
Fasnewsng
Add a review FollowOverview
-
Founded Date augusti 7, 2020
-
Sectors Telecom
-
Posted Jobs 0
-
Viewed 6
Company Description
Symbolic Artificial Intelligence
In synthetic intelligence, symbolic artificial intelligence (also known as classical expert system or logic-based synthetic intelligence) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of issues, logic and search. [3] Symbolic AI utilized tools such as reasoning programming, production guidelines, semantic internet and frames, and it established applications such as knowledge-based systems (in particular, skilled systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated preparation and scheduling systems. The Symbolic AI paradigm led to critical ideas in search, symbolic programming languages, representatives, multi-agent systems, the semantic web, and the strengths and constraints of official understanding and reasoning systems.
Symbolic AI was the dominant paradigm of AI research study from the mid-1950s till the mid-1990s. [4] Researchers in the 1960s and the 1970s were persuaded that symbolic methods would ultimately be successful in developing a maker with synthetic basic intelligence and considered this the ultimate objective 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 guarantees and was followed by the very first AI Winter as funding dried up. [5] [6] A second boom (1969-1986) accompanied the rise of professional systems, their pledge of catching business proficiency, 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 difficulties in knowledge acquisition, preserving large knowledge bases, and brittleness in managing out-of-domain problems emerged. Another, 2nd, AI Winter (1988-2011) followed. [9] Subsequently, AI researchers focused on attending to underlying problems in dealing with unpredictability and in understanding acquisition. [10] Uncertainty was attended to with formal approaches such as surprise Markov designs, Bayesian reasoning, and statistical relational knowing. [11] [12] Symbolic maker discovering addressed the understanding acquisition issue with contributions consisting of Version Space, Valiant’s PAC learning, Quinlan’s ID3 decision-tree knowing, case-based knowing, and inductive logic shows to discover relations. [13]
Neural networks, a subsymbolic method, had been pursued from early days and reemerged highly in 2012. Early examples are Rosenblatt’s perceptron knowing work, the backpropagation work of Rumelhart, Hinton and Williams, [14] and operate in convolutional neural networks by LeCun et al. in 1989. [15] However, neural networks were not deemed successful up until about 2012: ”Until Big Data became prevalent, the basic agreement in the Al community was that the so-called neural-network approach was helpless. Systems just didn’t work that well, compared to other approaches. … A transformation was available in 2012, when a variety of individuals, including a team of researchers working with Hinton, worked out a way to use the power of GPUs to enormously increase the power of neural networks.” [16] Over the next numerous years, deep learning had amazing success in managing vision, speech recognition, speech synthesis, image generation, and device translation. However, since 2020, as inherent problems with bias, description, comprehensibility, and effectiveness ended up being more evident with deep learning techniques; an increasing number of AI researchers have called for combining the very best of both the symbolic and neural network approaches [17] [18] and attending to locations that both techniques have trouble with, such as sensible thinking. [16]
A short history of symbolic AI to today day follows listed below. Time durations 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 summer: illogical vitality, 1948-1966
Success at early attempts in AI took place in three primary locations: synthetic neural networks, understanding representation, and heuristic search, contributing to high expectations. This section sums up Kautz’s reprise of early AI history.
Approaches inspired by human or animal cognition or behavior
Cybernetic approaches attempted to duplicate 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 internet, was constructed as early as 1948. This work can be seen as an early precursor to later work in neural networks, reinforcement learning, and located robotics. [20]
An important early symbolic AI program was the Logic theorist, composed by Allen Newell, Herbert Simon and Cliff Shaw in 1955-56, as it was able to show 38 primary theorems from Whitehead and Russell’s Principia Mathematica. Newell, Simon, and Shaw later on generalized this work to produce a domain-independent issue solver, GPS (General Problem Solver). GPS solved issues represented with formal operators through state-space search using means-ends analysis. [21]
During the 1960s, symbolic techniques attained fantastic success at replicating intelligent habits in structured environments such as game-playing, symbolic mathematics, and theorem-proving. AI research study was concentrated in four institutions in the 1960s: Carnegie Mellon University, Stanford, MIT and (later) University of Edinburgh. Each one developed its own design of research study. Earlier approaches 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 foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research study team used the results of psychological experiments to develop programs that simulated the techniques that individuals used to solve problems. [22] [23] This tradition, focused at Carnegie Mellon University would ultimately culminate in the development of the Soar architecture in the center 1980s. [24] [25]
Heuristic search
In addition to the extremely specialized domain-specific type of knowledge that we will see later on used in expert systems, early symbolic AI researchers discovered another more basic application of knowledge. These were called heuristics, guidelines that assist a search in appealing instructions: ”How can non-enumerative search be practical when the underlying issue is greatly difficult? The technique advocated by Simon and Newell is to utilize heuristics: fast algorithms that may stop working on some inputs or output suboptimal solutions.” [26] Another essential advance was to find a method to apply these heuristics that guarantees a service will be discovered, if there is one, not holding up against the periodic fallibility of heuristics: ”The A * algorithm supplied a basic frame for total and optimal heuristically guided search. A * is used as a subroutine within virtually every AI algorithm today however is still no magic bullet; its assurance of efficiency is purchased the expense of worst-case exponential time. [26]
Early work on knowledge representation and reasoning
Early work covered both applications of formal reasoning stressing first-order reasoning, along with attempts to manage common-sense reasoning in a less formal way.
Modeling formal thinking with reasoning: the ”neats”
Unlike Simon and Newell, John McCarthy felt that machines did not require to simulate the specific mechanisms of human thought, but might rather try to find the essence of abstract reasoning and problem-solving with reasoning, [27] no matter whether individuals used the same algorithms. [a] His lab at Stanford (SAIL) concentrated on using formal logic to solve a variety of problems, including understanding representation, planning and learning. [31] Logic was likewise the focus of the work at the University of Edinburgh and in other places in Europe which caused the development of the programs language Prolog and the science of reasoning 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 solving hard problems in vision and natural language processing required ad hoc solutions-they argued that no simple and basic concept (like reasoning) would record all the elements of smart behavior. Roger Schank described their ”anti-logic” approaches as ”shabby” (as opposed to the ”neat” paradigms at CMU and Stanford). [36] [37] Commonsense understanding bases (such as Doug Lenat’s Cyc) are an example of ”shabby” AI, considering that they need to be built by hand, one complicated principle at a time. [38] [39] [40]
The first AI winter season: crushed dreams, 1967-1977
The very first AI winter was a shock:
During the first AI summer, many individuals thought that maker intelligence might be attained in simply a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research study to utilize AI to fix issues of national security; in specific, to automate the translation of Russian to English for intelligence operations and to develop autonomous tanks for the battlefield. Researchers had actually begun to understand that attaining AI was going to be much more difficult than was supposed a years previously, but a combination of hubris and disingenuousness led lots of university and think-tank researchers to accept financing with pledges of deliverables that they should have understood they could not fulfill. By the mid-1960s neither beneficial natural language translation systems nor self-governing tanks had actually been developed, and a significant backlash set in. New DARPA management canceled existing AI financing programs.
Beyond the United States, the most fertile ground for AI research study was the UK. The AI winter season in the United Kingdom was stimulated on not so much by disappointed military leaders as by rival academics who viewed AI scientists as charlatans and a drain on research study funding. A professor of applied mathematics, Sir James Lighthill, was commissioned by Parliament to assess the state of AI research study in the country. The report mentioned that all of the issues being dealt with in AI would be better managed by researchers from other disciplines-such as used mathematics. The report also claimed that AI successes on toy issues could never 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 approaches became increasingly more apparent, [42] scientists from all 3 customs began to construct knowledge into AI applications. [43] [7] The understanding revolution was driven by the awareness that knowledge underlies high-performance, domain-specific AI applications.
