Knowledge representation and reasoning

137: 708:. KL-ONE and languages that were influenced by it such as Loom had an automated reasoning engine that was based on formal logic rather than on IF-THEN rules. This reasoner is called the classifier. A classifier can analyze a set of declarations and infer new assertions, for example, redefine a class to be a subclass or superclass of some other class that wasn't formally specified. In this way the classifier can function as an inference engine, deducing new facts from an existing knowledge base. The classifier can also provide consistency checking on a knowledge base (which in the case of KL-ONE languages is also referred to as an Ontology). 948:, but there are also many special-purpose theorem-proving environments. These environments can validate logical models and can deduce new theories from existing models. Essentially they automate the process a logician would go through in analyzing a model. Theorem-proving technology had some specific practical applications in the areas of software engineering. For example, it is possible to prove that a software program rigidly adheres to a formal logical specification. 3242: 940:, was often used as a form of functional knowledge representation. Frames and Rules were the next kind of primitive. Frame languages had various mechanisms for expressing and enforcing constraints on frame data. All data in frames are stored in slots. Slots are analogous to relations in entity-relation modeling and to object properties in object-oriented modeling. Another technique for primitives is to define languages that are modeled after 3252: 3262: 899:. Languages based on the Frame model with automatic classification provide a layer of semantics on top of the existing Internet. Rather than searching via text strings as is typical today, it will be possible to define logical queries and find pages that map to those queries. The automated reasoning component in these systems is an engine known as the classifier. Classifiers focus on the 964:

all frames would be instances of a frame class. That class object can be inspected at run time, so that the object can understand and even change its internal structure or the structure of other parts of the model. In rule-based environments, the rules were also usually instances of rule classes. Part of the meta protocol for rules were the meta rules that prioritized rule firing.

715:. One of the first realizations learned from trying to make software that can function with human natural language was that humans regularly draw on an extensive foundation of knowledge about the real world that we simply take for granted but that is not at all obvious to an artificial agent. Basic principles of common-sense physics, causality, intentions, etc. An example is the 818:

even to understand knowledge expressed in complex, mathematically-oriented ways. Secondly, because of its complex proof procedures, it can be difficult for users to understand complex proofs and explanations, and it can be hard for implementations to be efficient. As a consequence, unrestricted FOL can be intimidating for many software developers.

970:. Traditional logic requires additional axioms and constraints to deal with the real world as opposed to the world of mathematics. Also, it is often useful to associate degrees of confidence with a statement. I.e., not simply say "Socrates is Human" but rather "Socrates is Human with confidence 50%". This was one of the early innovations from 680:. It also had a complete frame-based knowledge base with triggers, slots (data values), inheritance, and message passing. Although message passing originated in the object-oriented community rather than AI it was quickly embraced by AI researchers as well in environments such as KEE and in the operating systems for Lisp machines from 1010:. The standard that Brachman and most AI researchers use to measure expressive adequacy is usually First Order Logic (FOL). Theoretical limitations mean that a full implementation of FOL is not practical. Researchers should be clear about how expressive (how much of full FOL expressive power) they intend their representation to be. 1079:, "Every ontology is a treaty- a social agreement among people with common motive in sharing." There are always many competing and differing views that make any general-purpose ontology impossible. A general-purpose ontology would have to be applicable in any domain and different areas of knowledge need to be unified. 888:"It is a medium for pragmatically efficient computation", i.e., "the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information" so as "to facilitate making the recommended inferences." 822:

representation formalisms, from databases to semantic nets to production systems, can be viewed as making various design decisions about how to balance expressive power with naturalness of expression and efficiency. In particular, this balancing act was a driving motivation for the development of IF-THEN rules in

1102:) looks substantially different from the same task viewed in terms of frames (e.g., INTERNIST). Where MYCIN sees the medical world as made up of empirical associations connecting symptom to disease, INTERNIST sees a set of prototypes, in particular prototypical diseases, to be matched against the case at hand. 1051:

project. Cyc was an attempt to build a huge encyclopedic knowledge base that would contain not just expert knowledge but common-sense knowledge. In designing an artificial intelligence agent, it was soon realized that representing common-sense knowledge, knowledge that humans simply take for granted,

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that gives developers run time access to the class objects and enables them to dynamically redefine the structure of the knowledge base even at run time. Meta-representation means the knowledge representation language is itself expressed in that language. For example, in most Frame based environments

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The integration of frames, rules, and object-oriented programming was significantly driven by commercial ventures such as KEE and Symbolics spun off from various research projects. At the same time, there was another strain of research that was less commercially focused and was driven by mathematical

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As knowledge-based technology scaled up, the need for larger knowledge bases and for modular knowledge bases that could communicate and integrate with each other became apparent. This gave rise to the discipline of ontology engineering, designing and building large knowledge bases that could be used

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One of the key discoveries of AI research in the 1970s was that languages that do not have the full expressive power of FOL can still provide close to the same expressive power of FOL, but can be easier for both the average developer and for the computer to understand. Many of the early AI knowledge

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It was not long before the frame communities and the rule-based researchers realized that there was a synergy between their approaches. Frames were good for representing the real world, described as classes, subclasses, slots (data values) with various constraints on possible values. Rules were good

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The commitment made selecting one or another ontology can produce a sharply different view of the task at hand. Consider the difference that arises in selecting the lumped element view of a circuit rather than the electrodynamic view of the same device. As a second example, medical diagnosis viewed

