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.

A Conjunctive Query Language for Description Logic Aboxes

Abstract: A serious shortcoming of many Description Logic based knowledge representation systems is the inadequacy of their query languages. In this paper we present a novel technique that can be used to provide an expressive query language for such systems. One of the main advantages of this approach is that, being based on a reduction to knowledge base satisfiability, it can easily be adapted to most existing (and future) Description Logic implementations. We believe that providing Description Logic systems with an expressive query language for interrogating the knowledge base will significantly increase their utility.

Plan recognition and discourse analysis: an integrated approach for understanding dialogues

Abstract: One promising computational approach to understanding dialogues has involved modeling the goals of the speakers in the domain of discourse. In general, these models work well as long as the topic follows the goal structure closely, but they have difficulty accounting for interrupting subdialogues such as clarifications and corrections. Furthermore, such models are typically unable to use many processing clues provided by the linguistic phenomena of the dialogues. This dissertation presents a computational theory and partial implementation of a discourse level model of dialogue understanding. The theory extends and integrates plan-based and linguistic-based approaches to language processing, arguing that such a synthesis is needed to computationally handle many discourse level phenomena present in naturally occurring dialogues. The simple, fairly syntactic results of discourse analysis (for example, explanations of phenomena in terms of very local discourse contexts as well as correlations between syntactic devices and discourse function) will be input to the plan recognition system, while the more complex inferential processes relating utterances have been totally reformulated within a plan-based framework. Such an integration has led to a new model of plan recognition, one that constructs a hierarchy of domain and meta-plans via the process of constraint satisfaction. Furthermore, the processing of the plan recognizer is explicitly coordinated with a set of linguistic clues. The resulting framework handles a wide variety of difficult linguistic phenomena (for example, interruptions, fragmental and elliptical utterances, and presence as well as absence of syntactic discourse clues), while maintaining the computational advantages of the plan-based approach. The implementation of the plan recognition aspects of this framework also addresses two difficult issues of knowledge representation inherent in any plan recognition task.

Cp-logic: A language of causal probabilistic events and its relation to logic programming

Abstract: This paper develops a logical language for representing probabilistic causal laws. Our interest in such a language is two-fold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

Abstract: This paper is the first attempt to implement a knowledge-based diagnostic approach for the auto-body assembly process launch. This approach enables quick detection and localization of assembly process faults based on in-line dimensional measurements. The proposed approach includes an auto-body assembly knowledge representation and a diagnostic reasoning mechanism. The knowledge representation is comprised of the product, tooling, process, and measurement representations in the form of hierarchical groups. The diagnostic reasoning performs fault diagnostic in three steps. First, an initial statistical analysis of measurement data is performed. Next, the Candidate Component and Candidate Station with the hypothetical fault are searched. Finally, the fault symptom is identified and the root cause is suggested. Two case studies are presented to demonstrate the implementation of the proposed method.

Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

Abstract: Since knowledge in an expert system is vague and modified frequently, expert systems are fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. A generalized fuzzy Petri net model, called adaptive fuzzy Petri net (AFPN), is proposed with this object in mind. AFPN not only has the descriptive advantages of the fuzzy Petri net, it also has learning ability like a neural network. Just as other fuzzy Petri net (FPN) models, AFPN can be used for knowledge representation and reasoning, but AFPN has one important advantage: it is suitable for dynamic knowledge, i.e., the weights of AFPN are adjustable. Based on the AFPN transition firing rule, a modified backpropagation learning algorithm is developed to assure the convergence of the weights.