Edward Feigenbaum said:
– ”In the understanding lies the power.” [44]
to describe that high performance in a specific domain requires both general and highly domain-specific understanding. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle:
( 1) The Knowledge Principle: if a program is to perform an intricate job well, it needs to know a good deal about the world in which it runs.
( 2) A plausible extension of that concept, called the Breadth Hypothesis: there are 2 extra abilities needed for intelligent behavior in unanticipated situations: drawing on increasingly basic understanding, and analogizing to particular however distant understanding. [45]
Success with professional systems
This ”understanding transformation” caused the development and deployment of specialist systems (introduced by Edward Feigenbaum), the first commercially successful type of AI software. [46] [47] [48]
Key expert systems were:
DENDRAL, which found the structure of natural particles from their chemical formula and mass spectrometer readings.
MYCIN, which diagnosed bacteremia – and recommended additional laboratory tests, when required – by translating laboratory results, patient history, and doctor observations. ”With about 450 rules, MYCIN was able to carry out in addition to some experts, and significantly much better than junior medical professionals.” [49] INTERNIST and CADUCEUS which tackled internal medicine medical diagnosis. Internist attempted to record the expertise of the chairman of internal medicine at the University of Pittsburgh School of Medicine while CADUCEUS could eventually identify as much as 1000 different diseases.
– GUIDON, which revealed how a knowledge base constructed for professional issue solving could be repurposed for teaching. [50] XCON, to configure VAX computer systems, a then laborious process that could take up to 90 days. XCON lowered the time to about 90 minutes. [9]
DENDRAL is thought about the first professional system that count on knowledge-intensive analytical. It is described listed below, by Ed Feigenbaum, from a Communications of the ACM interview, Interview with Ed Feigenbaum:
Among individuals at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genes. When I told him I desired an induction ”sandbox”, he said, ”I have just the one for you.” His lab was doing mass spectrometry of amino acids. The concern 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 excellent at heuristic search approaches, and he had an algorithm that was great at producing the chemical problem area.
We did not have a grand vision. We worked bottom up. Our chemist was Carl Djerassi, developer of the chemical behind the contraceptive pill, and likewise one of the world’s most respected mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We began to contribute to their knowledge, developing understanding of engineering as we went along. These experiments amounted to titrating DENDRAL more and more understanding. The more you did that, the smarter the program became. We had extremely great outcomes.
The generalization was: in the understanding lies the power. That was the big 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 most likely AI’s most effective generalization. [51]
The other expert systems mentioned above came after DENDRAL. MYCIN exhibits the traditional expert system architecture of a knowledge-base of rules coupled to a symbolic reasoning system, consisting of using certainty elements to handle uncertainty. GUIDON reveals how a specific knowledge base can be repurposed for a second application, tutoring, and is an example of an intelligent tutoring system, a specific type of knowledge-based application. Clancey revealed that it was not sufficient simply to utilize MYCIN’s guidelines for direction, but that he also required to include rules for discussion management and trainee modeling. [50] XCON is significant due to the fact that of the millions of dollars it conserved DEC, which triggered the expert system boom where most all significant corporations in the US had expert systems groups, to capture corporate competence, protect it, and automate it:
By 1988, DEC’s AI group had 40 specialist systems released, with more en route. DuPont had 100 in use and 500 in development. Nearly every significant U.S. corporation had its own Al group and was either utilizing or examining specialist systems. [49]
Chess professional knowledge was encoded in Deep Blue. In 1996, this enabled IBM’s Deep Blue, with the aid of symbolic AI, to win in a game of chess versus the world champion at that time, Garry Kasparov. [52]
Architecture of knowledge-based and professional systems
A key component of the system architecture for all specialist systems is the knowledge base, which stores truths and rules for problem-solving. [53] The easiest technique for an expert system knowledge base is merely a collection or network of production guidelines. Production guidelines link symbols in a relationship similar to an If-Then declaration. The expert system processes the guidelines to make deductions and to identify what extra info it requires, i.e. what questions to ask, using human-readable signs. For instance, OPS5, CLIPS and their followers Jess and Drools operate in this fashion.
Expert systems can run in either a forward chaining – from proof to conclusions – or backwards chaining – from goals to needed information and prerequisites – way. Advanced knowledge-based systems, such as Soar can also perform meta-level thinking, that is reasoning about their own reasoning in regards to deciding how to solve issues and monitoring the success of problem-solving strategies.
Blackboard systems are a 2nd sort of knowledge-based or expert system architecture. They design a neighborhood of professionals incrementally contributing, where they can, to solve an issue. The problem is represented in numerous levels of abstraction or alternate views. The specialists (understanding sources) offer their services whenever they acknowledge they can contribute. Potential analytical actions are represented on a program that is upgraded as the problem scenario modifications. A controller chooses how useful each contribution is, and who should make the next problem-solving action. One example, the BB1 blackboard architecture [54] was initially motivated by research studies of how human beings plan to carry out several jobs in a journey. [55] An innovation of BB1 was to use the very same blackboard design to fixing its control problem, i.e., its controller performed meta-level thinking with understanding sources that kept an eye on how well a plan or the problem-solving was proceeding and might switch from one strategy to another as conditions – such as objectives or times – changed. BB1 has been used in numerous domains: construction website preparation, smart tutoring systems, and real-time client tracking.
The second AI winter, 1988-1993
At the height of the AI boom, business such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to speed up the advancement of AI applications and research. In addition, numerous expert system business, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations.
Unfortunately, the AI boom did not last and Kautz best describes the 2nd AI winter season that followed:
Many reasons can be used for the arrival of the 2nd AI winter. The hardware business stopped working when far more cost-efficient basic Unix workstations from Sun together with great compilers for LISP and Prolog came onto the marketplace. Many commercial implementations of professional systems were terminated when they showed too costly to maintain. Medical expert systems never captured on for numerous factors: the difficulty in keeping them approximately date; the difficulty for medical professionals to discover how to utilize an overwelming variety of different professional systems for different medical conditions; and maybe most crucially, the reluctance of physicians to rely on a computer-made diagnosis over their gut instinct, even for particular domains where the professional systems could exceed a typical doctor. Venture capital cash deserted AI practically overnight. The world AI conference IJCAI hosted an enormous and extravagant trade convention 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 academic affair. [9]
Including more extensive foundations, 1993-2011
Uncertain thinking
Both analytical methods and extensions to reasoning were attempted.
One statistical approach, hidden Markov designs, had already been popularized in the 1980s for speech acknowledgment work. [11] Subsequently, in 1988, Judea Pearl popularized using Bayesian Networks as a noise however effective way of dealing with unpredictable thinking with his publication of the book Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. [56] and Bayesian techniques were used effectively in professional systems. [57] Even later on, in the 1990s, analytical relational knowing, a technique that combines possibility with sensible solutions, enabled possibility to be combined with first-order logic, e.g., with either Markov Logic Networks or Probabilistic Soft Logic.