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of concepts. Searching for a concept will be more effective than traditional text only searches. Frame languages and automatic classification play a big part in the vision for the future Semantic Web. The automatic classification gives developers technology to provide order on a constantly evolving

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The lumped element model, for instance, suggests that we think of circuits in terms of components with connections between them, with signals flowing instantaneously along the connections. This is a useful view, but not the only possible one. A different ontology arises if we need to attend to the

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the knowledge-bases were fairly small. The knowledge-bases that were meant to actually solve real problems rather than do proof of concept demonstrations needed to focus on well defined problems. So for example, not just medical diagnosis as a whole topic, but medical diagnosis of certain kinds of

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Arguably, FOL has two drawbacks as a knowledge representation formalism in its own right, namely ease of use and efficiency of implementation. Firstly, because of its high expressive power, FOL allows many ways of expressing the same information, and this can make it hard for users to formalise or

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Any mechanically embodied intelligent process will be comprised of structural ingredients that a) we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and b) independent of such external semantic attribution, play a formal

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In contrast, researchers at MIT rejected the resolution uniform proof procedure paradigm and advocated the procedural embedding of knowledge instead. The resulting conflict between the use of logical representations and the use of procedural representations was resolved in the early 1970s with the

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also in 1959. GPS featured data structures for planning and decomposition. The system would begin with a goal. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal. The Advisor Taker, on the other hand, proposed the use of the

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were one of the first knowledge representation primitives. Also, data structures and algorithms for general fast search. In this area, there is a strong overlap with research in data structures and algorithms in computer science. In early systems, the Lisp programming language, which was modeled

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Ontologies can of course be written down in a wide variety of languages and notations (e.g., logic, LISP, etc.); the essential information is not the form of that language but the content, i.e., the set of concepts offered as a way of thinking about the world. Simply put, the important part is

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Reasoning efficiency. This refers to the run time efficiency of the system. The ability of the knowledge base to be updated and the reasoner to develop new inferences in a reasonable period of time. In some ways, this is the flip side of expressive adequacy. In general, the more powerful a

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The good news in reducing KR service to theorem proving is that we now have a very clear, very specific notion of what the KR system should do; the bad new is that it is also clear that the services can not be provided... deciding whether or not a sentence in FOL is a theorem... is

1000:. Non-monotonic reasoning allows various kinds of hypothetical reasoning. The system associates facts asserted with the rules and facts used to justify them and as those facts change updates the dependent knowledge as well. In rule based systems this capability is known as a 885:"It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends." 1090:

electrodynamics in the device: Here signals propagate at finite speed and an object (like a resistor) that was previously viewed as a single component with an I/O behavior may now have to be thought of as an extended medium through which an electromagnetic wave flows.

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project. Cyc established its own Frame language and had large numbers of analysts document various areas of common-sense reasoning in that language. The knowledge recorded in Cyc included common-sense models of time, causality, physics, intentions, and many others.

879:"A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting," i.e., "by reasoning about the world rather than taking action in it." 1018:

engine will be. Efficiency was often an issue, especially for early applications of knowledge representation technology. They were usually implemented in interpreted environments such as Lisp, which were slow compared to more traditional platforms of the

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vs. facts and defaults. Universals are general statements about the world such as "All humans are mortal". Facts are specific examples of universals such as "Socrates is a human and therefore mortal". In logical terms definitions and universals are about

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relations in a knowledge base rather than rules. A classifier can infer new classes and dynamically change the ontology as new information becomes available. This capability is ideal for the ever-changing and evolving information space of the Internet.

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network of knowledge. Defining ontologies that are static and incapable of evolving on the fly would be very limiting for Internet-based systems. The classifier technology provides the ability to deal with the dynamic environment of the Internet.

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and can process basic statements and questions about the world, it is essential to represent this kind of knowledge. In addition to McCarthy and Hayes' situation calculus, one of the most ambitious programs to tackle this problem was Doug Lenat's

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in the mid-1970s. A frame is similar to an object class: It is an abstract description of a category describing things in the world, problems, and potential solutions. Frames were originally used on systems geared toward human interaction, e.g.

719:, that in an event driven logic there need to be axioms that state things maintain position from one moment to the next unless they are moved by some external force. In order to make a true artificial intelligence agent that can 643:

to answer questions and solve problems in the domain. In these early systems the facts in the knowledge base tended to be a fairly flat structure, essentially assertions about the values of variables used by the rules.

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for representing and utilizing complex logic such as the process to make a medical diagnosis. Integrated systems were developed that combined frames and rules. One of the most powerful and well known was the 1983

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Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used for solving complex problems.

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and the social settings in which various default expectations such as ordering food in a restaurant narrow the search space and allow the system to choose appropriate responses to dynamic situations.

994:. All forms of knowledge representation must deal with this aspect and most do so with some variant of set theory, modeling universals as sets and subsets and definitions as elements in those sets. 2162: 796:

For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.

578:, in turn, showed how to do robot plan-formation by applying resolution to the situation calculus. He also showed how to use resolution for question-answering and automatic programming. 1086:

widely used in representing electronic circuits (e.g.,), as well as ontologies for time, belief, and even programming itself. Each of these offers a way to see some part of the world.

