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Symbolic Artificial Intelligence

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

Symbolic AI was the dominant paradigm of AI research study from the mid-1950s up until the mid-1990s. [4] Researchers in the 1960s and the 1970s were encouraged that symbolic approaches would ultimately prosper in creating a machine with synthetic basic intelligence and considered this the supreme objective of their field. [citation needed] An early boom, with early successes such as the Logic Theorist and Samuel’s Checkers Playing Program, caused unrealistic expectations and pledges and was followed by the very first AI Winter as moneying dried up. [5] [6] A second boom (1969-1986) took place with the increase of expert systems, their guarantee of catching business competence, and a passionate business accept. [7] [8] That boom, and some early successes, e.g., with XCON at DEC, was followed once again by later frustration. [8] Problems with troubles in knowledge acquisition, maintaining big understanding bases, and brittleness in handling out-of-domain issues developed. Another, second, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers concentrated on addressing hidden issues in managing unpredictability and in knowledge acquisition. [10] Uncertainty was resolved with official techniques such as surprise Markov models, Bayesian thinking, and statistical relational learning. [11] [12] Symbolic device finding out attended to the knowledge acquisition problem with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree learning, case-based learning, and inductive reasoning programming to find out relations. [13]

Neural networks, a subsymbolic technique, 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 seen as effective till about 2012: « Until Big Data became commonplace, the basic consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn’t work that well, compared to other methods. … A revolution can be found in 2012, when a number of people, including a group of scientists working with Hinton, worked out a method to use the power of GPUs to tremendously increase the power of neural networks. » [16] Over the next a number of years, deep knowing had incredible success in handling vision, speech acknowledgment, speech synthesis, image generation, and maker translation. However, because 2020, as inherent difficulties with bias, explanation, coherence, and toughness ended up being more obvious with deep learning methods; an increasing variety of AI researchers have required combining the very best of both the symbolic and neural network methods [17] [18] and dealing with areas that both approaches have problem with, such as common-sense thinking. [16]

A short history of symbolic AI to today day follows listed below. Period and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture [19] and the longer Wikipedia short article on the History of AI, with dates and titles differing slightly for increased clarity.

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

at early attempts in AI happened in 3 primary areas: artificial neural networks, knowledge representation, and heuristic search, adding to high expectations. This area summarizes Kautz’s reprise of early AI history.

Approaches inspired by human or animal cognition or behavior

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

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

During the 1960s, symbolic approaches attained fantastic success at simulating smart behavior in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research was concentrated in four organizations in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Every one established its own style of research. Earlier techniques based upon cybernetics or synthetic neural networks were abandoned or pressed into the background.

Herbert Simon and Allen Newell studied human analytical skills and tried to formalize them, and their work laid the structures of the field of expert system, along with cognitive science, operations research and management science. Their research group utilized the results of psychological experiments to develop programs that simulated the methods that people utilized to solve issues. [22] [23] This custom, centered at Carnegie Mellon University would eventually 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 on utilized in specialist systems, early symbolic AI researchers found another more general application of knowledge. These were called heuristics, guidelines that assist a search in appealing directions: « How can non-enumerative search be practical when the underlying problem is tremendously difficult? The technique advocated by Simon and Newell is to utilize heuristics: fast algorithms that may fail on some inputs or output suboptimal solutions. » [26] Another important advance was to discover a method to apply these heuristics that ensures an option will be found, if there is one, not holding up against the periodic fallibility of heuristics: « The A * algorithm supplied a basic frame for complete and optimum heuristically assisted search. A * is used as a subroutine within almost every AI algorithm today however is still no magic bullet; its assurance of completeness is purchased at the cost of worst-case exponential time. [26]

Early deal with knowledge representation and thinking

Early work covered both applications of official thinking highlighting first-order logic, in addition to attempts to deal with common-sense thinking in a less official manner.