Other, non-probabilistic extensions to first-order logic to assistance were likewise tried. For instance, non-monotonic reasoning might be utilized with reality maintenance systems. A truth upkeep system tracked assumptions and validations for all inferences. It enabled reasonings to be withdrawn when assumptions were learnt to be inaccurate or a contradiction was derived. Explanations might be offered an inference by discussing which guidelines were applied to develop it and after that continuing through underlying inferences and guidelines all the way back to root assumptions. [58] Lofti Zadeh had presented a different kind of extension to deal with the representation of uncertainty. For example, in choosing how ”heavy” or ”high” a guy is, there is regularly no clear ”yes” or ”no” response, and a predicate for heavy or tall would instead return values between 0 and 1. Those values represented to what degree the predicates were true. His fuzzy logic even more supplied a method for propagating mixes of these values through logical solutions. [59]
Machine knowing
Symbolic device learning techniques were examined to deal with the knowledge acquisition traffic jam. One of the earliest is Meta-DENDRAL. Meta-DENDRAL utilized a generate-and-test technique to generate plausible rule hypotheses to check against spectra. Domain and job knowledge minimized the number of prospects evaluated to a manageable size. Feigenbaum described Meta-DENDRAL as
… the conclusion of my dream of the early to mid-1960s pertaining to theory formation. 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 guide and prune the search. That understanding acted due to the fact that we talked to individuals. But how did the individuals get the knowledge? By taking a look at countless spectra. So we wanted a program that would look at thousands of spectra and presume the knowledge of mass spectrometry that DENDRAL could utilize to fix private hypothesis development problems. We did it. We were even able to publish brand-new knowledge of mass spectrometry in the Journal of the American Chemical Society, offering credit just in a footnote that a program, Meta-DENDRAL, really did it. We were able to do something that had actually been a dream: to have a computer program come up with a brand-new and publishable piece of science. [51]
In contrast to the knowledge-intensive method of Meta-DENDRAL, Ross Quinlan invented a domain-independent method to analytical category, choice tree knowing, beginning initially with ID3 [60] and after that later on extending its abilities to C4.5. [61] The decision trees developed are glass box, interpretable classifiers, with human-interpretable category guidelines.
Advances were made in comprehending artificial intelligence theory, too. Tom Mitchell presented variation area knowing which describes learning as an explore an area of hypotheses, with upper, more general, and lower, more particular, limits including all feasible hypotheses consistent with the examples seen up until now. [62] More formally, Valiant presented Probably Approximately Correct Learning (PAC Learning), a framework for the mathematical analysis of artificial intelligence. [63]
Symbolic machine discovering encompassed more than finding out by example. E.g., John Anderson supplied a cognitive model of human learning where skill practice leads to a compilation of guidelines from a declarative format to a procedural format with his ACT-R cognitive architecture. For instance, a student may learn to apply ”Supplementary angles are two angles whose steps sum 180 degrees” as numerous various procedural guidelines. E.g., one rule may say that if X and Y are additional and you know X, then Y will be 180 – X. He called his technique ”understanding collection”. ACT-R has actually been used effectively to design aspects of human cognition, such as discovering and retention. ACT-R is also used in smart tutoring systems, called cognitive tutors, to successfully teach geometry, computer system shows, and algebra to school children. [64]
Inductive logic programs was another technique to discovering that enabled reasoning programs to be manufactured from input-output examples. E.g., Ehud Shapiro’s MIS (Model Inference System) could manufacture Prolog programs from examples. [65] John R. Koza applied genetic algorithms to program synthesis to produce hereditary programs, which he utilized to synthesize LISP programs. Finally, Zohar Manna and Richard Waldinger provided a more general technique to program synthesis that synthesizes a practical program in the course of proving its specs to be correct. [66]
As an option to reasoning, Roger Schank introduced case-based reasoning (CBR). The CBR technique described in his book, Dynamic Memory, [67] focuses initially on keeping in mind key analytical cases for future use and generalizing them where proper. When confronted with a brand-new problem, CBR recovers the most similar previous case and adapts it to the specifics of the present problem. [68] Another option to reasoning, hereditary algorithms and genetic programming are based upon an evolutionary design of learning, where sets of rules are encoded into populations, the rules govern the behavior of individuals, and selection of the fittest prunes out sets of unsuitable rules over numerous generations. [69]
Symbolic artificial intelligence was used to discovering concepts, guidelines, heuristics, and analytical. Approaches, aside from those above, include:
1. Learning from guideline or advice-i.e., taking human direction, impersonated recommendations, and figuring out how to operationalize it in specific scenarios. For instance, in a video game of Hearts, learning precisely how to play a hand to ”prevent taking points.” [70] 2. Learning from exemplars-improving performance by accepting subject-matter professional (SME) feedback throughout training. When analytical stops working, querying the specialist to either learn a brand-new prototype for problem-solving or to learn a brand-new description as to precisely why one prototype is more pertinent than another. For instance, the program Protos discovered to detect ringing in the ears cases by engaging with an audiologist. [71] 3. Learning by analogy-constructing problem options based on similar problems seen in the past, and then customizing their solutions to fit a new scenario or domain. [72] [73] 4. Apprentice knowing systems-learning novel solutions to issues by observing human analytical. Domain knowledge describes why novel services are right and how the option can be generalized. LEAP found out how to develop VLSI circuits by observing human designers. [74] 5. Learning by discovery-i.e., developing jobs to bring out experiments and then learning from the outcomes. Doug Lenat’s Eurisko, for example, found out 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 helpful macro-operators to be gained from series of basic analytical actions. Good macro-operators streamline problem-solving by permitting 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 technique has actually been compared to deep knowing as complementary ”… with parallels having actually been drawn sometimes by AI scientists between Kahneman’s research study on human thinking and 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 knowing and symbolic reasoning, respectively.” In this view, symbolic reasoning is more apt for deliberative reasoning, planning, and explanation while deep learning is more apt for quick pattern recognition in perceptual applications with noisy information. [17] [18]
Neuro-symbolic AI: integrating neural and symbolic approaches
Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a way that addresses strengths and weak points of each, in a complementary style, in order to support robust AI capable of reasoning, learning, and cognitive modeling. As argued by Valiant [77] and numerous others, [78] the efficient building of abundant computational cognitive models requires the combination of sound symbolic reasoning and effective (maker) knowing designs. Gary Marcus, similarly, argues that: ”We can not build rich cognitive designs in an appropriate, automated method without the triune of hybrid architecture, abundant anticipation, and advanced techniques for thinking.”, [79] and in particular: ”To construct a robust, knowledge-driven technique to AI we should have the machinery of symbol-manipulation in our toolkit. Excessive of helpful understanding is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge dependably is the device of sign manipulation. ” [80]
Henry Kautz, [19] Francesca Rossi, [81] and Bart Selman [82] have also argued for a synthesis. Their arguments are based upon a need to deal with the two type of thinking talked about in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is fast, automatic, intuitive and unconscious. System 2 is slower, detailed, and explicit. System 1 is the kind utilized for pattern recognition while System 2 is far much better suited for planning, reduction, and deliberative thinking. In this view, deep learning finest designs the very first type of believing while symbolic thinking best models the second kind and both are needed.
Garcez and Lamb describe research in this location as being continuous for a minimum of the previous twenty years, [83] dating from their 2002 book on neurosymbolic learning systems. [84] A series of workshops on neuro-symbolic reasoning has been held every year since 2005, see http://www.neural-symbolic.org/ for details.