753:. The Semantic Web seeks to add a layer of semantics (meaning) on top of the current Internet. Rather than indexing web sites and pages via keywords, the Semantic Web creates large 789:

is not the best formalism to use to solve complex problems. Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in

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because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all

814:(FOL), with its high expressive power and ability to formalise much of mathematics, is a standard for comparing the expressibility of knowledge representation languages. 860:. The resulting extended semantics of LP is a variation of the standard semantics of Horn clauses and FOL, and is a form of database semantics, which includes the 628:, etc. Rather than general problem solvers, AI changed its focus to expert systems that could match human competence on a specific task, such as medical diagnosis. 955:

in computer science. It refers to the capability of a formalism to have access to information about its own state. An example would be the meta-object protocol in

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was essential to make an AI that could interact with humans using natural language. Cyc was meant to address this problem. The language they defined was known as

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Levesque, H.J. and Brachman, R.J., 1987. Expressiveness and tractability in knowledge representation and reasoning 1. Computational intelligence, 3(1), pp.78-93.

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research which migrated to some commercial tools, the ability to associate certainty factors with rules and conclusions. Later research in this area is known as

911:(RDF) provides the basic capabilities to define knowledge-based objects on the Internet with basic features such as Is-A relations and object properties. The 396: 2037:

Davis R, Shrobe H E, Representing Structure and Behavior of Digital Hardware, IEEE Computer, Special Issue on Knowledge Representation, 16(10):75-82.

487: 2154: 1824: 1400: 1126: 1802: 1075:. Modularity—the ability to define boundaries around specific domains and problem spaces—is essential for these languages because as stated by 61:. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design 283: 248: 2285: 2008: 1915: 1735: 555:

to formalise mathematics and to automate the proof of mathematical theorems. A major step in this direction was the development of the

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that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from

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There is a long history of work attempting to build ontologies for a variety of task domains, e.g., an ontology for liquids, the

261: 2967: 1628: 3020: 2112: 2075: 1992: 1867: 1706: 1664: 1581:"The Semantic Web – A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities" 1560: 1497: 1370: 872: 480: 406: 360: 315: 310: 2871: 882:"It is a set of ontological commitments", i.e., "an answer to the question: In what terms should I think about the world?" 838: 621: 907:

The Semantic Web integrates concepts from knowledge representation and reasoning with markup languages based on XML. The

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Zlatarva, Nellie (1992). "Truth Maintenance Systems and their Application for Verifying Expert System Knowledge Bases".

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The early development of logic programming was largely a European phenomenon. In North America, AI researchers such as

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A key trade-off in the design of knowledge representation formalisms is that between expressivity and tractability.

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notions like connections and components, not the choice between writing them as predicates or LISP constructs.

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The earliest work in computerized knowledge representation was focused on general problem-solvers such as the

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MacGregor, Robert (June 1991). "Using a description classifier to enhance knowledge representation".

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logic and automated theorem proving. One of the most influential languages in this research was the

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today. In such approaches, problem solving was a form of graph traversal or path-finding, as in the

136: 3096: 2929: 2522: 2391: 1001: 987: 773:(OWL) provides additional levels of semantics and enables integration with classification engines. 367: 54: 769:(RDF) provides the basic capability to define classes, subclasses, and properties of objects. The 3157: 3091: 2982: 2798: 2465: 1039: 1007: 865: 609:

advocated the representation of domain-specific knowledge rather than general-purpose reasoning.

128: 46: 891:"It is a medium of human expression", i.e., "a language in which we say things about the world." 765:(DARPA) have integrated frame languages and classifiers with markup languages based on XML. The 3222: 3053: 2934: 2701: 2691: 2686: 1186: 861: 841:. But logic programs have a well-defined logical semantics, whereas production systems do not. 837:. Logic programs have a rule-based syntax, which is easily confused with the IF-THEN syntax of 669: 526: 501: 238: 631:

Expert systems gave us the terminology still in use today where AI systems are divided into a

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Hewitt, C., 2009. Inconsistency robustness in logic programs. arXiv preprint arXiv:0904.3036.

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Hayes P, Naive physics I: Ontology for liquids. University of Essex report, 1978, Essex, UK.

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Many of the early approaches to knowledge represention in AI used graph representations and

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in psychology and to the phase of AI focused on knowledge representation that resulted in

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as a logical representation of common sense knowledge about the laws of cause and effect.

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but causal and essential role in engendering the behavior that manifests that knowledge.

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about the world in a form that a computer system can use to solve complex tasks such as

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Brachman, Ron (1985). "Introduction". In Ronald Brachman and Hector J. Levesque (ed.).

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Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project

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Doran, J. E.; Michie, D. (1966-09-20). "Experiments with the Graph Traverser program".

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Proceedings of the 1986 ACM fourteenth annual conference on Computer science - CSC '86

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Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs

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Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures

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by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy

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Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland:

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by multiple projects. One of the leading research projects in this area was the

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Mary-Anne Williams and Hans Rott: "Frontiers in Belief Revision, Kluwer", 2001.

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outlined five distinct roles to analyze a knowledge representation framework:

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Knublauch, Holger; Oberle, Daniel; Tetlow, Phil; Wallace, Evan (2006-03-09).

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Knowledge representation and reasoning are a key enabling technology for the

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representation, the more it has expressive adequacy, the less efficient its

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What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks

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Some philosophical problems from the standpoint of artificial intelligence

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Primitives. What is the underlying framework used to represent knowledge?

