explores is the relaying of packets in a simple butterfly network. Despite its apparent simplicity, this problem enables to illustrate key features of situations of uncertain knowledge that arise in networks. The paper presents two impossibility facts and one possibility fact, in the latter of which a

Our knowledge forms a highly interconnected and dynamically changing body of propositions. One obviously important way that knowledge changes is via rational inference, based either upon new insight into the content of what we already know or upon new knowledge provided by the senses. The most

In this paper, we analyze why and how formal ontologies and probability theories should be combined and give an example of such a combination in a spatial system. 1

Abstract:- The paper discusses four types of knowledge concepts, especially three kinds of uncertain knowledge, based on relationships among data, information and meaning. The goal is to fuse research results on knowledge engineering and epistemology. We analyze four kinds of basic connection forms

Abstract This paper addresses the problem of bridging the gap between the fields of Knowledge Renresentation OCR) and Uncertain Reasoning (UR). The prohosed solution consists of a framework for representing uncertain knowledge in which two components, one dealing with (categorical) knowledge

framework for uncertainty handling in a description-based event recognition approach. The pro-posed approach allows the flexible modeling of composite events with complex temporal constraints. It uses proba-bility theory to provide a consistent framework for dealing with uncertain knowledge

in combination with first-order inference rules and recursive queries over RDF-like knowledge bases. In the spirit of data lineage, the intensional (i.e., rule-based) structure of query answers is reflected by Boolean formulas that capture the logical dependencies of each derived answer fact back to its

of the particular situation. Thus, the agents we implemented compute the expected utilities of various alternative communicative acts, and execute the best one. The agents use a probabilistic frame-based knowledge formalism to represent the uncertain information they have about the domain and about the other agents

Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, these approaches still make significant assumptions that limit their applicability for real-world planning. This work describes how one of these assumptions: that the world is perfectly known, can be removed. We propose a predictive roadmap planner that incorporates uncertainty directly into the planning process. This enables the planner to identify configuration space paths that minimize the risk due to uncertainty and, when necessary, directs sensing to reduce uncertainty. Experimental results in several domains indicate that the predictive roadmap is adept at planning despite uncertainty in its perception of the workspace. 1

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