ever scale to real-world applications due to combinatorial surge. [41]

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

Knowledge-based systems

As limitations with weak, domain-independent techniques ended up being more and more evident, [42] researchers from all three traditions began to develop knowledge into AI applications. [43] [7] The understanding transformation was driven by the awareness that understanding underlies high-performance, domain-specific AI applications.

Edward Feigenbaum said:

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

( 1) The Knowledge Principle: if a program is to carry out a complicated job well, it needs to know a lot about the world in which it runs.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are two extra capabilities essential for smart behavior in unexpected situations: falling back on increasingly general knowledge, and analogizing to particular however distant knowledge. [45]

Success with expert systems

This “understanding revolution” caused the development and deployment of professional systems (presented by Edward Feigenbaum), the first commercially successful form 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 identified bacteremia – and recommended more lab tests, when needed – by translating laboratory results, patient history, and medical professional observations. “With about 450 rules, MYCIN was able to carry out as well as some professionals, and considerably better than junior doctors.” [49] INTERNIST and CADUCEUS which took on internal medication diagnosis. Internist attempted to catch the proficiency of the chairman of internal medication at the University of Pittsburgh School of Medicine while CADUCEUS could eventually detect approximately 1000 different illness.
– GUIDON, which showed how an understanding base developed for expert issue solving might be repurposed for mentor. [50] XCON, to configure VAX computer systems, a then tiresome process that might take up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is considered the very first expert system that count on knowledge-intensive analytical. It is described below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:

One of the individuals at Stanford thinking about computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I desired an induction “sandbox”, he stated, “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 started the DENDRAL Project: I was proficient at heuristic search approaches, and he had an algorithm that was proficient at creating the chemical issue area.

We did not have a grandiose vision. We worked bottom up. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control tablet, and also among the world’s most appreciated mass spectrometrists. Carl and his postdocs were first-rate professionals in mass spectrometry. We started to add to their understanding, inventing understanding of engineering as we went along. These experiments amounted to titrating DENDRAL increasingly more knowledge. The more you did that, the smarter the program ended up being. We had really 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 previously. Sounds basic, however it’s probably AI’s most effective generalization. [51]

The other professional systems discussed above came after DENDRAL. MYCIN exhibits the timeless professional system architecture of a knowledge-base of rules coupled to a symbolic thinking mechanism, including the usage of certainty aspects to handle uncertainty. GUIDON demonstrates how a specific understanding base can be repurposed for a 2nd application, tutoring, and is an example of a smart tutoring system, a specific type of knowledge-based application. Clancey showed that it was not sufficient simply to utilize MYCIN’s rules for direction, but that he also needed to include rules for dialogue management and student modeling. [50] XCON is considerable because of the countless dollars it conserved DEC, which triggered the professional system boom where most all significant corporations in the US had skilled systems groups, to capture corporate knowledge, maintain it, and automate it:

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

Chess specialist understanding was encoded in Deep Blue. In 1996, this enabled IBM’s Deep Blue, with the assistance of symbolic AI, to win in a video game of chess versus the world champ at that time, Garry Kasparov. [52]

Architecture of knowledge-based and skilled systems

An essential component of the system architecture for all professional systems is the understanding base, which stores facts and guidelines for analytical. [53] The simplest technique for a professional system knowledge base is merely a collection or network of production rules. Production rules link signs in a relationship similar to an If-Then statement. The professional system processes the rules to make deductions and to identify what additional info it requires, i.e. what questions to ask, using human-readable signs. For example, OPS5, CLIPS and their successors 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 data and requirements – manner. More innovative knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of choosing how to fix problems and monitoring the success of analytical methods.