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that had a rigorous semantics, formal definitions for concepts such as an

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One of the most active areas of knowledge representation research is the

595: 401: 386: 50: 3207: 3137: 2730: 2470: 2326: 1953: 1504: (archived August 25, 2013). In Meltzer, B., and Michie, D., eds., 1076: 982: 544:. Typical applications included robot plan-formation and game-playing. 2220: 829:

A similar balancing act was also a motivation for the development of

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categorized the core issues for knowledge representation as follows:

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Another area of knowledge representation research was the problem of

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The justification for knowledge representation is that conventional

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Principles of Knowledge Representation and Reasoning Incorporated

1977:"A Fundamental Tradeoff in Knowledge Representation and Reasoning" 1691:"A Fundamental Tradeoff in Knowledge Representation and Reasoning" 441: 2103: 1789:

Davis, Randall; Shrobe, Howard; Szolovits, Peter (Spring 1993).

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Hayes-Roth, Frederick; Waterman, Donald; Lenat, Douglas (1983).

1545:"Prologue to Reflections and Semantics in a Procedural Language" 1359:

Hayes-Roth, Frederick; Waterman, Donald; Lenat, Douglas (1983).

635:, which includes facts and rules about a problem domain, and an 1621:"A Semantic Web Primer for Object-Oriented Software Developers" 1579:

Berners-Lee, Tim; Hendler, James; Lassila, Ora (May 17, 2001).

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Knowledge Representation and the Semantics of Natural Language

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have a reasoning or inference engine as part of the system.

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Frank van Harmelen, Vladimir Lifschitz and Bruce Porter:

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Meta-representation. This is also known as the issue of

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The earliest form of logic programming was based on the

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Examples of knowledge representation formalisms include

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subset of FOL. But later extensions of LP included the

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The starting point for knowledge representation is the

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Description Logic in Practice: A CLASSIC Application