Modeling official thinking with reasoning: the « neats »

Unlike Simon and Newell, John McCarthy felt that machines did not need to mimic the specific mechanisms of human thought, however might rather look for the essence of abstract thinking and problem-solving with reasoning, [27] no matter whether individuals used the same algorithms. [a] His laboratory at Stanford (SAIL) focused on using official logic to resolve a wide array of issues, consisting of knowledge representation, preparation and knowing. [31] Logic was likewise the focus of the work at the University of Edinburgh and in other places in Europe which resulted in the development of the shows language Prolog and the science of logic programs. [32] [33]

Modeling implicit sensible understanding with frames and scripts: the « scruffies »

Researchers at MIT (such as Marvin Minsky and Seymour Papert) [34] [35] [6] discovered that fixing difficult problems in vision and natural language processing needed ad hoc solutions-they argued that no simple and basic concept (like reasoning) would catch all the elements of intelligent behavior. Roger Schank described their « anti-logic » techniques as « shabby » (rather than the « neat » paradigms at CMU and Stanford). [36] [37] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of « scruffy » AI, given that they need to be constructed by hand, one complex concept at a time. [38] [39] [40]

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

The first AI winter was a shock:

During the first AI summer, lots of individuals believed that device intelligence might be attained in just a couple of years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research to use AI to resolve issues of nationwide security; in specific, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battlefield. Researchers had started to realize that attaining AI was going to be much more difficult than was expected a years earlier, but a combination 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 could not fulfill. By the mid-1960s neither helpful natural language translation systems nor autonomous tanks had been produced, and a remarkable reaction embeded in. New DARPA leadership canceled existing AI financing programs.

Outside of the United States, the most fertile ground for AI research was the United Kingdom. The AI winter season in the United Kingdom was stimulated on not a lot by disappointed military leaders as by competing academics who viewed AI scientists as charlatans and a drain on research funding. A teacher of applied mathematics, Sir James Lighthill, was commissioned by Parliament to evaluate the state of AI research study in the nation. The report mentioned that all of the issues being worked on in AI would be better managed by scientists from other disciplines-such as applied mathematics. The report also declared that AI successes on toy issues might never scale to real-world applications due to combinatorial explosion. [41]

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

Knowledge-based systems

As limitations with weak, domain-independent methods ended up being increasingly more obvious, [42] researchers from all three traditions started to build 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 stated:

– « In the knowledge lies the power. » [44]
to explain that high performance in a specific domain needs both general and extremely domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:

( 1) The Knowledge Principle: if a program is to perform a complex task well, it should know a good deal about the world in which it operates.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are 2 additional capabilities essential for smart behavior in unanticipated situations: drawing on progressively general understanding, and analogizing to specific but distant understanding. [45]

Success with expert systems

This « understanding transformation » caused the advancement and implementation of expert systems (introduced by Edward Feigenbaum), the very first commercially effective type of AI software. [46] [47] [48]

Key professional systems were:

DENDRAL, which discovered the structure of organic molecules from their chemical formula and mass spectrometer readings.
MYCIN, which identified bacteremia – and suggested further lab tests, when needed – by interpreting laboratory results, client history, and medical professional observations. « With about 450 guidelines, MYCIN was able to perform as well as some professionals, and significantly much better than junior medical professionals. » [49] INTERNIST and CADUCEUS which dealt with internal medication diagnosis. Internist attempted to catch the knowledge of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could ultimately diagnose up to 1000 different illness.
– GUIDON, which revealed how an understanding base constructed for professional issue solving could be repurposed for teaching. [50] XCON, to set up VAX computer systems, a then laborious process that could take up to 90 days. XCON decreased the time to about 90 minutes. [9]
DENDRAL is considered the first expert system that depend on knowledge-intensive problem-solving. It is described listed 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 designs of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I informed him I wanted an induction « sandbox », he stated, « I have simply the one for you. » His laboratory was doing mass spectrometry of amino acids. The question 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 excellent at heuristic search techniques, and he had an algorithm that was proficient at generating 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 first-rate experts in mass spectrometry. We started to include to their understanding, inventing understanding of engineering as we went along. These experiments totaled up to titrating DENDRAL more and more understanding. The more you did that, the smarter the program became. We had really great outcomes.

The generalization was: in the knowledge lies the power. That was the huge idea. In my career that is the big, « Ah ha!, » and it wasn’t the method AI was being done formerly. Sounds basic, but it’s probably AI’s most effective generalization. [51]

The other professional systems pointed out above came after DENDRAL. MYCIN exhibits the classic specialist system architecture of a knowledge-base of rules paired to a symbolic reasoning system, including making use of certainty elements to handle unpredictability. GUIDON demonstrates how an explicit understanding base can be repurposed for a 2nd application, tutoring, and is an example of an intelligent tutoring system, a specific sort of knowledge-based application. Clancey showed that it was not sufficient merely to utilize MYCIN’s guidelines for direction, but that he likewise required to include guidelines for discussion management and student modeling. [50] XCON is considerable since of the countless dollars it conserved DEC, which triggered the specialist system boom where most all major corporations in the US had skilled systems groups, to capture corporate know-how, preserve it, and automate it:

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

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

Architecture of knowledge-based and expert systems

An essential element of the system architecture for all specialist systems is the understanding base, which stores realities and guidelines for problem-solving. [53] The simplest approach for an expert system understanding base is just a collection or network of production guidelines. Production guidelines connect symbols in a relationship similar to an If-Then declaration. The professional system processes the guidelines to make deductions and to determine what extra info it requires, i.e. what questions to ask, using human-readable symbols. For instance, OPS5, CLIPS and their followers Jess and Drools run in this style.

Expert systems can operate in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and requirements – manner. Advanced knowledge-based systems, such as Soar can also perform meta-level thinking, that is thinking about their own thinking in regards to choosing how to fix problems and keeping an eye on the success of problem-solving strategies.

Blackboard systems are a second kind of knowledge-based or professional system architecture. They model a neighborhood of professionals incrementally contributing, where they can, to resolve a problem. The problem is represented in several levels of abstraction or alternate views. The experts (understanding sources) volunteer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on an agenda that is updated as the issue circumstance modifications. A controller decides how useful each contribution is, and who need to make the next problem-solving action. One example, the BB1 chalkboard architecture [54] was initially influenced by studies of how human beings plan to carry out numerous jobs in a journey. [55] A development of BB1 was to apply the exact same blackboard design to resolving its control issue, i.e., its controller performed meta-level reasoning with knowledge sources that kept an eye on how well a strategy or the analytical was continuing and might change from one method to another as conditions – such as goals or times – altered. BB1 has actually been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time client monitoring.

The 2nd AI winter, 1988-1993

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were offering LISP makers particularly targeted to speed up the advancement of AI applications and research study. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were offering skilled system shells, training, and speaking with to corporations.

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

Many factors can be offered for the arrival of the second AI winter season. The hardware companies failed when much more cost-efficient basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the market. Many industrial implementations of expert systems were discontinued when they showed too pricey to preserve. Medical specialist systems never ever caught on for several factors: the problem in keeping them approximately date; the obstacle for doctor to find out how to use an overwelming variety of various specialist systems for various medical conditions; and maybe most crucially, the hesitation of doctors to rely on a computer-made diagnosis over their gut instinct, even for particular domains where the expert systems could outshine an average doctor. Equity capital cash deserted AI almost over night. The world AI conference IJCAI hosted a huge and extravagant trade show and thousands of nonacademic guests in 1987 in Vancouver; the main AI conference the following year, AAAI 1988 in St. Paul, was a small and strictly scholastic affair. [9]

Including more rigorous foundations, 1993-2011

Uncertain reasoning

Both analytical approaches and extensions to logic were tried.

One statistical approach, hidden Markov designs, had already been popularized in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized the use of Bayesian Networks as a sound however efficient way of managing unsure reasoning with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were used successfully in professional systems. [57] Even later on, in the 1990s, statistical relational learning, an approach that integrates possibility with logical formulas, enabled probability to be integrated with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.

Other, non-probabilistic extensions to first-order reasoning to support were also tried. For instance, non-monotonic thinking could be used with fact maintenance systems. A reality maintenance system tracked assumptions and validations for all reasonings. It allowed reasonings to be withdrawn when presumptions were discovered to be incorrect or a contradiction was derived. Explanations might be offered for a reasoning by explaining which rules were used to create it and after that continuing through underlying inferences and guidelines all the way back to root presumptions. [58] Lofti Zadeh had introduced a different kind of extension to manage the representation of uncertainty. For instance, in deciding how « heavy » or « tall » a male is, there is frequently no clear « yes » or « no » answer, and a predicate for heavy or tall would instead return values in between 0 and 1. Those values represented to what degree the predicates held true. His fuzzy logic even more supplied a way for propagating combinations of these values through rational solutions. [59]

Artificial intelligence

Symbolic maker learning methods were investigated to attend to the knowledge acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test strategy to generate plausible guideline hypotheses to check against spectra. Domain and job understanding reduced the variety of candidates evaluated to a workable size. Feigenbaum explained Meta-DENDRAL as