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
The integration of the symbolic and connectionist paradigms of AI has actually been pursued by a relatively little research study community over the last twenty years and has yielded several significant outcomes. Over the last decade, neural symbolic systems have been revealed efficient in conquering the so-called propositional fixation of neural networks, as McCarthy (1988) put it in reaction to Smolensky (1988 ); see also (Hinton, 1990). Neural networks were revealed efficient in representing modal and temporal logics (d’Avila Garcez and Lamb, 2006) and pieces of first-order logic (Bader, Hitzler, Hölldobler, 2008; d’Avila Garcez, Lamb, Gabbay, 2009). Further, neural-symbolic systems have been applied to a number of issues in the areas of bioinformatics, control engineering, software verification and adjustment, visual intelligence, ontology knowing, and computer system video games. [78]
Approaches for integration are differed. Henry Kautz’s taxonomy of neuro-symbolic architectures, in addition to some examples, follows:
– Symbolic Neural symbolic-is the current technique of lots of neural designs in natural language processing, where words or subword tokens are both the supreme input and output of big language designs. Examples consist of BERT, RoBERTa, and GPT-3.
– Symbolic [Neural] -is exhibited by AlphaGo, where symbolic techniques are utilized to call neural techniques. In this case the symbolic method is Monte Carlo tree search and the neural methods find out how to assess video game positions.
– Neural|Symbolic-uses a neural architecture to translate affective data as symbols and relationships that are then reasoned about symbolically.
– Neural: Symbolic → Neural-relies on symbolic thinking to produce or identify training information that is consequently discovered by a deep learning model, e.g., to train a neural model for symbolic computation by using a Macsyma-like symbolic mathematics system to create or label examples.
– Neural _ Symbolic -uses a neural web that is created from symbolic rules. An example is the Neural Theorem Prover, [85] which constructs a neural network from an AND-OR proof tree generated from knowledge base rules and terms. Logic Tensor Networks [86] likewise fall into this classification.
– Neural [Symbolic] -enables a neural model to directly call a symbolic reasoning engine, e.g., to carry out an action or examine a state.
Many key research concerns remain, such as:
– What is the finest method to integrate neural and symbolic architectures? [87]- How should symbolic structures be represented within neural networks and extracted from them?
– How should sensible understanding be discovered and reasoned about?
– How can abstract knowledge that is difficult to encode realistically be dealt with?
Techniques and contributions
This area offers an overview of techniques and contributions in a general context leading to many other, more in-depth articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section.
AI shows languages
The key AI programming language in the US throughout the last symbolic AI boom period was LISP. LISP is the 2nd earliest programming language after FORTRAN and was developed in 1958 by John McCarthy. LISP provided 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 also supplied, along with the ability to change worths or functions and continue from breakpoints or mistakes. It had the very first self-hosting compiler, suggesting that the compiler itself was originally written in LISP and after that ran interpretively to assemble the compiler code.
Other crucial developments originated by LISP that have actually spread out to other programming languages include:
Garbage collection
Dynamic typing
Higher-order functions
Recursion
Conditionals
Programs were themselves data structures that other programs could run on, allowing the easy meaning of higher-level languages.
In contrast to the US, in Europe the crucial AI programs language during that same period was Prolog. Prolog supplied a built-in store of realities and clauses that could 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 reasoning Prolog was based on Horn provisions with a closed-world assumption-any realities not understood were considered false-and an unique name assumption for primitive terms-e.g., the identifier barack_obama was thought about to refer to exactly one things. Backtracking and unification are built-in to Prolog.
Alain Colmerauer and Philippe Roussel are credited as the developers of Prolog. Prolog is a type of logic shows, which was created by Robert Kowalski. Its history was likewise affected by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of approaches. For more information see the area on the origins of Prolog in the PLANNER short article.
Prolog is likewise a kind of declarative shows. The logic provisions that explain programs are directly analyzed to run the programs defined. No explicit series of actions is required, as is the case with important programs languages.
Japan promoted Prolog for its Fifth Generation Project, intending to construct unique hardware for high efficiency. Similarly, LISP makers were built to run LISP, however as the second AI boom turned to bust these companies might not take on new workstations that could now run LISP or Prolog natively at similar speeds. See the history section for more detail.
Smalltalk was another prominent AI programming language. For example, it presented metaclasses and, along with Flavors and CommonLoops, influenced the Common Lisp Object System, or (CLOS), that is now part of Common Lisp, the current basic Lisp dialect. CLOS is a Lisp-based object-oriented system that permits multiple inheritance, in addition to incremental extensions to both classes and metaclasses, thus supplying a run-time meta-object protocol. [88]
For other AI programs 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 substantial plan library that supports data science, natural language processing, and deep knowing. Python includes a read-eval-print loop, functional aspects such as higher-order functions, and object-oriented programs that consists of metaclasses.
Search
Search develops in numerous sort of problem fixing, consisting of preparation, restraint complete satisfaction, and playing games such as checkers, chess, and go. The very best understood AI-search tree search algorithms are breadth-first search, depth-first search, A *, and Monte Carlo Search. Key search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause learning, and the DPLL algorithm. For adversarial search when playing games, alpha-beta pruning, branch and bound, and minimax were early contributions.
Knowledge representation and thinking
Multiple various approaches to represent understanding and then factor with those representations have been examined. Below is a fast introduction of methods to knowledge representation and automated reasoning.
Knowledge representation
Semantic networks, conceptual graphs, frames, and reasoning are all approaches to modeling understanding such as domain knowledge, analytical understanding, and the semantic significance of language. Ontologies model essential ideas and their relationships in a domain. Example ontologies are YAGO, WordNet, and DOLCE. DOLCE is an example of an upper ontology that can be utilized for any domain while WordNet is a lexical resource that can also be considered as an ontology. YAGO incorporates WordNet as part of its ontology, to align truths extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology presently being used.
Description logic is a logic for automated category of ontologies and for identifying irregular classification information. OWL is a language used to represent ontologies with description logic. Protégé is an ontology editor that can read in OWL ontologies and after that inspect consistency with deductive classifiers such as such as HermiT. [89]
First-order logic is more general than description logic. The automated theorem provers gone over listed below can prove theorems in first-order logic. Horn provision logic is more restricted than first-order logic and is used in reasoning programming languages such as Prolog. Extensions to first-order reasoning consist of temporal reasoning, to manage time; epistemic logic, to reason about representative understanding; modal logic, to manage possibility and necessity; and probabilistic reasonings to deal with logic and probability together.
Automatic theorem showing
Examples of automated theorem provers for first-order reasoning are:
Prover9.
ACL2.
Vampire.
Prover9 can be used in conjunction with the Mace4 model checker. ACL2 is a theorem prover that can manage proofs by induction and is a descendant of the Boyer-Moore Theorem Prover, likewise called Nqthm.
Reasoning in knowledge-based systems
Knowledge-based systems have a specific understanding base, usually of guidelines, to boost reusability across domains by separating procedural code and domain knowledge. A separate reasoning engine processes rules and adds, deletes, or modifies a knowledge store.
Forward chaining reasoning engines are the most typical, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more restricted logical representation is utilized, Horn Clauses. Pattern-matching, specifically marriage, is used in Prolog.
A more versatile type of problem-solving happens when reasoning about what to do next occurs, instead of just choosing 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 capabilities, such as the capability to compile regularly utilized understanding into higher-level pieces.
Commonsense reasoning
Marvin Minsky first proposed frames as a method of translating typical visual circumstances, such as a workplace, and Roger Schank extended this idea to scripts for common regimens, such as dining out. Cyc has attempted to capture helpful sensible knowledge and has ”micro-theories” to manage specific type of domain-specific reasoning.
