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697:. 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).
937:, 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.
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929:, 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
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888:. 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
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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.
704:. 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
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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.
959:. 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
669:. 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
999:. 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.
1068:, "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.
877:"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."
811:
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
1091:) 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.
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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
989:. 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
874:"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."
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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.
868:"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."
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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.
708:, 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
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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.
983:. 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.
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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.
567:, 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.
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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.
742:. 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
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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
803:(FOL), with its high expressive power and ability to formalise much of mathematics, is a standard for comparing the expressibility of knowledge representation languages.
849:. 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
617:, 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.
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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
900:(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
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Davis R, Shrobe H E, Representing
Structure and Behavior of Digital Hardware, IEEE Computer, Special Issue on Knowledge Representation, 16(10):75-82.
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1064:. Modularity—the ability to define boundaries around specific domains and problem spaces—is essential for these languages because as stated by
50:. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design
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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
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1570:"The Semantic Web – A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities"
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871:"It is a set of ontological commitments", i.e., "an answer to the question: In what terms should I think about the world?"
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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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857:. These assumptions are much harder to state and reason with explicitly using the standard semantics of FOL.
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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
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762:(OWL) provides additional levels of semantics and enables integration with classification engines.
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758:(RDF) provides the basic capability to define classes, subclasses, and properties of objects. The
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advocated the representation of domain-specific knowledge rather than general-purpose reasoning.
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880:"It is a medium of human expression", i.e., "a language in which we say things about the world."
754:(DARPA) have integrated frame languages and classifiers with markup languages based on XML. The
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830:. But logic programs have a well-defined logical semantics, whereas production systems do not.
826:. Logic programs have a rule-based syntax, which is easily confused with the IF-THEN syntax of
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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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2017:
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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904:(OWL) adds additional semantics and integrates with automatic classification reasoners.
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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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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
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1493: (archived August 25, 2013). In Meltzer, B., and Michie, D., eds.,
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533:. Typical applications included robot plan-formation and game-playing.
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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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1390:"An Assessment of Tools for Building Large Knowledge-Based Systems"
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Principles of Knowledge Representation and Reasoning Incorporated
1966:"A Fundamental Tradeoff in Knowledge Representation and Reasoning"
1680:"A Fundamental Tradeoff in Knowledge Representation and Reasoning"
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Davis, Randall; Shrobe, Howard; Szolovits, Peter (Spring 1993).
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Hayes-Roth, Frederick; Waterman, Donald; Lenat, Douglas (1983).
1534:"Prologue to Reflections and Semantics in a Procedural Language"
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Hayes-Roth, Frederick; Waterman, Donald; Lenat, Douglas (1983).
624:, which includes facts and rules about a problem domain, and an
1610:"A Semantic Web Primer for Object-Oriented Software Developers"
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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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2000:(3rd ed.), Upper Saddle River, New Jersey: Prentice Hall,
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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
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1563:
1561:
2107:Randall Davis, Howard Shrobe, and Peter Szolovits;
1968:. In Ronald Brachman and Hector J. Levesque (ed.).
1682:. In Ronald Brachman and Hector J. Levesque (ed.).
1536:. In Ronald Brachman and Hector J. Levesque (ed.).
860:In a key 1993 paper on the topic, Randall Davis of
1844:
1497:4. Edinburgh: Edinburgh University Press. 463–502.
1424:"A Structural Paradigm for Representing Knowledge"
1558:
1256:Application of Theorem Proving to Problem Solving
1133:, a language for lexical knowledge representation
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2231:
1963:
1874:"Paradigm Shift: An Introduction to Fuzzy Logic"
1820:. Information Sciences Institute. Archived from
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788:Knowledge representation goes hand in hand with
2249:- a non-free 3d knowledge representation system
2144:Understanding Implementations of IS-A Relations
1728:(4th ed.). Hoboken: Pearson. p. 282.
1506:
540:for first-order logic, motivated by the use of
2222:DATR Lexical knowledge representation language
2292:Note: This template roughly follows the 2012
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2155:, Springer, Berlin, Heidelberg, New York 2006
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1194:
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1964:Levesque, Hector; Brachman, Ronald (1985).
1678:Levesque, Hector; Brachman, Ronald (1985).
1431:Bolt, Beranek, and Neumann Technical Report
1209:
710:converse with humans using natural language
2275:
2261:
1998:Artificial Intelligence: A Modern Approach
1996:Russell, Stuart J.; Norvig, Peter (2010),
1725:Artificial Intelligence: A Modern Approach
1327:Nilsson, Nils (1995). "Eye on the Prize".