Blackboard systems are a 2nd type of knowledge-based or professional system architecture. They design a neighborhood of specialists incrementally contributing, where they can, to fix a problem. The problem is represented in numerous levels of abstraction or alternate views. The professionals (understanding sources) volunteer their services whenever they recognize they can contribute. Potential analytical actions are represented on an agenda that is updated as the problem situation modifications. A controller decides how useful each contribution is, and who must make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was originally influenced by research studies of how people prepare to carry out several tasks in a trip. [55] A development of BB1 was to apply the very same blackboard design to solving its control problem, i.e., its controller carried out meta-level thinking with understanding sources that monitored how well a strategy or the analytical was continuing and could switch from one method to another as conditions – such as goals or times – changed. BB1 has actually been applied in numerous domains: construction site planning, smart tutoring systems, and real-time patient tracking.

The second AI winter season, 1988-1993

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

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

Many factors can be offered for the arrival of the second AI winter season. The hardware business failed when much more cost-effective basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the marketplace. Many industrial releases of professional systems were stopped when they showed too costly to keep. Medical specialist systems never caught on for several factors: the difficulty in keeping them as much as date; the challenge for medical professionals to find out how to use an overwelming range of different expert systems for various medical conditions; and perhaps most crucially, the hesitation of doctors to trust a computer-made diagnosis over their gut impulse, even for specific domains where the expert systems could exceed a typical doctor. Venture capital cash deserted AI practically over night. The world AI conference IJCAI hosted a massive and lavish trade convention and countless nonacademic participants in 1987 in Vancouver; the primary AI conference the following year, AAAI 1988 in St. Paul, was a small and strictly academic affair. [9]

Including more strenuous structures, 1993-2011

Uncertain reasoning

Both statistical techniques and extensions to reasoning were attempted.

One statistical method, hidden Markov designs, had currently been promoted in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a noise however efficient way of dealing with unsure reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were applied effectively in expert systems. [57] Even later on, in the 1990s, statistical relational learning, a technique that combines probability with rational solutions, permitted likelihood to be combined 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 fact upkeep systems. A fact maintenance system tracked presumptions and reasons for all reasonings. It enabled inferences to be withdrawn when presumptions were learnt to be incorrect or a contradiction was obtained. Explanations might be attended to an inference by discussing which guidelines were used to develop it and after that continuing through underlying inferences and rules all the method back to root assumptions. [58] Lofti Zadeh had actually introduced a various sort of extension to manage the representation of ambiguity. For example, in deciding how “heavy” or “tall” a male is, there is regularly no clear “yes” or “no” response, and a predicate for heavy or high would instead return values in between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy logic even more provided a way for propagating mixes of these worths through sensible solutions. [59]

Artificial intelligence

Symbolic maker discovering methods were investigated to address the knowledge acquisition bottleneck. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test method to produce possible guideline hypotheses to test versus spectra. Domain and task knowledge reduced the variety of candidates checked to a workable size. Feigenbaum explained Meta-DENDRAL as

… the culmination of my dream of the early to mid-1960s having to do with theory development. The conception was that you had a problem solver like DENDRAL that took some inputs and produced an output. In doing so, it used layers of understanding to steer and prune the search. That understanding acted since we spoke with people. But how did the individuals get the understanding? By taking a look at countless spectra. So we desired a program that would look at thousands of spectra and presume the understanding of mass spectrometry that DENDRAL might use to resolve specific hypothesis development issues. We did it. We were even able to publish brand-new understanding of mass spectrometry in the Journal of the American Chemical Society, giving credit just in a footnote that a program, Meta-DENDRAL, really did it. We had the ability to do something that had 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 approach of Meta-DENDRAL, Ross Quinlan developed a domain-independent technique to analytical classification, decision tree knowing, beginning first with ID3 [60] and then later extending its abilities to C4.5. [61] The choice trees created are glass box, interpretable classifiers, with human-interpretable category rules.