1788: 1574: 1572: 2118:Randall Davis, Howard Shrobe, and Peter Szolovits; 1979:. In Ronald Brachman and Hector J. Levesque (ed.). 1693:. In Ronald Brachman and Hector J. Levesque (ed.). 1547:. In Ronald Brachman and Hector J. Levesque (ed.). 871:In a key 1993 paper on the topic, Randall Davis of 1855: 1508:4. Edinburgh: Edinburgh University Press. 463–502. 1435:"A Structural Paradigm for Representing Knowledge" 1569: 1267:Application of Theorem Proving to Problem Solving 1144:, a language for lexical knowledge representation 3278: 2242: 1974: 1885:"Paradigm Shift: An Introduction to Fuzzy Logic" 1831:. Information Sciences Institute. Archived from 1688: 799:Knowledge representation goes hand in hand with 2260:- a non-free 3d knowledge representation system 2155:Understanding Implementations of IS-A Relations 1739:(4th ed.). 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J. 1969. 639:, which applies the knowledge in the 59:having a dialog in a natural language 2347:Energy consumption (Green computing) 2293: 2171:Handbook of Knowledge Representation 2089:Readings in Knowledge Representation 1981:Readings in Knowledge Representation 1858:Readings in Knowledge Representation 1695:Readings in Knowledge Representation 1549:Readings in Knowledge Representation 1451:from the original on April 30, 2020. 566:In the meanwhile, John McCarthy and 3026:Distributed artificial intelligence 2305:ACM Computing Classification System 2215:What is a Knowledge Representation? 2120:What Is a Knowledge Representation? 1882: 1210:. Lawrence Erlbaum Associates, Inc. 990:while facts and defaults are about 734:knowledge representation hypothesis 24: 2538:Integrated development environment 2221:Introduction to Knowledge Modeling 2044: 1761: 918: 805:knowledge representation languages 135: 25: 3318: 3006:Automated planning and scheduling 2543:Software configuration management 2208: 2180:Lawrence Erlbaum Associates, 1998 2101:Chein, M., Mugnier, M.-L. (2009), 1921:from the original on 12 June 2014 1599:10.1038/scientificamerican0501-34 1263: 666:Knowledge Engineering Environment 3260: 3250: 3241: 3240: 27:Field of artificial intelligence 3251: 2654:Computational complexity theory 2193:Adrian Walker, Michael McCord, 2031: 2022: 2001: 1968: 1933: 1876: 1847: 1816: 1805:from the original on 2012-04-06 1719: 1682: 1673: 1642: 1631:from the original on 2018-01-06 1536: 1511: 1490: 1455: 1426: 156:Artificial general intelligence 49:(AI) dedicated to representing 2445:Network performance evaluation 1942:Artificial Intelligence Review 1392: 1379: 1352: 1331: 1286: 1277: 1257: 1214: 1199: 1063:have been developed. Most are 909:Resource Description Framework 767:Resource Description Framework 658:understanding natural language 598:as goal-reduction procedures. 55:diagnosing a medical condition 13: 1: 2809:Multimedia information system 2794:Geographic information system 2784:Enterprise information system 2380:Computer systems organization 2122:AI Magazine, 14(1):17-33,1993 1192: 69:to automate various kinds of 3168:Computational social science 2756:Theoretical computer science 2576:Software development process 2352:Electronic design automation 2337:Very Large Scale Integration 2199:Knowledge Systems and Prolog 1983:. Morgan Kaufmann. pp. 1551:. Morgan Kaufmann. pp. 7: 2991:Natural language processing 2779:Information storage systems 2217:by Randall Davis and others 1697:. Morgan Kaufmann. p. 1655:. Addison-Wesley. pp. 1105: 992:existential quantifications 776: 191:Natural language processing 10: 3323: 2907:Human–computer interaction 2877:Intrusion detection system 2789:Social information systems 2774:Database management system 2253:The Rule Markup Initiative 1162:Logico-linguistic modeling 1127:Commonsense knowledge base 1027: 504:(GPS) system developed by 244:Hybrid intelligent systems 166:Recursive self-improvement 115: 3236: 3173:Computational engineering 3148:Computational mathematics 3125: 3072: 3034: 2981: 2943: 2905: 2847: 2764: 2710: 2672: 2624: 2561: 2494: 2458: 2415: 2379: 2312: 2301: 2141:Reasoning About Knowledge 2091:, Morgan Kaufmann, 1985, 1399:Mettrey, William (1987). 1295:"The limitation of logic" 1293:Kowalski, Robert (1986). 1150:, a KR language based on 1112:Alphabet of human thought 1098:in terms of rules (e.g., 651:developed the concept of 612:These efforts led to the 549:automated theorem-provers 3287:Knowledge representation 3183:Computational healthcare 3178:Differentiable computing 3097:Graphics processing unit 2523:Domain-specific language 2392:Computational complexity 2197:, and Walter G. Wilson: 2188:Knowledge Representation 2178:Knowledge Representation 2070:, Morgan Kaufmann, 2004 1543:Smith, Brian C. (1985). 