… the conclusion of my imagine the early to mid-1960s having to do with theory formation. 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 knowledge got in there because we interviewed people. But how did individuals get the understanding? By taking a look at thousands of spectra. So we wanted a program that would take a look at thousands of spectra and infer the knowledge of mass spectrometry that DENDRAL could utilize to solve private hypothesis development problems. We did it. We were even able to release brand-new understanding of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, actually did it. We were able to do something that had actually been a dream: to have a computer program developed a new and publishable piece of science. [51]

In contrast to the knowledge-intensive method of Meta-DENDRAL, Ross Quinlan developed a domain-independent technique to statistical classification, choice tree learning, starting first with ID3 [60] and then later on extending its capabilities to C4.5. [61] The decision trees produced are glass box, interpretable classifiers, with human-interpretable category rules.

Advances were made in comprehending device knowing theory, too. Tom Mitchell introduced variation area knowing which describes knowing as an explore a space of hypotheses, with upper, more general, and lower, more specific, boundaries including all viable hypotheses constant with the examples seen so far. [62] More officially, Valiant presented Probably Approximately Correct Learning (PAC Learning), a structure for the mathematical analysis of maker knowing. [63]

Symbolic maker learning incorporated more than finding out by example. E.g., John Anderson provided a cognitive model of human learning where skill practice leads to a collection of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a student may discover to use « Supplementary angles are 2 angles whose steps sum 180 degrees » as several various procedural rules. E.g., one guideline might state that if X and Y are supplementary and you know X, then Y will be 180 – X. He called his approach « understanding collection ». ACT-R has been used effectively to model elements of human cognition, such as finding out and retention. ACT-R is likewise utilized in smart tutoring systems, called cognitive tutors, to effectively teach geometry, computer shows, and algebra to school kids. [64]

Inductive logic programs was another method to discovering that enabled 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 create hereditary programs, which he utilized to manufacture LISP programs. Finally, Zohar Manna and Richard Waldinger supplied a more basic technique to program synthesis that manufactures a practical program in the course of showing its requirements to be correct. [66]

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

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

1. Learning from guideline or advice-i.e., taking human guideline, impersonated advice, and determining how to operationalize it in particular situations. For instance, in a video game of Hearts, finding out exactly how to play a hand to « avoid taking points. » [70] 2. Learning from exemplars-improving performance by accepting subject-matter professional (SME) feedback throughout training. When problem-solving fails, querying the professional to either discover a brand-new exemplar for analytical or to learn a new explanation regarding precisely why one exemplar is more appropriate than another. For example, the program Protos found out to diagnose tinnitus cases by interacting with an audiologist. [71] 3. Learning by analogy-constructing problem options based on similar problems seen in the past, and after that modifying their options to fit a new circumstance or domain. [72] [73] 4. Apprentice learning systems-learning unique options to problems by observing human analytical. Domain understanding describes why unique options are right and how the service can be generalized. LEAP learned how to design VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing tasks to perform experiments and then finding out from the outcomes. Doug Lenat’s Eurisko, for instance, discovered heuristics to beat human players at the Traveller role-playing game for two years in a row. [75] 6. Learning macro-operators-i.e., looking for useful macro-operators to be discovered from sequences of standard problem-solving actions. Good macro-operators simplify analytical by permitting problems to be resolved at a more abstract level. [76]
Deep knowing and neuro-symbolic AI 2011-now

With the increase of deep learning, the symbolic AI approach has been compared to deep knowing as complementary « … with parallels having actually been drawn lot of times by AI scientists in between Kahneman’s research on human reasoning and decision making – shown in his book Thinking, Fast and Slow – and the so-called « AI systems 1 and 2″, which would in concept be designed by deep learning and symbolic reasoning, respectively. » In this view, symbolic thinking is more apt for deliberative thinking, planning, and description while deep learning is more apt for fast pattern recognition in affective applications with loud information. [17] [18]

Neuro-symbolic AI: incorporating neural and symbolic approaches

Neuro-symbolic AI attempts to incorporate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of thinking, learning, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the reliable construction of rich computational cognitive designs requires the combination of sound symbolic reasoning and efficient (maker) learning models. Gary Marcus, likewise, argues that: « We can not construct abundant cognitive models in an adequate, automatic method without the triumvirate of hybrid architecture, rich anticipation, and sophisticated methods for reasoning. », [79] and in specific: « To build a robust, knowledge-driven method to AI we must have the machinery of symbol-manipulation in our toolkit. Too much of helpful understanding is abstract to make do without tools that represent and control abstraction, and to date, the only equipment that we know of that can manipulate such abstract understanding dependably is the apparatus of symbol control.  » [80]

Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have likewise argued for a synthesis. Their arguments are based upon a requirement to attend to the 2 kinds of believing discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman explains human thinking as having two parts, System 1 and System 2. System 1 is quick, automatic, user-friendly and unconscious. System 2 is slower, detailed, and specific. System 1 is the kind used for pattern recognition while System 2 is far better fit for preparation, deduction, and deliberative thinking. In this view, deep knowing finest models the very first kind of thinking while symbolic reasoning best models the 2nd kind and both are needed.

