--- together with well-defined meta-level reasoning algorithms --- provide for efficient reasoning about a large class of spatial relations. 1 INTRODUCTION This work has developed out of research done by Randell, Cui and Cohn (henceforth RCC) on formalising spatial and temporal concepts used in describing

Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon

extension by some specific concrete domain. We shall define a terminological and an assertional language, and consider the important inference problems such as subsumption, instantiation, and consistency. The formal semantics as well as the reasoning algorithms are given on the scheme level. In contrast

This paper describes a novel methodology for meta-level control of approximate reasoning. We show that approximate reasoning performed by anytime algorithms offers a simple means by which an intelligent system can trade-off decision quality for deliberation cost. The model exploits

concept language (or description logic), which is given well-defined set-theoretic semantics. The efficiency of reasoning has often been advocated as a primary motivation for the use of such systems. Deduction methods and computational properties of reasoning problems in concept languages are the subject

Description Logics (DLs) are used in knowledge-based systems to represent and reason about terminological knowledge of the application domain in a semantically well-defined manner. In this thesis, we establish a number of novel complexity results and give practical algorithms for expressive DLs

that endows agents with the capability to choose the appropriate coordination algorithm from a set of available coordination algorithms based on meta-level information about their problem solving situations. We present empirical results that strongly indicate the effectiveness of the learning algorithm. 1

resources by control activities such as scheduling and coordination, which may degrade an agent's performance. This paper describes work in progress to dene and build a meta-level control framework which will account for the cost of making appropriate decisions about these control activities

semantically well-founded logic for approximate reasoning, which is justifiable from the intuitive point of view, and to provide fast algorithms for dealing with it even when using expressive languages. We also want our logic to be useful to perform approximate reasoning in different contexts. We define a

from pixel to pixel and there is noise correlation between pixels. For similar reasons the noise level does not have the common square-root dependence on the number of samples taken. In the case of Cartesian sampling with reconstruction as initially explained, this can be made yet clearer. For one