477:
463:
2216:Introduction to Description Logics course
1811:
1603:
1601:
1450:
752:Defense Advanced Research Projects Agency
1928:
1842:
1507:Lenat, Doug; R. V. Guha (January 1990).
1421:
1378:, MIT-AI Laboratory Memo 306, June, 1974
1281:
822:(LP) and the logic programming language
750:Recent projects funded primarily by the
536:Other researchers focused on developing
1387:
1326:
1195:Schank, Roger; Abelson, Robert (1977).
1012:
689:language of the mid-'80s. KL-ONE was a
3275:
2992:Knowledge representation and reasoning
2190:, Second Edition, Addison-Wesley, 1990
2057:Knowledge Representation and Reasoning
2046:; IEEE Computer, 16 (10); October 1983
1851:. Morgan Kaufmann. pp. XVI–XVII.
1773:
1598:
1376:A Framework for Representing Knowledge
933:(FOL). The most well known example is
841:inference rule, which turns LP into a
661:. KEE had a complete rule engine with
20:Knowledge representation and reasoning
3017:Philosophy of artificial intelligence
2256:
1812:Macgregor, Robert (August 13, 1999).
1780:"What Is a Knowledge Representation?"
1771:
1769:
1767:
1765:
1763:
1761:
1759:
1757:
1755:
1753:
1531:
1485:McCarthy, J., and Hayes, P. J. 1969.
628:, which applies the knowledge in the
48:having a dialog in a natural language
2336:Energy consumption (Green computing)
2282:
2160:Handbook of Knowledge Representation
2078:Readings in Knowledge Representation
1970:Readings in Knowledge Representation
1847:Readings in Knowledge Representation
1684:Readings in Knowledge Representation
1538:Readings in Knowledge Representation
1440:from the original on April 30, 2020.
555:In the meanwhile, John McCarthy and
3022:Distributed artificial intelligence
2294:ACM Computing Classification System
2204:What is a Knowledge Representation?
2109:What Is a Knowledge Representation?
1871:
1199:. Lawrence Erlbaum Associates, Inc.
979:while facts and defaults are about
723:knowledge representation hypothesis
13:
2527:Integrated development environment
2210:Introduction to Knowledge Modeling
2033:
1750:
907:
794:knowledge representation languages
124:
14:
3314:
3002:Automated planning and scheduling
2532:Software configuration management
2197:
2169:Lawrence Erlbaum Associates, 1998
2090:Chein, M., Mugnier, M.-L. (2009),
1910:from the original on 12 June 2014
1588:10.1038/scientificamerican0501-34
1252:
655:Knowledge Engineering Environment
3256:
3246:
3237:
3236:
16:Field of artificial intelligence
3247:
2650:Computational complexity theory
2182:Adrian Walker, Michael McCord,
2020:
2011:
1990:
1957:
1922:
1865:
1836:
1805:
1794:from the original on 2012-04-06
1708:
1671:
1662:
1631:
1620:from the original on 2018-01-06
1525:
1500:
1479:
1444:
1415:
145:Artificial general intelligence
38:(AI) dedicated to representing
2434:Network performance evaluation
1931:Artificial Intelligence Review
1381:
1368:
1341:
1320:
1275:
1266:
1246:
1203:
1188:
1052:have been developed. Most are
898:Resource Description Framework
756:Resource Description Framework
647:understanding natural language
587:as goal-reduction procedures.
44:diagnosing a medical condition
1:
2805:Multimedia information system
2790:Geographic information system
2780:Enterprise information system
2369:Computer systems organization
2111:AI Magazine, 14(1):17-33,1993
1181:
58:to automate various kinds of
3164:Computational social science
2752:Theoretical computer science
2565:Software development process
2341:Electronic design automation
2326:Very Large Scale Integration
2188:Knowledge Systems and Prolog
1972:. Morgan Kaufmann. pp.
1540:. Morgan Kaufmann. pp.
7:
2987:Natural language processing
2775:Information storage systems
2206:by Randall Davis and others
1686:. Morgan Kaufmann. p.
1644:. Addison-Wesley. pp.
