increased intelligence in the system, which AI algorithms have proven useful for demonstrational interfaces, and how we cope with such well-known intelligent-interface-usability issues as knowing what the system can do and what it is doing at any given moment. Demonstrational interfaces allow the user to perform actions on concrete example objects (often using direct manipulation), though the e...

ÐThis paper is concerned with the problem of measuring the uncertainty in a broad class of belief networks, as encountered in evidential reasoning applications. In our discussion, we give an explicit account of the networks concerned, and coin them the Dempster-Shafer (D-S) belief networks. We examine the essence and the requirement of such an uncertainty measure based on welldefined discrete e...

Although RDF is serialized using XML, the many possible syntactic forms and the need for inferencing make it difficult to query RDF using existing XML query languages. Numerous new query languages for RDF with built-in knowledge about the semantics of particular inferencing formalisms like RDF Schema and OWL have been proposed or are currently under development. However most, if not all, are sp...

Declarative logic programs (LP) based on the well-founded semantics (WFS) are widely used for knowledge representation (KR). Logical functions are desirable expressively in KR, but when present make LP inferencing become undecidable. In this paper, we present radial restraint: a novel approach to bounded rationality in LP. Radial restraint is parameterized by a norm that measures the syntactic ...

We address the goal of making Delegation Logic (DL) into a practically implementable and tractable trustmanagement system. DL [22] is a logic-based knowledge representation (i.e., language) for authorization in largescale, open, distributed systems. As introduced in [22], DL inferencing is computationally intractable and highly impractical to implement. We introduce a new version of Delegation ...

We s tudy the problem of identifying phrase structure. We formalize it as the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, and develop two general approaches for it. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observations s tructure and of general class...

Explanation facilities are a particularly important feature of expert system frameworks. It is an area in which traditional rule-based expert system frameworks have had mixed results. While explanations about control are well handled, facilities are needed for generating be. tter explanations concerning knowledge base content. This pap41r approaches the explanation problem by examining the effe...