Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Chapter 7 Answer Sets

Abstract: Publisher Summary This chapter discusses Answer Set Prolog, which is a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs. The language has roots in the declarative programming, syntax, and semantics of standard Prolog, disjunctive databases, and nonmonotonic logic. Unlike standard Prolog, it allows expressing disjunction and classic or strong negation. It differs from many other knowledge representation languages by its ability to represent defaults. A substantial part of education consists in learning various defaults, exceptions to these defaults, and the ways of using this information to draw reasonable conclusions about the world and the consequences of one's actions. Answer Set Prolog provides a powerful logical model of this process. Its syntax allows a simple representation of defaults and their exceptions, its consequence relation characterizes the corresponding set of valid conclusions, and its inference mechanisms allow a program to find these conclusions in a reasonable amount of time.

Rule-Based Expert Systems

Abstract: Rule-based systems (also known as production systems or expert systems) are the simplest form of artificial intelligence. A rule based system uses rules as the knowledge representation for knowledge coded into the system [1][3][4] [13][14][16][17][18][20]. The definitions of rule-based system depend almost entirely on expert systems, which are system that mimic the reasoning of human expert in solving a knowledge intensive problem. Instead of representing knowledge in a declarative, static way as a set of things which are true, rule-based system represent knowledge in terms of a set of rules that tells what to do or what to conclude in different situations.

Polynomial time reasoning in a Description Logic with existential restrictions, GCI axioms, and—what else?

Abstract: In the area of Description Logic (DL) based knowledge representation, research on reasoning w.r.t. general terminologies has mainly focused on very expressive DLs. Recently, though, it was shown for the DL ℇL, providing only the constructors conjunction and existential restriction, that the subsumption problem w.r.t. cyclic terminologies can be decided in polynomial time, a surprisingly low upper bound. In this paper, we show that even admitting general concept inclusion (GCI) axioms and role hierarchies in ℇL terminologies preserves the polynomial time upper bound for subsumption. We also show that subsumption becomes co-NP hard when adding one of the constructors number restriction, disjunction, and 'allsome', an operator used in the DL K-REP. implication of the first result is that reasoning over the widely used medical terminology SNOMED is possible in polynomial time.

Control of selective perception using Bayes nets and decision theory

Abstract: A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision-making are central issues for selective vision, which takes advantage of prior knowledge of a domain''s abstract and geometrical structure and models for the expected performance and cost of visual operators. .pp The TEA-1 selective vision system uses Bayes nets for representation and benefit-cost analysis for control of visual and non-visual actions. It is the high-level control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA-1 demonstrates that Bayes nets and decision theoretic techniques provide a general, re-usable framework for constructing computer vision systems that are selective perception systems, and that Bayes nets provide a general framework for representing visual tasks. Control, or decision making, is the most important issue in a selective vision system. TEA-1''s decisions about what to do next are based on general hand-crafted ``goodness functions'''' constructed around core decision theoretic elements. Several goodness functions for different decisions are presented and evaluated. .pp The TEA-1 system solves a version of the T-world problem, an abstraction of a large set of domains and tasks. Some key factors that affect the success of selective perception are analyzed by examining how each factor affects the overall performance of TEA-1 when solving ensembles of randomly produced, simulated T-world domains and tasks. TEA-1''s decision making algorithms are also evaluated in this manner. Experiments in the lab for one specific T-world domain, table settings, are also presented.

Legal knowledge engineering : a modelling approach

Abstract: Although the field of Artificial Intelligence and Law has matured considerably, there is still no comprehensive view on the field, its achievements, and no agenda or clear direction for research. Moreover, present approaches to the development of legal knowledge-based systems (LKBS) - such as the use of rule-based systems, case-based systems, or logics - have obtained somewhat limited theoretical and practical results. This book provides a critical overview of the field by describing present approaches and analysing their problems in detail. A new "modelling approach" to legal knowledge engineering is proposed to address these problems and provide an agenda for research and development. This approach applies recent developments in knowledge modelling to the law domain. The book's central premise, that the development of LK BS should be centred on the elaboration of explicit models of law, is well demonstrated, it is an extremely worthwhile read for anyone interested in the theoretical foundations of AI and law and knowledge representation in particular.