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.

The logic of typed feature structures : with applications to unification grammars, logic programs, and constraint resolution

Abstract: This book develops the theory of typed feature structures, a data structure that generalizes both first-order terms and feature structures of unification-based grammars to include inheritance, typing, inequality, cycles and intensionality The resulting synthesis serves as a logical foundation for grammars, logic programming and constraint-based reasoning systems A logical perspective is adopted which employs an attribute-value description language along with complete equational axiomatizations of the various systems of feature structures At the same time, efficiency concerns are kept in mind and complexity and representability results are provided The application of feature structures to phrase structure grammars is described and completeness results are shown for standard evaluation strategies Definite clause logic programs are treated as a special case of phrase structure grammars Constraint systems are introduced and an enumeration technique is developed for solving arbitrary attribute-value logic constraints This book, with its innovative approach to data structure, will be essential reading for researchers in computational linguistics, logic programming and knowledge representation Its self-contained presentation makes it flexible enough to serve as both a research tool and a text book

Large-scale semantic mapping and reasoning with heterogeneous modalities

Abstract: This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chaingraph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space.

Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems☆

Abstract: The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.

Knowledge Representation as the Basis for Requirements Specifications

Abstract: Specification of many kinds of knowledge about the world is essential to requirements engineering. Research on knowledge representation in artificial intelligence provides a wealth of relevant techniques that can be incorporated into specification languages.

Strategies for Diagnosis

Abstract: Diagnosis is among the dominant applications of expert systems technology today. In the past, most diagnostic systems were built using some form of rule-based production system. Recently, however, many new strategies have emerged to support much more complex reasoning for diagnosis. A survey of the architectures for the complex strategies now available for diagnosis is presented.