Qualitative simulation, such as Benjamin Kuipers’s QSIM, [90] estimates human reasoning about naive physics, such as what happens when we warm a liquid in a pot on the range. We expect it to heat and potentially boil over, even though we may not understand 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 restraint solvers.
Constraints and constraint-based thinking
Constraint solvers perform a more minimal kind of reasoning than first-order reasoning. They can simplify sets of spatiotemporal restraints, such as those for RCC or Temporal Algebra, along with solving other type of puzzle problems, such as Wordle, Sudoku, cryptarithmetic issues, and so on. Constraint reasoning programs can be utilized to solve scheduling problems, for example with restriction dealing with rules (CHR).
Automated planning
The General Problem Solver (GPS) cast preparation as analytical utilized means-ends analysis to produce plans. STRIPS took a various approach, viewing planning as theorem proving. Graphplan takes a least-commitment technique to preparation, instead of sequentially picking actions from an initial state, working forwards, or an objective state if working in reverse. Satplan is a method to preparing where a planning problem is lowered to a Boolean satisfiability problem.
Natural language processing
Natural language processing concentrates on treating language as information to perform tasks such as recognizing topics without always understanding the intended meaning. Natural language understanding, on the other hand, constructs a significance representation and uses that for more processing, such as addressing concerns.
Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb expression chunking are all aspects of natural language processing long managed by symbolic AI, however since improved by deep learning techniques. In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis likewise supplied vector representations of documents. In the latter case, vector parts are interpretable as ideas named by Wikipedia posts.
New deep learning techniques based upon Transformer designs have now eclipsed these earlier symbolic AI methods and obtained advanced efficiency 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 meaning of the vector elements is opaque.
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 standard textbook on artificial intelligence is organized to show agent architectures of increasing elegance. [91] The sophistication of agents differs from basic reactive representatives, to those with a design of the world and automated preparation abilities, potentially a BDI representative, i.e., one with beliefs, desires, and objectives – or alternatively a support learning model learned with time to pick actions – up to a combination of alternative architectures, such as a neuro-symbolic architecture [87] that includes deep knowing for understanding. [92]
In contrast, a multi-agent system consists of numerous representatives that communicate among themselves with some inter-agent interaction language such as Knowledge Query and Manipulation Language (KQML). The agents need not all have the very same internal architecture. Advantages of multi-agent systems consist of the ability to divide work amongst the representatives and to increase fault tolerance when agents are lost. Research issues consist of how agents reach consensus, dispersed issue resolving, multi-agent learning, multi-agent preparation, and distributed restraint 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 rejected symbolic approaches-primarily connectionists-and those outside the field. Critiques from beyond the field were mainly from thinkers, on intellectual grounds, however also from financing agencies, specifically throughout the 2 AI winters.
The Frame Problem: knowledge representation difficulties for first-order logic
Limitations were discovered in using basic first-order reasoning to factor about vibrant domains. Problems were discovered both with regards to mentioning the preconditions for an action to be successful and in offering axioms for what did not alter after an action was performed.
McCarthy and Hayes presented the Frame Problem in 1969 in the paper, ”Some Philosophical Problems from the Standpoint of Expert System.” [93] A basic example happens in ”proving that one person could enter 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 be successful. Similar axioms would be needed for other domain actions to specify what did not change.
A comparable problem, called the Qualification Problem, takes place in trying to mention the prerequisites for an action to succeed. A boundless variety of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a vehicle from operating properly.
McCarthy’s method to repair the frame issue was circumscription, a kind of non-monotonic reasoning where deductions could be made from actions that need only define what would change while not having to explicitly specify whatever that would not alter. Other non-monotonic logics provided reality upkeep systems that modified beliefs causing contradictions.
Other methods of handling more open-ended domains included probabilistic reasoning systems and device learning to learn new principles and guidelines. McCarthy’s Advice Taker can be deemed an inspiration here, as it could integrate brand-new understanding supplied by a human in the kind of assertions or rules. For instance, speculative symbolic maker finding out systems checked out the capability to take top-level natural language recommendations and to translate it into domain-specific actionable guidelines.
Similar to the issues in handling dynamic domains, common-sense reasoning is likewise hard to record in official thinking. Examples of common-sense reasoning consist of implicit thinking about how individuals think or general understanding of day-to-day events, things, and living creatures. This type of understanding is taken for given and not viewed as noteworthy. Common-sense thinking is an open location of research and challenging both for symbolic systems (e.g., Cyc has actually attempted to record key parts of this knowledge over more than a decade) and neural systems (e.g., self-driving cars and trucks that do not understand not to drive into cones or not to strike pedestrians strolling a bicycle).
McCarthy viewed his Advice Taker as having sensible, but his definition of common-sense was various than the one above. [94] He defined a program as having good sense ”if it instantly deduces for itself a sufficiently large class of instant repercussions of anything it is informed and what it already knows. ”
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 advanced methods, such as Transformers, GANs, and other work in deep learning.
Three philosophical positions [96] have been described among connectionists:
1. Implementationism-where connectionist architectures implement the abilities for symbolic processing,
2. Radical connectionism-where symbolic processing is declined totally, and connectionist architectures underlie intelligence and are completely adequate to describe it,
3. Moderate connectionism-where symbolic processing and connectionist architectures are deemed complementary and both are needed for intelligence
Olazaran, in his sociological history of the controversies within the neural network neighborhood, described the moderate connectionism consider as essentially suitable with present research in neuro-symbolic hybrids:
The third and last position I want to examine here is what I call the moderate connectionist view, a more eclectic view of the present debate between connectionism and symbolic AI. Among the scientists who has actually elaborated this position most explicitly is Andy Clark, a theorist from the School of Cognitive and Computing Sciences of the University of Sussex (Brighton, England). Clark protected hybrid (partly symbolic, partly connectionist) systems. He declared that (at least) two kinds 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 processes (such as serial, deductive thinking, and generative symbol adjustment processes) the symbolic paradigm offers sufficient designs, and not only ”approximations” (contrary to what radical connectionists would declare). [97]
Gary Marcus has actually declared that the animus in the deep learning community versus symbolic approaches now may be more sociological than philosophical:
To think that we can simply desert symbol-manipulation is to suspend disbelief.
And yet, for the many part, that’s how most existing AI proceeds. Hinton and lots of others have actually striven to get rid of symbols completely. The deep knowing hope-seemingly grounded not so much in science, but in a sort of historical grudge-is that intelligent behavior will emerge purely from the confluence of huge information and deep knowing. Where classical computers and software application fix tasks by defining sets of symbol-manipulating rules dedicated to particular tasks, such as modifying a line in a word processor or carrying out a computation in a spreadsheet, neural networks normally try to solve tasks by statistical approximation and gaining from examples.
According to Marcus, Geoffrey Hinton and his coworkers have been vehemently ”anti-symbolic”:
When deep knowing reemerged in 2012, it was with a type of take-no-prisoners mindset that has defined the majority of the last decade. By 2015, his hostility toward all things signs had actually totally taken shape. He offered a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s biggest errors.