Advances were made in comprehending machine knowing theory, too. Tom Mitchell introduced version space learning which explains learning as an explore an area of hypotheses, with upper, more general, and lower, more particular, limits encompassing all viable hypotheses constant with the examples seen up until now. [62] More formally, Valiant introduced Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of machine learning. [63]

Symbolic device learning incorporated more than learning by example. E.g., John Anderson offered a cognitive design of human learning where skill 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 might discover to use “Supplementary angles are two angles whose procedures sum 180 degrees” as several various procedural rules. E.g., one rule may state that if X and Y are additional and you know X, then Y will be 180 – X. He called his approach “understanding compilation”. ACT-R has been utilized successfully to model aspects of human cognition, such as finding out and retention. ACT-R is also utilized in intelligent tutoring systems, called cognitive tutors, to successfully teach geometry, computer programming, and algebra to school children. [64]

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

As an option to logic, Roger Schank presented case-based thinking (CBR). The CBR technique laid out in his book, Dynamic Memory, [67] focuses initially on keeping in mind essential analytical cases for future usage and generalizing them where suitable. When confronted with a new issue, CBR recovers the most similar previous case and adapts it to the specifics of the existing issue. [68] Another option to logic, genetic algorithms and hereditary programming are based on an evolutionary model of knowing, where sets of guidelines are encoded into populations, the guidelines govern the habits of individuals, and choice of the fittest prunes out sets of inappropriate rules over many generations. [69]

Symbolic machine learning was used to finding out ideas, rules, heuristics, and problem-solving. Approaches, aside from those above, consist of:

1. Learning from direction or advice-i.e., taking human direction, postured as guidance, and identifying how to operationalize it in specific scenarios. For instance, in a video game of Hearts, discovering exactly how to play a hand to “avoid taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter specialist (SME) feedback during training. When problem-solving stops working, querying the expert to either find out a brand-new prototype for analytical or to find out a brand-new description as to precisely why one exemplar is more appropriate than another. For instance, the program Protos learned to diagnose ringing in the ears cases by communicating with an audiologist. [71] 3. Learning by analogy-constructing issue services based on similar problems seen in the past, and then customizing their options to fit a new scenario or domain. [72] [73] 4. Apprentice knowing systems-learning novel solutions to problems by observing human problem-solving. Domain understanding explains why unique solutions are correct and how the service can be generalized. LEAP found out how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., creating tasks to perform experiments and after that finding out from the results. Doug Lenat’s Eurisko, for example, discovered heuristics to beat human gamers at the Traveller role-playing video game for two years in a row. [75] 6. Learning macro-operators-i.e., searching for useful macro-operators to be learned from sequences of fundamental analytical actions. Good macro-operators simplify problem-solving by enabling problems to be resolved at a more abstract level. [76]
Deep learning and neuro-symbolic AI 2011-now

With the rise of deep learning, the symbolic AI approach has actually been compared to deep knowing as complementary “… with parallels having actually been drawn often times by AI researchers in between Kahneman’s research on human thinking and choice 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 reasoning, respectively.” In this view, symbolic reasoning is more apt for deliberative thinking, preparation, and explanation while deep learning is more apt for quick pattern recognition in perceptual applications with loud information. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic approaches

Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a way that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI efficient in reasoning, discovering, and cognitive modeling. As argued by Valiant [77] and lots of others, [78] the effective building of rich computational cognitive designs requires the combination of sound symbolic thinking and efficient (machine) knowing designs. Gary Marcus, similarly, argues that: “We can not build abundant cognitive designs in a sufficient, automated method without the set of three of hybrid architecture, rich anticipation, and sophisticated strategies for thinking.”, [79] and in specific: “To build a robust, knowledge-driven technique to AI we need to have the machinery of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and abstraction, and to date, the only equipment that we know of that can manipulate such abstract knowledge dependably is the device of symbol adjustment. ” [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based upon a need to deal with the two type of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having 2 elements, System 1 and System 2. System 1 is fast, automated, intuitive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern acknowledgment while System 2 is far much better matched for planning, reduction, and deliberative thinking. In this view, deep knowing finest designs the very first type of thinking while symbolic reasoning finest designs the second kind and both are required.

