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.

Optimization Models for Training Belief-Rule-Based Systems

Abstract: A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.

Inside the LOOM description classifier

Abstract: This paper presents a symbol level account of some of the representation and reasoning structures within the LOOM knowledge representation system. Reasoning in LOOM centers around a classifier whose primary function is to construct a taxonomy of all descriptions that have been entered into the system. The LOOM classifier is unique in that it constructs a separate taxonomy for each of seven kinds of non-composite descriptions, and uses a marker passing algorithm to replace the quadratic time subsumption test found in most classifiers with a linear time test. We briefly illustrate how the selection of data structures within LOOM impacts the completeness of the classification algorithm, and we describe the LOOM option that allows concepts to be reasoned with in either a forward-chaining or a backward-chaining mode.