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.

Nonmonotonic databases and epistemic queries

Abstract: The approach to database query evaluation developed by Levesque and Reiter treats databases as first order theories, and queries as formulas of the language which includes, in addition to the language of the database, an epistemic modal operator. In this epistemic query language, one can express questions not only about the external world described by the database, but also about the database itself-- about what the database knows. On the other hand, epistemic formulas are used in knowledge representation for the purpose of expressing defaults. Autoepistemic logic is the best known epistemic nonmonotonic formalism; the logic of grounded knowledge, proposed recently by Lin and Shoham, is another such system. This paper brings these two directions of research together. We describe a new version of the Lin/Shoham logic, similar in spirit to the Levesque/Reiter theory of epistemic queries. Using this formalism, we can give meaning to epistemic queries in the context of nonmonotonic databases, including logic programs with negation as failure.

Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments

Abstract: A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented.

Principles and Practice of Semantic Web Reasoning

Abstract: Architectures.- SomeWhere in the Semantic Web.- A Framework for Aligning Ontologies.- A Revised Architecture for Semantic Web Reasoning.- Semantic Web Architecture: Stack or Two Towers?.- Languages.- Ten Theses on Logic Languages for the Semantic Web.- Semantic and Computational Advantages of the Safe Integration of Ontologies and Rules.- Logical Reconstruction of RDF and Ontology Languages.- Marriages of Convenience: Triples and Graphs, RDF and XML in Web Querying.- Descriptive Typing Rules for Xcerpt.- A General Language for Evolution and Reactivity in the Semantic Web.- Reasoning.- Use Cases for Reasoning with Metadata or What Have Web Services to Do with Integrity Constraints?.- Principles of Inductive Reasoning on the Semantic Web: A Framework for Learning in -Log.- Computational Treatment of Temporal Notions: The CTTN-System.- A Geospatial World Model for the Semantic Web.- Generating Contexts for Expression Data Using Pathway Queries.

Deep versus compiled knowledge approaches to diagnostic problem-solving

Abstract: Most of the current generation expert systems use knowledge which does not represent a deep understanding of the domain, but is instead a collection of “pattern?action” rules, which correspond to the problem-solving heuristics of the expert in the domain. There has thus been some debate in the field about the need for and role of “deep” knowledge in the design of expert systems. It is often argued that this underlying deep knowledge will enable an expert system to solve hard problems. In this paper we consider diagnostic expert systems and argue that given a body of underlying knowledge that is relevant to diagnostic reasoning in a medical domain, it is possible to create a diagnostic problem-solving structure which has all the aspects of the underlying knowledge needed for diagnostic reasoning “compiled” into it. It is argued this compiled structure can solve all the diagnostic problems in its scope efficiently, without any need to access the underlying structures. We illustrate such a diagnostic structure by reference to our medical system MDX. We also analyze the use of these knowledge structures in providing explanations of diagnostic reasoning.