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

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

The combination of the symbolic and connectionist paradigms of AI has actually been pursued by a fairly small research neighborhood over the last 20 years and has yielded a number of considerable outcomes. Over the last years, neural symbolic systems have been shown capable of conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see likewise (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal reasonings (d’Avila Garcez and Lamb, 2006) and fragments of first-order reasoning (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been used to a variety of problems in the locations of bioinformatics, control engineering, software application verification and adjustment, visual intelligence, ontology knowing, and video game. [78]

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

– Symbolic Neural symbolic-is the present approach of lots of neural models in natural language processing, where words or subword tokens are both the supreme input and output of large language designs. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exemplified by AlphaGo, where symbolic strategies are utilized to call neural methods. In this case the symbolic approach is Monte Carlo tree search and the neural methods find out how to examine game positions.
– Neural|Symbolic-uses a neural architecture to analyze perceptual data as signs and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to produce or identify training information that is subsequently learned by a deep learning model, e.g., to train a neural design for symbolic calculation by using a Macsyma-like symbolic mathematics system to develop or identify examples.
– Neural _ Symbolic -uses a neural web that is produced from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree produced from understanding base rules and terms. Logic Tensor Networks [86] likewise fall into this classification.
– Neural [Symbolic] -allows a neural design to straight call a symbolic thinking engine, e.g., to carry out an action or assess a state.

Many key research study concerns stay, such as:

– What is the finest way to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and drawn out from them?
– How should common-sense understanding be found out and reasoned about?
– How can abstract knowledge that is tough to encode logically be dealt with?

Techniques and contributions

This area offers a summary of methods and contributions in a total context causing lots of other, more detailed articles in Wikipedia. Sections on Artificial Intelligence and Uncertain Reasoning are covered earlier in the history area.

AI programming languages

The crucial AI programming language in the US throughout the last symbolic AI boom duration was LISP. LISP is the 2nd earliest programs language after FORTRAN and was created in 1958 by John McCarthy. LISP supplied the very first read-eval-print loop to support rapid program development. Compiled functions could be freely blended with interpreted functions. Program tracing, stepping, and breakpoints were also offered, along with the capability to alter worths or functions and continue from breakpoints or mistakes. It had the first self-hosting compiler, implying that the compiler itself was initially written in LISP and then ran interpretively to assemble the compiler code.

Other essential innovations pioneered by LISP that have actually infected other shows languages consist of:

Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals

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

In contrast to the US, in Europe the crucial AI shows language throughout that same period was Prolog. Prolog provided an integrated store of facts and clauses that could be queried by a read-eval-print loop. The store could act as an understanding base and the provisions could act as guidelines or a restricted form of reasoning. As a subset of first-order logic Prolog was based upon Horn clauses with a closed-world assumption-any truths not understood were considered false-and a distinct name assumption for primitive terms-e.g., the identifier barack_obama was considered to refer to exactly one object. Backtracking and marriage are built-in to Prolog.

Alain Colmerauer and Philippe Roussel are credited as the creators of Prolog. Prolog is a type of logic shows, which was invented 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 section on the origins of Prolog in the PLANNER post.

Prolog is likewise a type of declarative programming. The logic provisions that describe programs are straight interpreted to run the programs defined. No explicit series of actions is required, as is the case with necessary programs languages.

Japan promoted Prolog for its Fifth Generation Project, planning to construct special hardware for high performance. Similarly, LISP machines were constructed to run LISP, but as the second AI boom turned to bust these companies could not complete with new workstations that could now run LISP or Prolog natively at comparable speeds. See the history section for more detail.