…
Ever since, his anti-symbolic campaign has actually only increased in strength. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in among science’s essential journals, Nature. It closed with a direct attack on sign control, calling not for reconciliation but for straight-out replacement. Later, Hinton informed a gathering of European Union leaders that investing any additional money in symbol-manipulating approaches was ”a big error,” likening it to investing in internal combustion engines in the age of electrical vehicles. [98]
Part of these disagreements might be due to unclear terminology:
Turing award winner Judea Pearl uses a review of maker learning which, sadly, conflates the terms maker learning and deep learning. Similarly, when Geoffrey Hinton refers to symbolic AI, the undertone of the term tends to be that of specialist systems dispossessed of any capability to discover. The usage of the terminology is in requirement of explanation. Machine learning is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the choice of representation, localist sensible rather than distributed, and the non-use of gradient-based learning algorithms). Equally, symbolic AI is not almost production guidelines written by hand. A correct definition of AI issues knowledge representation and thinking, self-governing multi-agent systems, preparation and argumentation, along with learning. [99]
Situated robotics: the world as a design
Another critique 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 need to study the system as a whole; the brain’s operating exploits consistencies in its environment, consisting of the rest of its body. Under the embodied cognition approach, robotics, vision, and other sensors become central, not peripheral. [100]
Rodney Brooks invented behavior-based robotics, one method to embodied cognition. Nouvelle AI, another name for this approach, is deemed an alternative to both symbolic AI and connectionist AI. His technique declined representations, either symbolic or dispersed, as not just unnecessary, but as detrimental. Instead, he developed the subsumption architecture, a layered architecture for embodied representatives. Each layer achieves a various function and should work in the genuine world. For instance, the first robot he describes in Intelligence Without Representation, has three layers. The bottom layer translates finder sensors to avoid items. The middle layer causes the robotic to roam around when there are no challenges. The top layer triggers the robot to go to more remote places for additional exploration. Each layer can momentarily prevent or suppress a lower-level layer. He criticized AI scientists for specifying AI problems for their systems, when: ”There is no clean division between understanding (abstraction) and reasoning in the genuine world.” [101] He called his robots ”Creatures” and each layer was ”composed of a fixed-topology network of simple finite state devices.” [102] In the Nouvelle AI technique, ”First, it is essential to evaluate the Creatures we integrate in the real world; i.e., in the very same world that we humans live in. It is dreadful to fall under the temptation of checking them in a simplified world first, even with the finest intents of later transferring activity to an unsimplified world.” [103] His focus on real-world screening remained in contrast to ”Early work in AI concentrated on video games, geometrical problems, symbolic algebra, theorem proving, and other official systems” [104] and making use of the blocks world in symbolic AI systems such as SHRDLU.
Current views
Each approach-symbolic, connectionist, and behavior-based-has advantages, however has been criticized by the other techniques. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in managing the affective issues where deep finding out excels. In turn, connectionist AI has been slammed as inadequately suited for deliberative detailed issue resolving, integrating understanding, and dealing with planning. Finally, Nouvelle AI masters reactive and real-world robotics domains but has actually been slammed for problems in integrating learning and understanding.
Hybrid AIs integrating one or more of these methods are currently considered as the course forward. [19] [81] [82] Russell and Norvig conclude that:
Overall, Dreyfus saw areas where AI did not have complete answers and stated that Al is therefore difficult; we now see a lot of these very same locations going through continued research study and advancement resulting in increased capability, not impossibility. [100]
Artificial intelligence.
Automated planning and scheduling
Automated theorem proving
Belief modification
Case-based reasoning
Cognitive architecture
Cognitive science
Connectionism
Constraint shows
Deep knowing
First-order logic
GOFAI
History of expert system
Inductive reasoning shows
Knowledge-based systems
Knowledge representation and reasoning
Logic programming
Machine knowing
Model monitoring
Model-based reasoning
Multi-agent system
Natural language processing
Neuro-symbolic AI
Ontology
Philosophy of artificial intelligence
Physical symbol systems hypothesis
Semantic Web
Sequential pattern mining
Statistical relational knowing
Symbolic mathematics
YAGO ontology
WordNet
Notes
^ McCarthy as soon as stated: ”This is AI, so we don’t care if it’s psychologically real”. [4] McCarthy restated his position in 2006 at the AI@50 conference where he stated ”Artificial intelligence is not, by definition, simulation of human intelligence”. [28] Pamela McCorduck writes that there are ”2 significant branches of expert system: one targeted at producing smart behavior despite how it was accomplished, and the other targeted at modeling smart processes discovered in nature, particularly human ones.”, [29] Stuart Russell and Peter Norvig wrote ”Aeronautical engineering texts do not define the objective of their field as making ’makers that fly so exactly like pigeons that they can fool even other pigeons.'” [30] Citations
^ Garnelo, Marta; Shanahan, Murray (October 2019). ”Reconciling deep learning with symbolic expert system: representing items and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796.
^ Thomason, Richmond (February 27, 2024). ”Logic-Based Expert System”. In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.
^ Garnelo, Marta; Shanahan, Murray (2019-10-01). ”Reconciling deep learning with symbolic expert system: representing things and relations”. Current Opinion in Behavioral Sciences. 29: 17-23. doi:10.1016/ j.cobeha.2018.12.010. hdl:10044/ 1/67796. S2CID 72336067.
^ a b Kolata 1982.
^ Kautz 2022, pp. 107-109.
^ a b Russell & Norvig 2021, p. 19.
^ a b Russell & Norvig 2021, pp. 22-23.
^ a b Kautz 2022, pp. 109-110.
^ a b c Kautz 2022, p. 110.
^ Kautz 2022, pp. 110-111.
^ a b Russell & Norvig 2021, p. 25.
^ Kautz 2022, p. 111.
^ Kautz 2020, pp. 110-111.
^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986 ). ”Learning representations by back-propagating mistakes”. Nature. 323 (6088 ): 533-536. Bibcode:1986 Natur.323..533 R. doi:10.1038/ 323533a0. ISSN 1476-4687. S2CID 205001834.
^ LeCun, Y.; Boser, B.; Denker, I.; Henderson, D.; Howard, R.; Hubbard, W.; Tackel, L. (1989 ). ”Backpropagation Applied to Handwritten Zip Code Recognition”. Neural Computation. 1 (4 ): 541-551. doi:10.1162/ neco.1989.1.4.541. S2CID 41312633.
^ a b Marcus & Davis 2019.
^ a b Rossi, Francesca. ”Thinking Fast and Slow in AI”. AAAI. Retrieved 5 July 2022.
^ a b Selman, Bart. ”AAAI Presidential Address: The State of AI”. AAAI. Retrieved 5 July 2022.
^ a b c Kautz 2020.
^ Kautz 2022, p. 106.
^ Newell & Simon 1972.
^ & McCorduck 2004, pp. 139-179, 245-250, 322-323 (EPAM).
^ Crevier 1993, pp. 145-149.
^ McCorduck 2004, pp. 450-451.
^ Crevier 1993, pp. 258-263.
^ a b Kautz 2022, p. 108.
^ Russell & Norvig 2021, p. 9 (logicist AI), p. 19 (McCarthy’s work).
^ Maker 2006.
^ McCorduck 2004, pp. 100-101.
^ Russell & Norvig 2021, p. 2.
^ McCorduck 2004, pp. 251-259.
^ Crevier 1993, pp. 193-196.
^ Howe 1994.
^ McCorduck 2004, pp. 259-305.
^ Crevier 1993, pp. 83-102, 163-176.
^ McCorduck 2004, pp. 421-424, 486-489.
^ Crevier 1993, p. 168.
^ McCorduck 2004, p. 489.
^ Crevier 1993, pp. 239-243.
