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.

Approximate reasoning in expert systems

Abstract: Follow up what we will offer in this article about approximate reasoning in expert systems. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

LaSSIE—a knowledge-based software information system

Abstract: The authors discuss the important problem of invisibility that is inherent in the task of developing large software systems. It is pointed out that there are no direct solutions to this problem; however, there are several categories of systems-relational code analyzers, reuse librarians, and project management databases-that can be seen as addressing aspects of the invisibility problem. It is argued that these systems do not adequately deal with certain important aspects of the problem of invisibility-semantic proliferation, multiple views, and the need for intelligent indexing. A system called LaSSIE, which uses knowledge representation and reasoning technology to address each of these three issues directly and thereby help with the invisibility problem, has been built. The authors conclude with an evaluation of the system and a discussion of open problems and ongoing work. >

Reasoning with very expressive fuzzy description logics

Abstract: It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN.

Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach

Abstract: The key idea behind cultural algorithms is to acquire problem solving knowledge (beliefs) from the evolving population and in return apply that knowledge to guide the search (R.G. Reynolds et al., 1993; 1996), In solving nonlinear constraint optimization problems, the key problem is how to represent and store the knowledge about the constraints. Previously, Chung (Chan-Jin Chung and R.G. Reynolds, 1996; 1998) used cultural algorithms to solve unconstraint optimization problems. Use was made of interval schemata proposed by L.J. Eshelman and J.D. Schaffer (1992) to represent global knowledge about the independent problem parameters. However, in constraint optimization, the problem intervals generally must be modified dependently. In order to solve constraint optimization problems, we need to extend the interval representation to allow for the representation of constraints. We define an n-dimensional regional based schema, called belief cell, which can provide an explicit mechanism to support the acquisition, storage and integration of knowledge about the constraints. In a cultural algorithm framework, the belief space can "contain" a set of these schemata, each of them can be used to guide the search of the evolving population, i.e. these kind of region based schemata can be used to guide the optimization search in a direct way by pruning the unfeasible regions and promoting the promising regions. We compared the results of 4 CA configurations that manipulate these schemata for an example problem.

Qualifying number restrictions in concept languages

Abstract: We investigate the subsumption problem in logic-based knowledge representation languages of the KL-ONE family. The language presented in this paper provides the constructs for conjunction, disjunction, and negation of concepts, as well as qualifying number restrictions. The latter ones generalize the well-known role quantifications (such as value restrictions) and ordinary number restrictions, which are present in almost all KL-ONE based systems. Until now, only little attempts were made to integrate qualifying number restrictions into concept languages. It turns out that all known subsumption algorithms which try to handle these constructs are incomplete, and thus detecting only few subsumption relations between concepts. We present a subsumption algorithm for our language which is sound and complete. Subsequently we discuss why the subsumption problem in this language is rather hard from a computational point of view. This leads to an idea of how to recognize concepts which cause tractable problems.