Garcez and Lamb explain research in this area as being ongoing for at least the previous twenty years, [83] dating from their 2002 book on neurosymbolic knowing systems. [84] A series of workshops on neuro-symbolic reasoning has 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 been pursued by a relatively small research neighborhood over the last 20 years and has yielded several substantial outcomes. Over the last years, neural symbolic systems have 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 shown capable of 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 applied to a number of problems in the areas of bioinformatics, control engineering, software verification and adjustment, visual intelligence, ontology learning, and video game. [78]

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

– Symbolic Neural symbolic-is the present technique of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of large language models. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are used to call neural techniques. In this case the symbolic method is Monte Carlo tree search and the neural techniques find out how to examine game positions.
– Neural|Symbolic-uses a neural architecture to translate perceptual information as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep knowing design, e.g., to train a neural design for symbolic calculation by utilizing a Macsyma-like symbolic mathematics system to develop or label examples.
– Neural _ Symbolic -utilizes 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 proof tree generated from knowledge base guidelines and terms. Logic Tensor Networks [86] also fall under this category.
– Neural [Symbolic] -permits a neural model to straight call a symbolic thinking engine, e.g., to carry out an action or evaluate 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 drawn out from them?
– How should sensible understanding be learned and reasoned about?
– How can abstract understanding that is hard to encode logically be dealt with?

Techniques and contributions

This section supplies an overview of techniques and contributions in an overall context causing many 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 crucial AI programs language in the US throughout the last symbolic AI boom period was LISP. LISP is the second earliest programming language after FORTRAN and was produced in 1958 by John McCarthy. LISP supplied the first read-eval-print loop to support fast program advancement. Compiled functions might be easily combined with translated functions. Program tracing, stepping, and breakpoints were likewise provided, together with the ability to alter worths or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, indicating that the compiler itself was initially composed in LISP and after that ran interpretively to compile the compiler code.

Other essential innovations pioneered by LISP that have actually spread to other programs languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the key AI shows language during that exact same period was Prolog. Prolog supplied an integrated shop of facts and clauses that might be queried by a read-eval-print loop. The shop might serve as a knowledge base and the stipulations might function as rules or a limited kind of reasoning. As a subset of first-order logic Prolog was based on Horn stipulations with a closed-world assumption-any facts not understood were considered false-and a distinct name presumption for primitive terms-e.g., the identifier barack_obama was considered to refer to precisely one item. Backtracking and unification are built-in to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the innovators of Prolog. Prolog is a type of logic programs, which was developed by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more detail see the area on the origins of Prolog in the PLANNER post.

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

Japan promoted Prolog for its Fifth Generation Project, planning to develop special hardware for high performance. Similarly, LISP devices were constructed to run LISP, however as the second AI boom turned to bust these business might not take on new workstations that might now run LISP or Prolog natively at equivalent speeds. See the history area for more information.

Smalltalk was another influential AI programs language. For example, it presented metaclasses and, in addition to 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 allows numerous inheritance, in addition to incremental extensions to both classes and metaclasses, hence offering a run-time meta-object procedure. [88]

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

Search

Search occurs in numerous sort of issue fixing, consisting of planning, restriction satisfaction, and playing video games such as checkers, chess, and go. The best known AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause knowing, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and reasoning

Multiple different techniques to represent understanding and then factor with those representations have actually been examined. Below is a quick overview of approaches to knowledge representation and automated reasoning.

Knowledge representation

Semantic networks, conceptual charts, frames, and logic are all techniques to modeling understanding such as domain knowledge, analytical understanding, and the semantic significance of language. Ontologies model crucial ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be deemed an ontology. YAGO incorporates WordNet as part of its ontology, to line up realities drawn out from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being utilized.

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

First-order reasoning is more basic than description logic. The automated theorem provers discussed below can prove theorems in first-order reasoning. Horn provision reasoning is more limited than first-order logic and is used in logic shows languages such as Prolog. Extensions to first-order logic include temporal logic, to manage time; epistemic logic, to factor about agent knowledge; modal logic, to manage possibility and necessity; and probabilistic logics to manage logic and likelihood together.

Automatic theorem showing

Examples of automated theorem provers for first-order logic are:

Prover9.
ACL2.
Vampire.

Prover9 can be utilized 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 known as Nqthm.

Reasoning in knowledge-based systems

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

Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining happens in Prolog, where a more minimal sensible representation is used, Horn Clauses. Pattern-matching, specifically unification, is utilized in Prolog.