Smalltalk was another influential AI shows language. For instance, it presented metaclasses and, together with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the existing standard Lisp dialect. CLOS is a Lisp-based object-oriented system that enables several inheritance, in addition to incremental extensions to both classes and metaclasses, thus offering a run-time meta-object protocol. [88]

For other AI shows languages see this list of programming languages for artificial intelligence. Currently, Python, a multi-paradigm shows language, is the most popular programming language, partially due to its comprehensive plan library that supports data science, natural language processing, and deep knowing. Python includes a read-eval-print loop, practical elements such as higher-order functions, and object-oriented shows that consists of metaclasses.

Search

Search emerges in numerous kinds of problem resolving, including planning, constraint complete satisfaction, and playing video games such as checkers, chess, and go. The very best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven stipulation learning, and the DPLL algorithm. For adversarial search when playing video games, alpha-beta pruning, branch and bound, and minimax were early contributions.

Knowledge representation and reasoning

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

Knowledge representation

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

Description logic is a reasoning for automated classification of ontologies and for spotting irregular category data. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can check out in OWL ontologies and then inspect consistency with deductive classifiers such as such as HermiT. [89]

First-order reasoning is more basic than description reasoning. The automated theorem provers gone over below can show theorems in first-order logic. Horn clause logic is more restricted than first-order reasoning and is used in reasoning programs languages such as Prolog. Extensions to first-order logic consist of temporal reasoning, to deal with time; epistemic logic, to reason about representative knowledge; modal logic, to manage possibility and need; and probabilistic reasonings to manage reasoning and probability together.

Automatic theorem showing

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

Prover9.
ACL2.
Vampire.

Prover9 can be utilized in conjunction with the Mace4 design checker. ACL2 is a theorem prover that can deal with evidence by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise known as Nqthm.

Reasoning in knowledge-based systems

Knowledge-based systems have a specific knowledge base, generally of guidelines, to enhance reusability throughout domains by separating procedural code and domain understanding. A separate reasoning engine processes guidelines and includes, deletes, or customizes an understanding shop.

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

A more flexible sort of problem-solving takes place when thinking about what to do next takes place, instead of simply selecting among the readily available actions. This sort of meta-level thinking is used in Soar and in the BB1 blackboard architecture.

Cognitive architectures such as ACT-R might have additional capabilities, such as the capability to assemble regularly used understanding into higher-level portions.

Commonsense reasoning

Marvin Minsky initially proposed frames as a method of translating common visual situations, such as a workplace, and Roger Schank extended this idea to scripts for typical regimens, such as eating in restaurants. Cyc has attempted to capture helpful common-sense knowledge and has « micro-theories » to manage specific type of domain-specific thinking.

Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human thinking about naive physics, such as what occurs when we heat up a liquid in a pot on the stove. We expect it to heat and possibly boil over, despite the fact that we might not know its temperature, 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 resolved with restraint solvers.

Constraints and constraint-based reasoning

Constraint solvers carry out a more minimal sort of inference than first-order reasoning. They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with fixing other kinds of puzzle issues, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint reasoning programming can be used to solve scheduling problems, for instance with constraint dealing with rules (CHR).

Automated preparation

The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create strategies. STRIPS took a various method, viewing planning as theorem proving. Graphplan takes a least-commitment method to planning, instead of sequentially selecting actions from an initial state, working forwards, or an objective state if working in reverse. Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem.

Natural language processing

Natural language processing focuses on dealing with language as information to carry out tasks such as recognizing topics without always comprehending the desired significance. Natural language understanding, in contrast, constructs a meaning representation and uses that for more processing, such as addressing questions.

Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long managed by symbolic AI, but because improved by deep knowing methods. In symbolic AI, discourse representation theory and first-order reasoning have actually been utilized to represent sentence meanings. Latent semantic analysis (LSA) and specific semantic analysis likewise offered vector representations of documents. In the latter case, vector components are interpretable as principles named by Wikipedia articles.

New deep learning methods based on Transformer designs have actually now eclipsed these earlier symbolic AI approaches and attained advanced performance in natural language processing. However, Transformer models are opaque 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 parts is nontransparent.

Agents and multi-agent systems

Agents are self-governing systems embedded in an environment they view and act upon in some sense. Russell and Norvig’s basic book 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 design of the world and automated preparation abilities, possibly a BDI representative, i.e., one with beliefs, desires, and intentions – or alternatively a support learning model discovered over time to select actions – approximately a mix of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep knowing for understanding. [92]

On the other hand, a multi-agent system includes multiple agents that interact amongst themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The representatives require not all have the very same internal architecture. Advantages of multi-agent systems consist of the ability to divide work among the representatives and to increase fault tolerance when representatives are lost. Research problems include how representatives reach agreement, dispersed problem solving, multi-agent knowing, multi-agent preparation, and dispersed restriction optimization.