1059:After CycL, a number of 1002:truth maintenance system 988:universal quantification 368:Artificial consciousness 18:Knowledge representation 3292:Intelligence assessment 3158:Computational chemistry 3092:Photograph manipulation 2983:Artificial intelligence 2799:Decision support system 1904:10.1109/MP.2006.1635021 1825:"Retrospective on Loom" 1652:Building Expert Systems 1362:Building Expert Systems 1040:knowledge-based systems 998:Non-monotonic reasoning 866:closed world assumption 239:Evolutionary algorithms 129:Artificial intelligence 47:artificial intelligence 3223:Educational technology 3054:Reinforcement learning 2804:Process control system 2702:Computational geometry 2692:Algorithmic efficiency 2687:Analysis of algorithms 2342:Systems on Chip (SoCs) 2238:Loom Project Home Page 1433:Brachman, Ron (1978). 1243:10.1098/rspa.1966.0205 1187:Valuation-based system 1038:In the early years of 862:unique name assumption 747: 713:common-sense reasoning 620:in the 1970s and 80s, 527:common sense reasoning 502:General Problem Solver 140: 3302:Programming paradigms 3193:Electronic publishing 3163:Computational biology 3153:Computational physics 3049:Unsupervised learning 2963:Distributed computing 2839:Information retrieval 2746:Mathematical analysis 2736:Mathematical software 2626:Theory of computation 2591:Software construction 2581:Requirements analysis 2459:Software organization 2387:Computer architecture 2357:Hardware acceleration 2322:Printed circuit board 1307:10.1145/324634.325168 1223:Proc. R. Soc. Lond. A 1122:Chunking (psychology) 1065:declarative languages 913:Web Ontology Language 771:Web Ontology Language 742: 139: 3297:Scientific modelling 2953:Concurrent computing 2925:Ubiquitous computing 2897:Application security 2892:Information security 2721:Discrete mathematics 2697:Randomized algorithm 2649:Computability theory 2634:Model of computation 2606:Software maintenance 2601:Software engineering 2563:Software development 2513:Programming language 2508:Programming paradigm 2425:Network architecture 1883:Bih, Joseph (2006). 1506:Machine Intelligence 1172:Knowledge management 1084:lumped element model 1030:Ontology engineering 1024:Ontology engineering 736:first formalized by 614:cognitive revolution 607:Frederick Hayes-Roth 181:General game playing 3307:Automated reasoning 3228:Document management 3218:Operations research 3143:Enterprise software 3059:Multi-task learning 3044:Supervised learning 2766:Information systems 2596:Software deployment 2553:Software repository 2407:Real-time computing 2176:Arthur B. Markman: 2143:, MIT Press, 1995, 1586:Scientific American 1411:(4). Archived from 1235:1966RSPSA.294..235D 1182:Semantic technology 1016:automated reasoning 1008:Expressive adequacy 854:non-monotonic logic 850:negation as failure 801:automated reasoning 542:A* search algorithm 333:Machine translation 249:Systems integration 186:Knowledge reasoning 123:Part of a series on 98:automated reasoning 3011:Search methodology 2958:Parallel computing 2915:Interaction design 2824:Computing platform 2751:Numerical analysis 2741:Information theory 2533:Software framework 2496:Software notations 2435:Network components 2332:Integrated circuit 2085:Hector J. Levesque 2081:Ronald J. Brachman 2065:Hector J. Levesque 2061:Ronald J. Brachman 2051:Ronald J. Brachman 1954:10.1007/bf00155580 1835:on 25 October 2013 1727:Russell, Stuart J. 1605:on April 24, 2013. 1522:. Addison-Wesley. 1365:. Addison-Wesley. 1071:, or are based on 1061:ontology languages 622:production systems 572:situation calculus 561:John Alan Robinson 553:mathematical logic 523:predicate calculus 141: 45:) is the field of 3274: 3273: 3203:Electronic voting 3133:Quantum Computing 3126:Applied computing 3112:Image compression 2882:Hardware security 2872:Security services 2829:Digital marketing 2616:Open-source model 2528:Modeling language 2440:Network scheduler 2130:Joseph Y. Halpern 2113:978-1-84800-285-2 2107:, Springer, 2009, 2076:978-1-55860-932-7 1994:978-0-934613-01-9 1869:978-0-934613-01-9 1708:978-0-934613-01-9 1666:978-0-201-10686-2 1562:978-0-934613-01-9 1372:978-0-201-10686-2 1301:. pp. 7–13. 1229:(1437): 235–259. 1157:Logic programming 1152:First-order logic 1073:first-order logic 1067:, and are either 1034:Ontology language 942:First Order Logic 933:Semantic networks 858:default reasoning 831:logic programming 826:expert systems. 812:First Order Logic 690:Texas Instruments 678:backward chaining 584:logic programming 557:resolution method 534:semantic networks 498: 497: 234:Bayesian networks 161:Intelligent agent 112:and classifiers. 