1094:
981:existential quantifications
765:
180:Natural language processing
10:
3319:
2903:Human–computer interaction
2873:Intrusion detection system
2785:Social information systems
2770:Database management system
2242:The Rule Markup Initiative
1151:Logico-linguistic modeling
1116:Commonsense knowledge base
1016:
493:(GPS) system developed by
233:Hybrid intelligent systems
155:Recursive self-improvement
104:
3232:
3169:Computational engineering
3144:Computational mathematics
3121:
3068:
3030:
2977:
2939:
2901:
2843:
2760:
2706:
2668:
2613:
2550:
2483:
2447:
2404:
2368:
2301:
2290:
2130:Reasoning About Knowledge
2080:, Morgan Kaufmann, 1985,
1388:Mettrey, William (1987).
1284:"The limitation of logic"
1282:Kowalski, Robert (1986).
1139:, a KR language based on
1101:Alphabet of human thought
1087:in terms of rules (e.g.,
640:developed the concept of
601:These efforts led to the
538:automated theorem-provers
3283:Knowledge representation
3179:Computational healthcare
3174:Differentiable computing
3093:Graphics processing unit
2512:Domain-specific language
2381:Computational complexity
2186:, and Walter G. Wilson:
2177:Knowledge Representation
2167:Knowledge Representation
2059:, Morgan Kaufmann, 2004
1532:Smith, Brian C. (1985).
1048:After CycL, a number of
991:truth maintenance system
977:universal quantification
357:Artificial consciousness
3288:Intelligence assessment
3154:Computational chemistry
3088:Photograph manipulation
2979:Artificial intelligence
2795:Decision support system
1893:10.1109/MP.2006.1635021
1814:"Retrospective on Loom"
1641:Building Expert Systems
1351:Building Expert Systems
1029:knowledge-based systems
987:Non-monotonic reasoning
855:closed world assumption
228:Evolutionary algorithms
118:Artificial intelligence
36:artificial intelligence
3219:Educational technology
3050:Reinforcement learning
2800:Process control system
2698:Computational geometry
2688:Algorithmic efficiency
2683:Analysis of algorithms
2331:Systems on Chip (SoCs)
2227:Loom Project Home Page
1422:Brachman, Ron (1978).
1232:10.1098/rspa.1966.0205
1176:Valuation-based system
1027:In the early years of
851:unique name assumption
736:
702:common-sense reasoning
609:in the 1970s and 80s,
516:common sense reasoning
491:General Problem Solver
129:
3298:Programming paradigms
3189:Electronic publishing
3159:Computational biology
3149:Computational physics
3045:Unsupervised learning
2959:Distributed computing
2835:Information retrieval
2742:Mathematical analysis
2732:Mathematical software
2615:Theory of computation
2580:Software construction
2570:Requirements analysis
2448:Software organization
2376:Computer architecture
2346:Hardware acceleration
2311:Printed circuit board
1296:10.1145/324634.325168
1212:Proc. R. Soc. Lond. A
1111:Chunking (psychology)
1054:declarative languages
902:Web Ontology Language
760:Web Ontology Language
731:
128:
3293:Scientific modelling
2949:Concurrent computing
2921:Ubiquitous computing
2893:Application security
2888:Information security
2717:Discrete mathematics
2693:Randomized algorithm
2645:Computability theory
2623:Model of computation
2595:Software maintenance
2590:Software engineering
2552:Software development
2502:Programming language
2497:Programming paradigm
2414:Network architecture
1872:Bih, Joseph (2006).
1495:Machine Intelligence
1161:Knowledge management
1073:lumped element model
1019:Ontology engineering
1013:Ontology engineering
725:first formalized by
603:cognitive revolution
596:Frederick Hayes-Roth
170:General game playing
3303:Automated reasoning
3224:Document management
3214:Operations research
3139:Enterprise software
3055:Multi-task learning
3040:Supervised learning
2762:Information systems
2585:Software deployment
2542:Software repository
2396:Real-time computing
2165:Arthur B. Markman:
2132:, MIT Press, 1995,
1575:Scientific American
1400:(4). Archived from
1224:1966RSPSA.294..235D
1171:Semantic technology
1005:automated reasoning
997:Expressive adequacy
843:non-monotonic logic
839:negation as failure
790:automated reasoning
531:A* search algorithm
322:Machine translation
238:Systems integration
175:Knowledge reasoning
112:Part of a series on
87:automated reasoning
3007:Search methodology
2954:Parallel computing
2911:Interaction design
2820:Computing platform
2747:Numerical analysis
2737:Information theory
2522:Software framework
2485:Software notations
2424:Network components
2321:Integrated circuit
2074:Hector J. Levesque
2070:Ronald J. Brachman
2054:Hector J. Levesque
2050:Ronald J. Brachman
2040:Ronald J. Brachman
1943:10.1007/bf00155580
1824:on 25 October 2013
1716:Russell, Stuart J.