Controversies occurred 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 in between those who welcomed AI but turned down symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mainly from philosophers, on intellectual premises, however likewise from financing companies, particularly throughout the 2 AI winters.

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

Limitations were found in using simple first-order reasoning to factor about vibrant domains. Problems were found both with regards to specifying the prerequisites for an action to prosper and in providing axioms for what did not change 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] A basic example takes place in « showing that a person person could enter into discussion with another », as an axiom asserting « if an individual has a telephone he still has it after looking up a number in the telephone book » would be needed for the reduction to prosper. Similar axioms would be needed for other domain actions to define what did not alter.

A comparable problem, called the Qualification Problem, happens in attempting to identify the prerequisites for an action to succeed. An unlimited variety of pathological conditions can be envisioned, e.g., a banana in a tailpipe could prevent a cars and truck from operating properly.

McCarthy’s method to repair the frame issue was circumscription, a type of non-monotonic logic where reductions could be made from actions that need only specify what would change while not needing to explicitly specify whatever that would not alter. Other non-monotonic reasonings provided truth maintenance systems that revised beliefs leading to contradictions.

Other methods of managing more open-ended domains included probabilistic thinking systems and artificial intelligence to discover brand-new ideas and rules. McCarthy’s Advice Taker can be seen as a motivation here, as it could integrate brand-new understanding provided by a human in the type of assertions or rules. For instance, experimental symbolic machine learning systems checked out the ability to take high-level natural language suggestions and to analyze it into domain-specific actionable rules.

Similar to the issues in dealing with vibrant domains, common-sense thinking is likewise challenging to record in formal thinking. Examples of common-sense reasoning include implicit thinking about how individuals think or general understanding of day-to-day events, objects, and living animals. This type of knowledge is taken for granted and not considered as noteworthy. Common-sense thinking is an open area of research and challenging both for symbolic systems (e.g., Cyc has tried to capture key parts of this understanding over more than a decade) and neural systems (e.g., self-driving vehicles that do not know not to drive into cones or not to strike pedestrians strolling a bike).

McCarthy saw his Advice Taker as having common-sense, but his meaning of common-sense was different than the one above. [94] He defined a program as having good sense « if it automatically deduces for itself a sufficiently broad class of instant effects of anything it is informed and what it already understands. « 

Connectionist AI: philosophical challenges and sociological disputes

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

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

1. Implementationism-where connectionist architectures execute the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined totally, and connectionist architectures underlie intelligence and are fully enough to explain it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are seen as complementary and both are required for intelligence

Olazaran, in his sociological history of the controversies within the neural network community, explained the moderate connectionism consider as basically suitable with current research study in neuro-symbolic hybrids:

The 3rd and last position I would like to take a look at here is what I call the moderate connectionist view, a more diverse view of the present dispute in between connectionism and symbolic AI. One of the scientists who has actually elaborated this position most clearly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark safeguarded hybrid (partly symbolic, partially connectionist) systems. He declared that (a minimum of) 2 type of theories are required in order to study and model cognition. On the one hand, for some information-processing jobs (such as pattern recognition) connectionism has benefits over symbolic models. But on the other hand, for other cognitive procedures (such as serial, deductive thinking, and generative sign adjustment procedures) the symbolic paradigm offers sufficient designs, and not only « approximations » (contrary to what extreme connectionists would declare). [97]

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

To think that we can just abandon symbol-manipulation is to suspend disbelief.

And yet, for the most part, that’s how most present AI proceeds. Hinton and numerous others have striven to eliminate symbols altogether. The deep knowing hope-seemingly grounded not a lot in science, however in a sort of historic grudge-is that smart habits will emerge simply from the confluence of enormous data and deep learning. Where classical computers and software application solve jobs by defining sets of symbol-manipulating guidelines committed to particular jobs, such as editing a line in a word processor or carrying out a calculation in a spreadsheet, neural networks normally attempt to fix jobs by statistical approximation and learning from examples.