102:inference engines 16:(Redirected from 3314: 3264: 3263: 3254: 3253: 3244: 3243: 3064:Cross-validation 3036:Machine learning 2920:Social computing 2887:Network security 2682:Algorithm design 2611:Programming team 2571:Control variable 2548:Software library 2486:Software quality 2481:Operating system 2430:Network protocol 2295:Computer science 2288: 2281: 2274: 2265: 2264: 2223:by Pejman Makhfi 2160:Hermann Helbig: 2157:. ER 1996: 42-57 2038: 2035: 2029: 2026: 2020: 2005: 1999: 1998: 1972: 1966: 1965: 1937: 1931: 1930: 1928: 1926: 1920: 1889: 1880: 1874: 1873: 1861: 1851: 1845: 1844: 1842: 1840: 1820: 1814: 1813: 1811: 1810: 1786: 1759: 1758: 1723: 1717: 1716: 1686: 1680: 1677: 1671: 1670: 1646: 1640: 1639: 1637: 1636: 1616: 1607: 1606: 1601:. Archived from 1576: 1567: 1566: 1540: 1534: 1533: 1515: 1509: 1494: 1488: 1487: 1476:10.1109/64.87683 1459: 1453: 1452: 1450: 1439: 1430: 1424: 1423: 1421: 1420: 1396: 1390: 1383: 1377: 1376: 1356: 1350: 1349: 1335: 1329: 1328: 1290: 1284: 1281: 1275: 1274: 1272: 1264:Green, Cordell. 1261: 1255: 1254: 1218: 1212: 1211: 1203: 1132:Conceptual graph 981:Definitions and 839:production rules 637:inference engine 538:knowledge graphs 512:in 1959 and the 510:Herbert A. Simon 490: 483: 476: 397:Existential risk 219:Machine learning 120: 119: 110:model generators 100:engines include 21: 3322: 3321: 3317: 3316: 3315: 3313: 3312: 3311: 3277: 3276: 3275: 3270: 3261: 3232: 3213:Word processing 3121: 3107:Virtual reality 3068: 3030: 3001:Computer vision 2977: 2973:Multiprocessing 2939: 2901: 2867:Security hacker 2843: 2819:Digital library 2760: 2711:Mathematics of 2706: 2668: 2644:Automata theory 2639:Formal language 2620: 2586:Software design 2557: 2490: 2476:Virtual machine 2454: 2450:Network service 2411: 2402:Embedded system 2375: 2308: 2297: 2292: 2211: 2047: 2045:Further reading 2042: 2041: 2036: 2032: 2027: 2023: 2006: 2002: 1995: 1973: 1969: 1938: 1934: 1924: 1922: 1918: 1892:IEEE Potentials 1887: 1881: 1877: 1870: 1852: 1848: 1838: 1836: 1821: 1817: 1808: 1806: 1787: 1762: 1747: 1724: 1720: 1709: 1687: 1683: 1678: 1674: 1667: 1647: 1643: 1634: 1632: 1617: 1610: 1577: 1570: 1563: 1541: 1537: 1530: 1516: 1512: 1502:Wayback Machine 1495: 1491: 1460: 1456: 1448: 1437: 1431: 1427: 1418: 1416: 1397: 1393: 1385:Marvin Minsky, 1384: 1380: 1373: 1357: 1353: 1336: 1332: 1317: 1291: 1287: 1282: 1278: 1270: 1262: 1258: 1219: 1215: 1204: 1200: 1195: 1167:Knowledge graph 1117:Belief revision 1108: 1069:frame languages 1036: 1028:Main articles: 1026: 938:lambda calculus 921: 919:Characteristics 787:procedural code 779: 626:frame languages 582:development of 494: 465: 464: 455: 447: 446: 422: 412: 411: 383:Control problem 363: 353: 352: 264: 254: 253: 214: 206: 205: 176:Computer vision 151: 118: 106:theorem provers 28: 23: 22: 15: 12: 11: 5: 3320: 3310: 3309: 3304: 3299: 3294: 3289: 3272: 3271: 3269: 3268: 3258: 3248: 3237: 3234: 3233: 3231: 3230: 3225: 3220: 3215: 3210: 3205: 3200: 3195: 3190: 3185: 3180: 3175: 3170: 3165: 3160: 3155: 3150: 3145: 3140: 3135: 3129: 3127: 3123: 3122: 3120: 3119: 3117:Solid modeling 3114: 3109: 3104: 3099: 3094: 3089: 3084: 3078: 3076: 3070: 3069: 3067: 3066: 3061: 3056: 3051: 3046: 3040: 3038: 3032: 3031: 3029: 3028: 3023: 3018: 3016:Control method 3013: 3008: 3003: 2998: 2993: 2987: 2985: 2979: 2978: 2976: 2975: 2970: 2968:Multithreading 2965: 2960: 2955: 2949: 2947: 2941: 2940: 2938: 2937: 2932: 2927: 2922: 2917: 2911: 2909: 2903: 2902: 2900: 2899: 2894: 2889: 2884: 2879: 2874: 2869: 2864: 2862:Formal methods 2859: 2853: 2851: 2845: 2844: 2842: 2841: 2836: 2834:World Wide Web 2831: 2826: 2821: 2816: 2811: 2806: 2801: 2796: 2791: 2786: 2781: 2776: 2770: 2768: 2762: 2761: 2759: 2758: 2753: 2748: 2743: 2738: 2733: 2728: 2723: 2717: 2715: 2708: 2707: 2705: 2704: 2699: 2694: 2689: 2684: 2678: 2676: 2670: 2669: 2667: 2666: 2661: 2656: 2651: 2646: 2641: 2636: 2630: 2628: 2622: 2621: 2619: 2618: 2613: 2608: 2603: 2598: 2593: 2588: 2583: 2578: 2573: 2567: 2565: 2559: 2558: 2556: 2555: 2550: 2545: 2540: 2535: 2530: 2525: 2520: 2515: 2510: 2504: 2502: 2492: 2491: 2489: 2488: 2483: 2478: 2473: 2468: 2462: 2460: 2456: 2455: 2453: 2452: 2447: 2442: 2437: 2432: 2427: 2421: 2419: 2413: 2412: 2410: 2409: 2404: 2399: 2394: 2389: 2383: 2381: 2377: 2376: 2374: 2373: 2364: 2359: 2354: 2349: 2344: 2339: 2334: 2329: 2324: 2318: 2316: 2310: 2309: 2302: 2299: 2298: 2291: 2290: 2283: 2276: 2268: 2262: 2261: 2255: 2250: 2245: 2240: 2235: 2230: 2224: 2218: 2210: 2209:External links 2207: 2206: 2205: 2202: 2191: 2181: 2174: 2167: 2158: 2151: 2138:Moshe Y. Vardi 2123: 2116: 2099: 2078: 2058: 2046: 2043: 2040: 2039: 2030: 2021: 2000: 1993: 1967: 1932: 1875: 1868: 1846: 1815: 1760: 1746:978-0134610993 1745: 1731:Norvig, Peter. 