^ Russell & Norvig 2021, p. 316, 340.
^ Kautz 2022, p. 109.
^ Russell & Norvig 2021, p. 22.
^ McCorduck 2004, pp. 266-276, 298-300, 314, 421.
^ Shustek, Len (June 2010). ”An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-07-14.
^ Lenat, Douglas B; Feigenbaum, Edward A (1988 ). ”On the thresholds of understanding”. Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications: 291-300. doi:10.1109/ AIIA.1988.13308. S2CID 11778085.
^ Russell & Norvig 2021, pp. 22-24.
^ McCorduck 2004, pp. 327-335, 434-435.
^ Crevier 1993, pp. 145-62, 197-203.
^ a b Russell & Norvig 2021, p. 23.
^ a b Clancey 1987.
^ a b Shustek, Len (2010 ). ”An interview with Ed Feigenbaum”. Communications of the ACM. 53 (6 ): 41-45. doi:10.1145/ 1743546.1743564. ISSN 0001-0782. S2CID 10239007. Retrieved 2022-08-05.
^ ”The fascination with AI: what is expert system?”. IONOS Digitalguide. Retrieved 2021-12-02.
^ Hayes-Roth, Murray & Adelman 2015.
^ Hayes-Roth, Barbara (1985 ). ”A blackboard architecture for control”. Artificial Intelligence. 26 (3 ): 251-321. doi:10.1016/ 0004-3702( 85 )90063-3.
^ Hayes-Roth, Barbara (1980 ). Human Planning Processes. RAND.
^ Pearl 1988.
^ Spiegelhalter et al. 1993.
^ Russell & Norvig 2021, pp. 335-337.
^ Russell & Norvig 2021, p. 459.
^ Quinlan, J. Ross. ”Chapter 15: Learning Efficient Classification Procedures and their Application to Chess End Games”. In Michalski, Carbonell & Mitchell (1983 ).
^ Quinlan, J. Ross (1992-10-15). C4.5: Programs for Artificial Intelligence (1st ed.). San Mateo, Calif: Morgan Kaufmann. ISBN 978-1-55860-238-0.
^ Mitchell, Tom M.; Utgoff, Paul E.; Banerji, Ranan. ”Chapter 6: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics”. In Michalski, Carbonell & Mitchell (1983 ).
^ Valiant, L. G. (1984-11-05). ”A theory of the learnable”. Communications of the ACM. 27 (11 ): 1134-1142. doi:10.1145/ 1968.1972. ISSN 0001-0782. S2CID 12837541.
^ Koedinger, K. R.; Anderson, J. R.; Hadley, W. H.; Mark, M. A.; others (1997 ). ”Intelligent tutoring goes to school in the big city”. International Journal of Expert System in Education (IJAIED). 8: 30-43. Retrieved 2012-08-18.
^ Shapiro, Ehud Y (1981 ). ”The Model Inference System”. Proceedings of the 7th global joint conference on Expert system. IJCAI. Vol. 2. p. 1064.
^ Manna, Zohar; Waldinger, Richard (1980-01-01). ”A Deductive Approach to Program Synthesis”. ACM Trans. Program. Lang. Syst. 2 (1 ): 90-121. doi:10.1145/ 357084.357090. S2CID 14770735.
^ Schank, Roger C. (1983-01-28). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge Cambridgeshire: New York: Cambridge University Press. ISBN 978-0-521-27029-8.
^ Hammond, Kristian J. (1989-04-11). Case-Based Planning: Viewing Planning as a Memory Task. Boston: Academic Press. ISBN 978-0-12-322060-8.
^ Koza, John R. (1992-12-11). Genetic Programming: On the Programming of Computers by Means of Natural Selection (1st ed.). Cambridge, Mass: A Bradford Book. ISBN 978-0-262-11170-6.
^ Mostow, David Jack. ”Chapter 12: Machine Transformation of Advice into a Heuristic Search Procedure”. In Michalski, Carbonell & Mitchell (1983 ).
^ Bareiss, Ray; Porter, Bruce; Wier, Craig. ”Chapter 4: Protos: An Exemplar-Based Learning Apprentice”. In Michalski, Carbonell & Mitchell (1986 ), pp. 112-139.
^ Carbonell, Jaime. ”Chapter 5: Learning by Analogy: Formulating and Generalizing Plans from Past Experience”. In Michalski, Carbonell & Mitchell (1983 ), pp. 137-162.
^ Carbonell, Jaime. ”Chapter 14: Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition”. In Michalski, Carbonell & Mitchell (1986 ), pp. 371-392.
^ Mitchell, Tom; Mabadevan, Sridbar; Steinberg, Louis. ”Chapter 10: LEAP: A Knowing Apprentice for VLSI Design”. In Kodratoff & Michalski (1990 ), pp. 271-289.
^ Lenat, Douglas. ”Chapter 9: The Role of Heuristics in Learning by Discovery: Three Case Studies”. In Michalski, Carbonell & Mitchell (1983 ), pp. 243-306.
^ Korf, Richard E. (1985 ). Learning to Solve Problems by Searching for Macro-Operators. Research Notes in Expert System. Pitman Publishing. ISBN 0-273-08690-1.
^ Valiant 2008.
^ a b Garcez et al. 2015.
^ Marcus 2020, p. 44.
^ Marcus 2020, p. 17.
^ a b Rossi 2022.
^ a b Selman 2022.
^ Garcez & Lamb 2020, p. 2.
^ Garcez et al. 2002.
^ Rocktäschel, Tim; Riedel, Sebastian (2016 ). ”Learning Knowledge Base Inference with Neural Theorem Provers”. Proceedings of the 5th Workshop on Automated Knowledge Base Construction. San Diego, CA: Association for Computational Linguistics. pp. 45-50. doi:10.18653/ v1/W16 -1309. Retrieved 2022-08-06.
^ Serafini, Luciano; Garcez, Artur d’Avila (2016 ), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
^ a b Garcez, Artur d’Avila; Lamb, Luis C.; Gabbay, Dov M. (2009 ). Neural-Symbolic Cognitive Reasoning (1st ed.). Berlin-Heidelberg: Springer. Bibcode:2009 nscr.book … D. doi:10.1007/ 978-3-540-73246-4. ISBN 978-3-540-73245-7. S2CID 14002173.
^ Kiczales, Gregor; Rivieres, Jim des; Bobrow, Daniel G. (1991-07-30). The Art of the Metaobject Protocol (1st ed.). Cambridge, Mass: The MIT Press. ISBN 978-0-262-61074-2.
^ Motik, Boris; Shearer, Rob; Horrocks, Ian (2009-10-28). ”Hypertableau Reasoning for Description Logics”. Journal of Artificial Intelligence Research. 36: 165-228. arXiv:1401.3485. doi:10.1613/ jair.2811. ISSN 1076-9757. S2CID 190609.
^ Kuipers, Benjamin (1994 ). Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press. ISBN 978-0-262-51540-5.
^ Russell & Norvig 2021.
^ Leo de Penning, Artur S. d’Avila Garcez, LuĂs C. Lamb, John-Jules Ch. Meyer: ”A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning.” IJCAI 2011: 1653-1658.
^ McCarthy & Hayes 1969.
^ McCarthy 1959.
^ Nilsson 1998, p. 7.
^ Olazaran 1993, pp. 411-416.
^ Olazaran 1993, pp. 415-416.
^ Marcus 2020, p. 20.
^ Garcez & Lamb 2020, p. 8.