According to Marcus, Geoffrey Hinton and his colleagues have actually been vehemently « anti-symbolic »:

When deep learning reemerged in 2012, it was with a type of take-no-prisoners mindset that has actually identified most of the last decade. By 2015, his hostility toward all things symbols had completely crystallized. He lectured at an AI workshop at Stanford comparing symbols to aether, one of science’s biggest mistakes.

Ever since, his anti-symbolic campaign has only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton composed a manifesto for deep knowing in one of science’s essential journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation however for straight-out replacement. Later, Hinton informed a gathering of European Union leaders that investing any additional cash in symbol-manipulating approaches was « a huge error, » likening it to buying internal combustion engines in the period of electric cars. [98]

Part of these disagreements might be because of unclear terms:

Turing award winner Judea Pearl offers a critique of artificial intelligence which, sadly, conflates the terms artificial intelligence and deep knowing. Similarly, when Geoffrey Hinton describes symbolic AI, the undertone of the term tends to be that of professional systems dispossessed of any capability to learn. Making use of the terms requires clarification. Artificial intelligence is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep knowing being the choice of representation, localist rational instead of dispersed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not practically production rules composed by hand. A correct meaning of AI issues understanding representation and reasoning, self-governing multi-agent systems, planning and argumentation, along with knowing. [99]

Situated robotics: the world as a model

Another critique of symbolic AI is the embodied cognition technique:

The embodied cognition approach declares that it makes no sense to consider the brain independently: cognition takes location within a body, which is embedded in an environment. We require to study the system as a whole; the brain’s functioning exploits consistencies in its environment, including the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensing units end up being main, not peripheral. [100]

Rodney Brooks invented behavior-based robotics, one approach to embodied cognition. Nouvelle AI, another name for this technique, is viewed as an alternative to both symbolic AI and connectionist AI. His method rejected representations, either symbolic or dispersed, as not only unnecessary, however as harmful. Instead, he created the subsumption architecture, a layered architecture for embodied representatives. Each layer achieves a various purpose and must function in the real world. For example, the very first robotic he explains in Intelligence Without Representation, has 3 layers. The bottom layer translates finder sensing units to prevent objects. The middle layer triggers the robot to wander around when there are no obstacles. The leading layer triggers the robotic to go to more remote places for more exploration. Each layer can briefly hinder or reduce a lower-level layer. He slammed AI scientists for specifying AI problems for their systems, when: « There is no clean department between understanding (abstraction) and reasoning in the real life. » [101] He called his robots « Creatures » and each layer was « made up of a fixed-topology network of easy limited state machines. » [102] In the Nouvelle AI method, « First, it is essential to check the Creatures we integrate in the real world; i.e., in the very same world that we humans populate. It is disastrous to fall under the temptation of checking them in a simplified world initially, even with the best objectives of later moving activity to an unsimplified world. » [103] His focus on real-world screening remained in contrast to « Early operate in AI focused on games, geometrical problems, symbolic algebra, theorem proving, and other official systems » [104] and the usage 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 criticized by the other approaches. Symbolic AI has actually been slammed as disembodied, responsible to the credentials problem, and bad in dealing with the perceptual issues where deep finding out excels. In turn, connectionist AI has actually been slammed as poorly fit for deliberative step-by-step problem fixing, including knowledge, and dealing with planning. Finally, Nouvelle AI masters reactive and real-world robotics domains however has actually been criticized for problems in incorporating knowing and understanding.

Hybrid AIs integrating one or more of these methods are presently 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 stated that Al is therefore impossible; we now see a number of these very same locations undergoing continued research and development resulting in increased capability, not impossibility. [100]

Expert system.
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 expert system
Inductive logic programs
Knowledge-based systems
Knowledge representation and thinking
Logic programming
Artificial intelligence
Model checking
Model-based thinking
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of synthetic intelligence
Physical sign systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet

Notes

^ McCarthy once said: « This is AI, so we don’t care if it’s psychologically genuine ». [4] McCarthy repeated 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 expert system: one focused on producing smart behavior regardless of how it was accomplished, and the other focused on modeling intelligent procedures discovered in nature, particularly human ones. », [29] Stuart Russell and Peter Norvig composed « Aeronautical engineering texts do not specify the goal of their field as making ‘machines that fly so precisely like pigeons that they can deceive even other pigeons.' » [30] Citations

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^ Thomason, Richmond (February 27, 2024). « Logic-Based Artificial Intelligence ». In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
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^ 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.
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^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
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^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
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^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
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^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
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^ Crevier 1993, pp. 239-243.
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