A more flexible type of problem-solving happens when reasoning about what to do next occurs, rather than merely picking among the offered actions. This sort of meta-level reasoning is utilized in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R may have additional abilities, such as the capability to put together regularly utilized understanding into higher-level portions.

Commonsense thinking

Marvin Minsky initially proposed frames as a method of interpreting typical visual scenarios, such as a workplace, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has tried to catch useful sensible knowledge and has “micro-theories” to deal with specific type of domain-specific reasoning.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] approximates human thinking about naive physics, such as what takes place when we heat a liquid in a pot on the stove. We expect it to heat and perhaps boil over, despite the fact that we might not know its temperature level, its boiling point, or other details, such as air 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 solved with constraint solvers.

Constraints and constraint-based thinking

Constraint solvers carry out a more limited kind of reasoning than first-order reasoning. They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, together with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint logic programming can be used to fix scheduling problems, for example with restriction managing rules (CHR).

Automated preparation

The General Problem Solver (GPS) cast planning as problem-solving utilized means-ends analysis to produce strategies. STRIPS took a different method, viewing planning as theorem proving. Graphplan takes a least-commitment method to preparation, rather than sequentially picking actions from an initial state, working forwards, or a goal state if working in reverse. Satplan is a technique to preparing where a preparation problem is lowered to a Boolean satisfiability issue.

Natural language processing

Natural language processing focuses on dealing with language as information to perform tasks such as identifying topics without always understanding the desired significance. Natural language understanding, in contrast, constructs a meaning representation and utilizes that for additional processing, such as addressing questions.

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

New deep knowing approaches based on Transformer designs have actually now eclipsed these earlier symbolic AI methods and attained modern performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and files. Instead, they produce task-specific vectors where the significance of the vector elements is opaque.

Agents and multi-agent systems

Agents are autonomous systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig’s standard textbook on artificial intelligence is arranged to show representative architectures of increasing sophistication. [91] The elegance of representatives varies from easy reactive agents, to those with a model of the world and automated planning abilities, possibly a BDI agent, i.e., one with beliefs, desires, and objectives – or additionally a reinforcement learning design discovered with time to choose actions – as much as a mix of alternative architectures, such as a neuro-symbolic architecture [87] that consists of deep learning for understanding. [92]

In contrast, a multi-agent system includes multiple agents that communicate among themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The agents require not all have the exact same internal architecture. Advantages of multi-agent systems include the ability to divide work among the representatives and to increase fault tolerance when agents are lost. Research issues include how agents reach consensus, distributed problem fixing, multi-agent learning, multi-agent planning, and dispersed restraint optimization.

Controversies emerged from early in symbolic AI, both within the field-e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)- and between those who welcomed AI but declined symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mainly from thinkers, on intellectual premises, but also from funding firms, particularly during the two AI winter seasons.

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

Limitations were discovered in utilizing basic first-order logic to reason about dynamic domains. Problems were discovered both with regards to mentioning the preconditions for an action to prosper and in supplying axioms for what did not alter after an action was carried out.

McCarthy and Hayes presented the Frame Problem in 1969 in the paper, “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” [93] An easy example occurs in “proving that a person individual could get into discussion with another”, as an axiom asserting “if an individual has a telephone he still has it after searching for a number in the telephone book” would be needed for the reduction to prosper. Similar axioms would be required for other domain actions to define what did not change.

A similar problem, called the Qualification Problem, happens in attempting to enumerate the prerequisites for an action to prosper. An infinite variety of pathological conditions can be envisioned, e.g., a banana in a tailpipe could avoid a vehicle from running correctly.

McCarthy’s technique to fix the frame problem was circumscription, a type of non-monotonic reasoning where deductions might be made from actions that need only specify what would change while not having to clearly define everything that would not change. Other non-monotonic logics supplied fact maintenance systems that revised beliefs resulting in contradictions.