^ a b Russell & Norvig 2021, p. 982.
^ Brooks 1991, p. 143.
^ Brooks 1991, p. 151.
^ Brooks 1991, p. 150.
^ Brooks 1991, p. 142.
References
Brooks, Rodney A. (1991 ). ”Intelligence without representation”. Expert system. 47 (1 ): 139-159. doi:10.1016/ 0004-3702( 91 )90053-M. ISSN 0004-3702. S2CID 207507849. Retrieved 2022-09-13.
Clancey, William (1987 ). Knowledge-Based Tutoring: The GUIDON Program (MIT Press Series in Artificial Intelligence) (Hardcover ed.).
Crevier, Daniel (1993 ). AI: The Tumultuous Look For Expert System. New York City, NY: BasicBooks. ISBN 0-465-02997-3.
Dreyfus, Hubert L (1981 ). ”From micro-worlds to knowledge representation: AI at an impasse” (PDF). Mind Design. MIT Press, Cambridge, MA: 161-204.
Garcez, Artur S. d’Avila; Broda, Krysia; Gabbay, Dov M.; Gabbay, Augustus de Morgan Professor of Logic Dov M. (2002 ). Neural-Symbolic Learning Systems: Foundations and Applications. Springer Science & Business Media. ISBN 978-1-85233-512-0.
Garcez, Artur; Besold, Tarek; De Raedt, Luc; Földiák, Peter; Hitzler, Pascal; Icard, Thomas; KĂĽhnberger, Kai-Uwe; Lamb, LuĂs; Miikkulainen, Risto; Silver, Daniel (2015 ). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. AAI Spring Symposium – Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches. Stanford, CA: AAAI Press. doi:10.13140/ 2.1.1779.4243.
Garcez, Artur d’Avila; Gori, Marco; Lamb, Luis C.; Serafini, Luciano; Spranger, Michael; Tran, Son N. (2019 ), Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning, arXiv:1905.06088.
Garcez, Artur d’Avila; Lamb, Luis C. (2020 ), Neurosymbolic AI: The 3rd Wave, arXiv:2012.05876.
Haugeland, John (1985 ), Artificial Intelligence: The Very Idea, Cambridge, Mass: MIT Press, ISBN 0-262-08153-9.
Hayes-Roth, Frederick; Murray, William; Adelman, Leonard (2015 ). ”Expert systems”. AccessScience. doi:10.1036/ 1097-8542.248550.
Honavar, Vasant; Uhr, Leonard (1994 ). Symbolic Artificial Intelligence, Connectionist Networks & Beyond (Technical report). Iowa State University Digital Repository, Computer Science Technical Reports. 76. p. 6.
Honavar, Vasant (1995 ). Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy. The Springer International Series In Engineering and Computer Science. Springer US. pp. 351-388. doi:10.1007/ 978-0-585-29599-2_11.
Howe, J. (November 1994). ”Artificial Intelligence at Edinburgh University: a Viewpoint”. Archived from the initial on 15 May 2007. Retrieved 30 August 2007.
Kautz, Henry (2020-02-11). The Third AI Summer, Henry Kautz, AAAI 2020 Robert S. Engelmore Memorial Award Lecture. Retrieved 2022-07-06.
Kautz, Henry (2022 ). ”The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture”. AI Magazine. 43 (1 ): 93-104. doi:10.1609/ aimag.v43i1.19122. ISSN 2371-9621. S2CID 248213051. Retrieved 2022-07-12.
Kodratoff, Yves; Michalski, Ryszard, eds. (1990 ). Machine Learning: an Expert System Approach. Vol. III. San Mateo, Calif.: Morgan Kaufman. ISBN 0-934613-09-5. OCLC 893488404.
Kolata, G. (1982 ). ”How can computer systems get common sense?”. Science. 217 (4566 ): 1237-1238. Bibcode:1982 Sci … 217.1237 K. doi:10.1126/ science.217.4566.1237. PMID 17837639.
Maker, Meg Houston (2006 ). ”AI@50: AI Past, Present, Future”. Dartmouth College. Archived from the original on 3 January 2007. Retrieved 16 October 2008.
Marcus, Gary; Davis, Ernest (2019 ). Rebooting AI: Building Expert System We Can Trust. New York City: Pantheon Books. ISBN 9781524748258. OCLC 1083223029.
Marcus, Gary (2020 ), The Next Decade in AI: Four Steps Towards Robust Expert system, arXiv:2002.06177.
McCarthy, John (1959 ). PROGRAMS WITH COMMON SENSE. Symposium on Mechanization of Thought Processes. NATIONAL PHYSICAL LABORATORY, TEDDINGTON, UK. p. 8.
McCarthy, John; Hayes, Patrick (1969 ). ”Some Philosophical Problems From the Standpoint of Artificial Intelligence”. Machine Intelligence 4. B. Meltzer, Donald Michie (eds.): 463-502.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1983 ). Artificial intelligence: an Artificial Intelligence Approach. Vol. I. Palo Alto, Calif.: Tioga Publishing Company. ISBN 0-935382-05-4. OCLC 9262069.
Michalski, Ryszard; Carbonell, Jaime; Mitchell, Tom, eds. (1986 ). Artificial intelligence: an Artificial Intelligence Approach. Vol. II. Los Altos, Calif.: Morgan Kaufman. ISBN 0-934613-00-1.
Newell, Allen; Simon, Herbert A. (1972 ). Human Problem Solving (1st ed.). Englewood Cliffs, New Jersey: Prentice Hall. ISBN 0-13-445403-0.
Newell, Allen; Simon, H. A. (1976 ). ”Computer Science as Empirical Inquiry: Symbols and Search”. Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.
Olazaran, Mikel (1993-01-01), ”A Sociological History of the Neural Network Controversy”, in Yovits, Marshall C. (ed.), Advances in Computers Volume 37, vol. 37, Elsevier, pp. 335-425, doi:10.1016/ S0065-2458( 08 )60408-8, ISBN 9780120121373, retrieved 2023-10-31.
Pearl, J. (1988 ). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California: Morgan Kaufmann. ISBN 978-1-55860-479-7. OCLC 249625842.
Russell, Stuart J.; Norvig, Peter (2021 ). Expert system: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-13-461099-3. LCCN 20190474.
Rossi, Francesca (2022-07-06). ”AAAI2022: Thinking Fast and Slow in AI (AAAI 2022 Invited Talk)”. Retrieved 2022-07-06.
Selman, Bart (2022-07-06). ”AAAI2022: Presidential Address: The State of AI”. Retrieved 2022-07-06.
Serafini, Luciano; Garcez, Artur d’Avila (2016-07-07), Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge, arXiv:1606.04422.
Spiegelhalter, David J.; Dawid, A. Philip; Lauritzen, Steffen; Cowell, Robert G. (1993 ). ”Bayesian analysis in specialist systems”. Statistical Science. 8 (3 ).
Turing, A. M. (1950 ). ”I.-Computing Machinery and Intelligence”. Mind. LIX (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 0026-4423. Retrieved 2022-09-14.
Valiant, Leslie G (2008 ). ”Knowledge Infusion: In Pursuit of Robustness in Expert System”. In Hariharan, R.; Mukund, M.; Vinay, V. (eds.). Foundations of Software Technology and Theoretical Computer Technology (Bangalore). pp. 415-422.
Xifan Yao; Jiajun Zhou; Jiangming Zhang; Claudio R. Boer (2017 ). From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Artificial Intelligence and Further On.