1718: 1707: 1681: 1672: 1665: 1641: 1608: 1568: 1561: 1535: 1529:978-0201517521 1528: 1510: 1489: 1454: 1425: 1391: 1378: 1371: 1351: 1330: 1315: 1285: 1276: 1256: 1213: 1197: 1196: 1194: 1191: 1190: 1189: 1184: 1179: 1174: 1169: 1164: 1159: 1154: 1145: 1139: 1134: 1129: 1124: 1119: 1114: 1107: 1104: 1025: 1022: 1021: 1020: 1011: 1005: 995: 979: 972:expert systems 968:Incompleteness 965: 949: 920: 917: 893: 892: 889: 886: 883: 880: 864:and a form of 791:expert systems 778: 775: 738:Brian C. Smith 702:frame language 641:knowledge base 633:knowledge base 618:expert systems 592:SLD resolution 570:developed the 496: 495: 493: 492: 485: 478: 470: 467: 466: 463: 462: 456: 453: 452: 449: 448: 445: 444: 439: 434: 429: 423: 418: 417: 414: 413: 410: 409: 404: 399: 394: 389: 380: 375: 370: 364: 359: 358: 355: 354: 351: 350: 345: 340: 335: 330: 329: 328: 318: 313: 308: 307: 306: 301: 296: 286: 281: 279:Earth sciences 276: 271: 269:Bioinformatics 265: 260: 259: 256: 255: 252: 251: 246: 241: 236: 231: 226: 221: 215: 212: 211: 208: 207: 204: 203: 198: 193: 188: 183: 178: 173: 168: 163: 158: 152: 147: 146: 143: 142: 132: 131: 125: 124: 117: 114: 96:. Examples of 90:logic programs 26: 9: 6: 4: 3: 2: 3319: 3308: 3305: 3303: 3300: 3298: 3295: 3293: 3290: 3288: 3285: 3284: 3282: 3267: 3259: 3257: 3249: 3247: 3239: 3238: 3235: 3229: 3226: 3224: 3221: 3219: 3216: 3214: 3211: 3209: 3206: 3204: 3201: 3199: 3196: 3194: 3191: 3189: 3186: 3184: 3181: 3179: 3176: 3174: 3171: 3169: 3166: 3164: 3161: 3159: 3156: 3154: 3151: 3149: 3146: 3144: 3141: 3139: 3136: 3134: 3131: 3130: 3128: 3124: 3118: 3115: 3113: 3110: 3108: 3105: 3103: 3102:Mixed reality 3100: 3098: 3095: 3093: 3090: 3088: 3085: 3083: 3080: 3079: 3077: 3075: 3071: 3065: 3062: 3060: 3057: 3055: 3052: 3050: 3047: 3045: 3042: 3041: 3039: 3037: 3033: 3027: 3024: 3022: 3019: 3017: 3014: 3012: 3009: 3007: 3004: 3002: 2999: 2997: 2994: 2992: 2989: 2988: 2986: 2984: 2980: 2974: 2971: 2969: 2966: 2964: 2961: 2959: 2956: 2954: 2951: 2950: 2948: 2946: 2942: 2936: 2935:Accessibility 2933: 2931: 2930:Visualization 2928: 2926: 2923: 2921: 2918: 2916: 2913: 2912: 2910: 2908: 2904: 2898: 2895: 2893: 2890: 2888: 2885: 2883: 2880: 2878: 2875: 2873: 2870: 2868: 2865: 2863: 2860: 2858: 2855: 2854: 2852: 2850: 2846: 2840: 2837: 2835: 2832: 2830: 2827: 2825: 2822: 2820: 2817: 2815: 2812: 2810: 2807: 2805: 2802: 2800: 2797: 2795: 2792: 2790: 2787: 2785: 2782: 2780: 2777: 2775: 2772: 2771: 2769: 2767: 2763: 2757: 2754: 2752: 2749: 2747: 2744: 2742: 2739: 2737: 2734: 2732: 2729: 2727: 2724: 2722: 2719: 2718: 2716: 2714: 2709: 2703: 2700: 2698: 2695: 2693: 2690: 2688: 2685: 2683: 2680: 2679: 2677: 2675: 2671: 2665: 2662: 2660: 2657: 2655: 2652: 2650: 2647: 2645: 2642: 2640: 2637: 2635: 2632: 2631: 2629: 2627: 2623: 2617: 2614: 2612: 2609: 2607: 2604: 2602: 2599: 2597: 2594: 2592: 2589: 2587: 2584: 2582: 2579: 2577: 2574: 2572: 2569: 2568: 2566: 2564: 2560: 2554: 2551: 2549: 2546: 2544: 2541: 2539: 2536: 2534: 2531: 2529: 2526: 2524: 2521: 2519: 2516: 2514: 2511: 2509: 2506: 2505: 2503: 2501: 2497: 2493: 2487: 2484: 2482: 2479: 2477: 2474: 2472: 2469: 2467: 2464: 2463: 2461: 2457: 2451: 2448: 2446: 2443: 2441: 2438: 2436: 2433: 2431: 2428: 2426: 2423: 2422: 2420: 2418: 2414: 2408: 2405: 2403: 2400: 2398: 2397:Dependability 2395: 2393: 2390: 2388: 2385: 2384: 2382: 2378: 2372: 2368: 2365: 2363: 2360: 2358: 2355: 2353: 2350: 2348: 2345: 2343: 2340: 2338: 2335: 2333: 2330: 2328: 2325: 2323: 2320: 2319: 2317: 2315: 2311: 2306: 2300: 2296: 2289: 2284: 2282: 2277: 2275: 2270: 2269: 2266: 2259: 2258:Nelements KOS 2256: 2254: 2251: 2249: 2246: 2244: 2241: 2239: 2236: 2234: 2231: 2228: 2225: 2222: 2219: 2216: 2213: 2212: 2203: 2200: 2196: 2192: 2189: 2185: 2182: 2179: 2175: 2172: 2168: 2165: 2164: 2159: 2156: 2152: 2150: 2149:0-262-06162-7 2146: 2142: 2139: 2135: 2131: 2127: 2124: 2121: 2117: 2114: 2110: 2106: 2105: 2100: 2098: 2097:0-934613-01-X 2094: 2090: 2086: 2082: 2079: 2077: 2073: 2069: 2066: 2062: 2059: 2056: 2052: 2049: 2048: 2034: 2025: 2018: 2017:0-13-604259-7 2014: 2010: 2004: 1996: 1990: 1986: 1982: 1978: 1971: 1963: 1959: 1955: 1951: 1947: 1943: 1936: 1917: 1913: 1909: 1905: 1901: 1897: 1893: 1886: 1879: 1871: 1865: 1860: 1859: 1850: 1834: 1830: 1826: 1819: 1804: 1800: 1796: 1792: 1785: 1783: 1781: 1779: 1777: 1775: 1773: 1771: 1769: 1767: 1765: 1756: 1752: 1748: 1742: 1738: 1737: 1732: 1728: 1722: 1715: 1710: 1704: 1700: 1696: 1692: 1685: 1676: 1668: 1662: 1658: 1654: 1653: 1645: 1630: 1626: 1622: 1615: 1613: 1604: 1600: 1596: 1592: 1588: 1587: 1582: 1575: 1573: 1564: 1558: 1554: 1550: 1546: 1539: 1531: 1525: 1521: 1514: 1507: 1503: 1499: 1493: 1485: 1481: 1477: 1473: 1469: 1465: 1458: 1447: 1443: 1436: 1429: 1415:on 2013-11-10 1414: 1410: 1406: 1402: 1395: 1388: 1382: 1374: 1368: 1364: 1363: 1355: 1347: 1343: 1342: 1334: 1326: 1322: 1318: 1316:0-89791-177-6 1312: 1308: 1304: 1300: 1296: 1289: 1280: 1273:. 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Sowa 2187: 2184:John F. Sowa 2177: 2161: 2140: 2126:Ronald Fagin 2102: 2088: 2067: 2033: 2024: 2019:, p. 437-439 2003: 1980: 1970: 1945: 1941: 1935: 1923:. Retrieved 1895: 1891: 1878: 1857: 1849: 1837:. Retrieved 1833:the original 1828: 1818: 1807:. Retrieved 1801:(1): 17–33. 1798: 1794: 1734: 1721: 1712: 1694: 1684: 1675: 1651: 1644: 1633:. Retrieved 1603:the original 1593:(5): 34–43. 1590: 1584: 1548: 1538: 1519: 1513: 1505: 1492: 1470:(3): 41–46. 1467: 1463: 1457: 1441: 1428: 1417:. 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