Other methods of managing more open-ended domains included probabilistic reasoning systems and maker learning to learn brand-new principles and guidelines. McCarthy’s Advice Taker can be considered as a motivation here, as it could integrate new knowledge offered by a human in the form of assertions or guidelines. For instance, speculative symbolic maker discovering systems checked out the capability to take high-level natural language suggestions and to analyze it into domain-specific actionable guidelines.

Similar to the problems in managing dynamic domains, sensible reasoning is also tough to record in official reasoning. Examples of sensible reasoning consist of implicit thinking about how people believe or general knowledge of daily occasions, objects, and living creatures. This type of knowledge is considered granted and not deemed noteworthy. Common-sense thinking is an open location of research study and challenging both for symbolic systems (e.g., Cyc has actually attempted to record key parts of this knowledge over more than a years) and neural systems (e.g., self-driving cars and trucks that do not know not to drive into cones or not to hit pedestrians strolling a bicycle).

McCarthy viewed his Advice Taker as having sensible, but his meaning of sensible was various than the one above. [94] He defined a program as having sound judgment “if it immediately deduces for itself an adequately large class of immediate repercussions of anything it is informed and what it already understands. “

Connectionist AI: philosophical challenges and sociological disputes

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

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

1. Implementationism-where connectionist architectures execute the capabilities for symbolic processing,
2. Radical connectionism-where symbolic processing is turned down absolutely, and connectionist architectures underlie intelligence and are fully sufficient to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are required for intelligence

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

The 3rd and last position I want to examine here is what I call the moderate connectionist view, a more eclectic view of the existing debate in between connectionism and symbolic AI. One of the scientists who has actually elaborated this position most explicitly is Andy Clark, a philosopher from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark safeguarded hybrid (partially symbolic, partly connectionist) systems. He declared that (at least) 2 sort of theories are needed in order to study and model cognition. On the one hand, for some information-processing tasks (such as pattern recognition) connectionism has advantages over symbolic models. But on the other hand, for other cognitive processes (such as serial, deductive reasoning, and generative symbol manipulation procedures) the symbolic paradigm offers sufficient models, and not only “approximations” (contrary to what extreme connectionists would declare). [97]

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

To think that we can simply desert symbol-manipulation is to suspend shock.

And yet, for the a lot of part, that’s how most current AI earnings. Hinton and many others have tried difficult to eliminate symbols completely. The deep learning hope-seemingly grounded not so much in science, however in a sort of historical grudge-is that smart habits will emerge simply from the confluence of enormous information and deep learning. Where classical computer systems and software application resolve jobs by defining sets of symbol-manipulating rules committed to specific jobs, such as editing a line in a word processor or carrying out a computation in a spreadsheet, neural networks typically attempt to solve jobs by statistical approximation and gaining from examples.

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

When deep learning reemerged in 2012, it was with a type of take-no-prisoners attitude that has identified the majority of the last years. By 2015, his hostility toward all things symbols had actually fully crystallized. He provided a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes.

…

Since then, his anti-symbolic project has only increased in intensity. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in among science’s essential journals, Nature. It closed with a direct attack on sign manipulation, calling not for reconciliation however for straight-out replacement. Later, Hinton told a gathering of European Union leaders that investing any further money in symbol-manipulating methods was “a huge error,” comparing it to buying internal combustion engines in the period of electric cars and trucks. [98]

Part of these disputes may be because of uncertain terminology:

Turing award winner Judea Pearl offers a critique of machine knowing which, sadly, conflates the terms device learning and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the connotation of the term tends to be that of professional systems dispossessed of any ability to find out. Making use of the terminology needs clarification. Artificial intelligence is not confined to association guideline mining, c.f. the body of work on symbolic ML and relational knowing (the distinctions to deep learning being the choice of representation, localist rational rather than dispersed, and the non-use of gradient-based knowing algorithms). Equally, symbolic AI is not just about production guidelines composed by hand. A proper meaning of AI issues knowledge representation and reasoning, autonomous multi-agent systems, planning and argumentation, as well as knowing. [99]

Situated robotics: the world as a design

Another review of symbolic AI is the embodied cognition technique:

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

Rodney Brooks developed behavior-based robotics, one technique to embodied cognition. Nouvelle AI, another name for this technique, is deemed an alternative to both symbolic AI and connectionist AI. His method turned down representations, either symbolic or distributed, as not only unneeded, however as damaging. Instead, he produced the subsumption architecture, a layered architecture for embodied representatives. Each layer attains a various purpose and should function in the real life. For instance, the very first robotic he describes in Intelligence Without Representation, has 3 layers. The bottom layer analyzes sonar sensors to avoid items. The middle layer causes the robot to roam around when there are no barriers. The top layer triggers the robot to go to more distant places for more expedition. Each layer can briefly inhibit or suppress a lower-level layer. He slammed AI scientists for defining AI problems for their systems, when: “There is no clean division in between understanding (abstraction) and reasoning in the genuine world.” [101] He called his robots “Creatures” and each layer was “made up of a fixed-topology network of easy limited state machines.” [102] In the Nouvelle AI method, “First, it is essential to evaluate the Creatures we integrate in the real life; i.e., in the very same world that we human beings inhabit. It is disastrous to fall into the temptation of evaluating them in a simplified world initially, even with the very best intents of later transferring activity to an unsimplified world.” [103] His emphasis on real-world screening was in contrast to “Early operate in AI focused on games, geometrical issues, symbolic algebra, theorem proving, and other formal systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.

Current views

Each approach-symbolic, connectionist, and behavior-based-has benefits, however has actually been criticized by the other approaches. Symbolic AI has actually been criticized as disembodied, responsible to the certification issue, and bad in handling the affective issues where deep learning excels. In turn, connectionist AI has been criticized as inadequately suited for deliberative step-by-step problem resolving, integrating knowledge, and managing planning. Finally, Nouvelle AI masters reactive and real-world robotics domains but has been criticized for troubles in integrating learning and understanding.

Hybrid AIs including one or more of these approaches are currently considered 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 difficult; we now see a lot of these very same locations going through continued research study and development leading to increased ability, not impossibility. [100]

Artificial intelligence.
Automated preparation and scheduling
Automated theorem proving
Belief revision
Case-based thinking
Cognitive architecture
Cognitive science
Connectionism
Constraint programming
Deep learning
First-order reasoning
GOFAI
History of synthetic intelligence
Inductive logic shows
Knowledge-based systems
Knowledge representation and thinking
Logic programs
Machine knowing
Model checking
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of expert system
Physical symbol 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 repeated his position in 2006 at the AI@50 conference where he stated “Expert system is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are “2 major branches of expert system: one targeted at producing intelligent habits no matter how it was accomplished, and the other focused on modeling intelligent processes found in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig wrote “Aeronautical engineering texts do not define the goal of their field as making ‘makers that fly so precisely like pigeons that they can fool even other pigeons.'” [30] Citations

^ Garnelo, Marta; Shanahan, Murray (October 2019). “Reconciling deep knowing with symbolic artificial intelligence: representing objects and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). “Logic-Based Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). “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. 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 limits 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.
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^ Mitchell, Tom M.; Utgoff, Paul E.; Banerji, Ranan. “Chapter 6: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics”. In Michalski, Carbonell & Mitchell (1983 ).
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^ Shapiro, Ehud Y (1981 ). “The Model Inference System”. Proceedings of the 7th international joint conference on Artificial intelligence. IJCAI. Vol. 2. p. 1064.
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^ Schank, Roger C. (1983-01-28). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge Cambridgeshire: New York City: Cambridge University Press. ISBN 978-0-521-27029-8.
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^ Bareiss, Ray; Porter, Bruce; Wier, Craig. “Chapter 4: Protos: An Exemplar-Based Learning Apprentice”. In Michalski, Carbonell & Mitchell (1986 ), pp. 112-139.
^ Carbonell, Jaime. “Chapter 5: Learning by Analogy: Formulating and Generalizing Plans from Past Experience”. In Michalski, Carbonell & Mitchell (1983 ), pp. 137-162.
^ 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.
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^ Garcez & Lamb 2020, p. 2.
^ Garcez et al. 2002.
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