Predstavljanje znanja i rezonovanje

Predstavljanje znanja i rezonovanje (KRR, KR&R, KR²) polje je veštačke inteligencije (VI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems, and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.

Examples of knowledge representation formalisms include semantic nets, frames, rules, logic programs and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, model generators and classifiers.

Pregled

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.

The justification for knowledge representation is that conventional procedural code 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 expert systems.

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.

Knowledge representation goes hand in hand with automated reasoning 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 knowledge representation languages have a reasoning or inference engine as part of the system.

A key trade-off in the design of knowledge representation formalisms is that between expressivity and tractability. First Order Logic (FOL), with its high expressive power and ability to formalise much of mathematics, is a standard for comparing the expressibility of knowledge representation languages.

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 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.

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 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 rule-based expert systems.

A similar balancing act was also a motivation for the development of logic programming (LP) and the logic programming language Prolog. Logic programs have a rule-based syntax, which is easily confused with the IF-THEN syntax of production rules. But logic programs have a well-defined logical semantics, whereas production systems do not.

The earliest form of logic programming was based on the Horn clause subset of FOL. But later extensions of LP included the negation as failure inference rule, which turns LP into a non-monotonic logic for default reasoning. 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 unique name assumption and a form of closed world assumption. These assumptions are much harder to state and reason with explicitly using the standard semantics of FOL.

In a key 1993 paper on the topic, Randall Davis of MIT outlined five distinct roles to analyze a knowledge representation framework:

• "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."
• "It is a set of ontological commitments", i.e., "an answer to the question: In what terms should I think about the world?"
• "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."
• "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."
• "It is a medium of human expression", i.e., "a language in which we say things about the world."

Knowledge representation and reasoning are a key enabling technology for the Semantic Web. 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 subsumption 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.

The Semantic Web integrates concepts from knowledge representation and reasoning with markup languages based on XML. The Resource Description Framework (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 Web Ontology Language (OWL) adds additional semantics and integrates with automatic classification reasoners.

Literatura

• Ronald J. Brachman; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983
• Ronald J. Brachman, Hector J. Levesque Knowledge Representation and Reasoning, Morgan Kaufmann, 2004 ISBN 978-1-55860-932-7
• Ronald J. Brachman, Hector J. Levesque (eds) Readings in Knowledge Representation, Morgan Kaufmann, 1985, ISBN 0-934613-01-X
• Chein, M., Mugnier, M.-L. (2009),Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs, Springer, 2009,ISBN 978-1-84800-285-2.
• Randall Davis, Howard Shrobe, and Peter Szolovits; What Is a Knowledge Representation? AI Magazine, 14(1):17-33,1993
• Ronald Fagin, Joseph Y. Halpern, Yoram Moses, Moshe Y. Vardi Reasoning About Knowledge, MIT Press, 1995, ISBN 0-262-06162-7
• Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland: Understanding Implementations of IS-A Relations. ER 1996: 42-57
• Hermann Helbig: Knowledge Representation and the Semantics of Natural Language, Springer, Berlin, Heidelberg, New York 2006
• Frank van Harmelen, Vladimir Lifschitz and Bruce Porter: Handbook of Knowledge Representation 2007.
• Arthur B. Markman: Knowledge Representation Lawrence Erlbaum Associates, 1998
• John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000
• Adrian Walker, Michael McCord, John F. Sowa, and Walter G. Wilson: Knowledge Systems and Prolog, Second Edition, Addison-Wesley, 1990
• Mary-Anne Williams and Hans Rott: "Frontiers in Belief Revision, Kluwer", 2001.

Spoljašnje veze

• What is a Knowledge Representation? by Randall Davis and others
• Introduction to Knowledge Modeling by Pejman Makhfi
• Introduction to Description Logics course by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy
• DATR Lexical knowledge representation language
• Loom Project Home Page
• Principles of Knowledge Representation and Reasoning Incorporated
• Description Logic in Practice: A CLASSIC Application
• The Rule Markup Initiative
• Nelements KOS - a non-free 3d knowledge representation system