1594:on April 24, 2013.
1511:. Addison-Wesley.
1354:. Addison-Wesley.
1060:, or are based on
1050:ontology languages
611:production systems
561:situation calculus
550:John Alan Robinson
542:mathematical logic
512:predicate calculus
130:
34:) is the field of
3270:
3269:
3199:Electronic voting
3129:Quantum Computing
3122:Applied computing
3108:Image compression
2878:Hardware security
2868:Security services
2825:Digital marketing
2605:Open-source model
2517:Modeling language
2429:Network scheduler
2119:Joseph Y. Halpern
2102:978-1-84800-285-2
2096:, Springer, 2009,
2065:978-1-55860-932-7
1983:978-0-934613-01-9
1858:978-0-934613-01-9
1697:978-0-934613-01-9
1655:978-0-201-10686-2
1551:978-0-934613-01-9
1361:978-0-201-10686-2
1290:. pp. 7–13.
1218:(1437): 235–259.
1146:Logic programming
1141:First-order logic
1062:first-order logic
1056:, and are either
1023:Ontology language
931:First Order Logic
922:Semantic networks
847:default reasoning
820:logic programming
815:expert systems.
801:First Order Logic
679:Texas Instruments
667:backward chaining
573:logic programming
546:resolution method
523:semantic networks
487:
486:
223:Bayesian networks
150:Intelligent agent
101:and classifiers.
91:inference engines
3310:
3260:
3259:
3250:
3249:
3240:
3239:
3060:Cross-validation
3032:Machine learning
2916:Social computing
2883:Network security
2678:Algorithm design
2600:Programming team
2560:Control variable
2537:Software library
2475:Software quality
2470:Operating system
2419:Network protocol
2284:Computer science
2277:
2270:
2263:
2254:
2253:
2212:by Pejman Makhfi
2149:Hermann Helbig:
2146:. ER 1996: 42-57
2027:
2024:
2018:
2015:
2009:
1994:
1988:
1987:
1961:
1955:
1954:
1926:
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1909:
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1666:
1660:
1659:
1635:
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1626:
1625:
1605:
1596:
1595:
1590:. Archived from
1565:
1556:
1555:
1529:
1523:
1522:
1504:
1498:
1483:
1477:
1476:
1465:10.1109/64.87683
1448:
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1264:
1263:
1261:
1253:Green, Cordell.
1250:
1244:
1243:
1207:
1201:
1200:
1192:
1121:Conceptual graph
970:Definitions and
828:production rules
626:inference engine
527:knowledge graphs
501:in 1959 and the
499:Herbert A. Simon
479:
472:
465:
386:Existential risk
208:Machine learning
109:
108:
99:model generators
89:engines include
3318:
3317:
3313:
3312:
3311:
3309:
3308:
3307:
3273:
3272:
3271:
3266:
3257:
3228:
3209:Word processing
3117:
3103:Virtual reality
3064:
3026:
2997:Computer vision
2973:
2969:Multiprocessing
2935:
2897:
2863:Security hacker
2839:
2815:Digital library
2756:
2707:Mathematics of
2702:
2664:
2640:Automata theory
2635:Formal language
2609:
2575:Software design
2546:
2479:
2465:Virtual machine
2443:
2439:Network service
2400:
2391:Embedded system
2364:
2297:
2286:
2281:
2200:
2036:
2034:Further reading
2031:
2030:
2025:
2021:
2016:
2012:
1995:
1991:
1984:
1962:
1958:
1927:
1923:
1913:
1911:
1907:
1881:IEEE Potentials
1876:
1870:
1866:
1859:
1841:
1837:
1827:
1825:
1810:
1806:
1797:
1795:
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1519:
1505:
1501:
1491:Wayback Machine
1484:
1480:
1449:
1445:
1437:
1426:
1420:
1416:
1407:
1405:
1386:
1382:
1374:Marvin Minsky,
1373:
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1342:
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1321:
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1276:
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1208:
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1189:
1184:
1156:Knowledge graph
1106:Belief revision
1097:
1058:frame languages
1025:
1017:Main articles:
1015:
927:lambda calculus
910:
908:Characteristics
776:procedural code
768:
615:frame languages
571:development of
483:
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372:Control problem
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165:Computer vision
140:
107:
95:theorem provers
17:
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3113:Solid modeling
3110:
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3012:Control method
3009:
3004:
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2964:Multithreading
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2858:Formal methods
2855:
2849:
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2838:
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2832:
2830:World Wide Web
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2199:
2198:External links
2196:
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2127:Moshe Y. Vardi
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1735:978-0134610993
1734:
1720:Norvig, Peter.
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961:expert systems
957:Incompleteness
954:
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882:
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869:
853:and a form of
780:expert systems
767:
764:
727:Brian C. Smith
691:frame language
630:knowledge base
622:knowledge base
607:expert systems
581:SLD resolution
559:developed the
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592:Ed Feigenbaum
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565:Cordell Green
562:
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507:John McCarthy
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278:Generative AI
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67:semantic nets
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2173:John F. Sowa
2166:
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2115:Ronald Fagin
2091:
2077:
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2008:, p. 437-439
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1934:
1930:
1924:
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1884:
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1826:. Retrieved
1822:the original
1817:
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1796:. Retrieved
1790:(1): 17–33.
1787:
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1592:the original
1582:(5): 34–43.
1579:
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1494:
1481:
1459:(3): 41–46.
1456:
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1406:. Retrieved
1402:the original
1397:
1393:
1383:
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1277:
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1255:
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1196:
1190:
1126:DIKW pyramid
1085:
1081:
1077:
1070:
1047:
1034:
1026:
914:Ron Brachman
911:
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886:Semantic Web
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740:Semantic Web
737:
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635:
625:
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600:
589:
585:Horn clauses
569:
554:
535:
520:
505:proposed by
503:Advice Taker
495:Allen Newell
488:
362:Chinese room
251:Applications
174:
64:
59:
31:
27:
23:
19:
18:
3204:Video games
3184:Digital art
2941:Concurrency
2810:Data mining
2722:Probability
2455:Interpreter
2123:Yoram Moses
1914:24 December
1828:10 December
1784:AI Magazine
1703:unsolvable.
1453:IEEE Expert
1394:AI Magazine
1330:AI Magazine
965:fuzzy logic
890:subsumption
835:Horn clause
659:Intellicorp
657:(KEE) from
636:Meanwhile,
391:Turing test
367:Friendly AI
138:Major goals
40:information
3277:Categories
3262:Glossaries
3134:E-commerce
2727:Statistics
2670:Algorithms
2628:Stochastic
2460:Middleware
2316:Peripheral
1937:: 67–110.
1798:2011-03-23
1624:2008-07-30
1408:2013-12-24
1182:References
1066:Tom Gruber
1032:diseases.
972:universals
942:reflection
925:after the
813:rule-based
744:ontologies
396:Regulation
350:Philosophy
305:Healthcare
300:Government
202:Approaches
83:ontologies
52:formalisms
3083:Rendering
3078:Animation
2709:computing
2660:Semantics
2351:Processor
946:Smalltalk
912:In 1985,
729:in 1985:
671:Symbolics
583:to treat
557:Pat Hayes
426:AI winter
327:Military
190:AI safety
60:reasoning
3242:Category
3070:Graphics
2845:Security
2507:Compiler
2406:Networks
2303:Hardware
1951:24696160
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1314:17211581
1240:21698093
1166:Mind map
1095:See also
766:Overview
579:, using
449:Glossary
443:Glossary
421:Progress
416:Timeline
376:Takeover
337:Projects
310:Industry
273:Finance
263:Deepfake
213:Symbolic
185:Robotics
160:Planning
28:KR&R
3252:Outline
1818:isi.edu
1489:at the
1220:Bibcode
663:forward
431:AI boom
409:History
332:Physics
105:History
2136:
2100:
2084:
2076:(eds)
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935:Prolog
824:Prolog
687:KL-ONE
677:, and
577:Prolog
381:Ethics
71:frames
2655:Logic
2489:tools
2162:2007.
1974:41–70
1947:S2CID
1908:(PDF)
1897:S2CID
1877:(PDF)
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1469:S2CID
1438:(PDF)
1427:(PDF)
1310:S2CID
1260:(PDF)
1236:S2CID
1137:FO(.)
1089:MYCIN
1008:time.
675:Xerox
642:frame
293:Music
288:Audio
75:rules
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2002:ISBN
1978:ISBN
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