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In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a(More)
In this article, we describe a new approach that gives an explicit probabilistic interpretation for social networks. In particular, we focus on the observation that many existing Web-based trust-inference algorithms conflate the notions of “trust” and “confidence,” and treat the amalgamation of the two concepts to compute the trust(More)
Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a novel algorithm, called YoYo, for planning in nondeterministic domains under the assumption of full observability. This algorithm enables us to combine the power of search-control(More)
Despite the recent advances in planning with MDPs, the problem of generating good policies is still hard. This paper describes a way to generate policies in MDPs by (1) determinizing the given MDP model into a classical planning problem; (2) building partial policies off-line by producing solution plans to the classical planning problem and incrementally(More)
In many Web service composition problems, information may be needed from Web services during the composition process. Existing research on Web service composition (WSC) procedures has generally assumed that this information will not change. We describe two ways to take such WSC procedures and systematically modify them to deal with volatile information. The(More)
We design the simple hierarchical ordered planner (SHOP) and its successor, SHOP2, with two goals in mind: to investigate research issues in automated planning and to provide some simple, practical planning tools. SHOP and SHOP2 are based on a planning formalism called hierarchical task network planning. SHOP and SHOP2 use a search-control strategy called(More)
Researchers have developed a huge number of algorithms to solve classical planning problems. We provide a way to use these algorithms, unmodified, to generate strong-cyclic solutions in fully-observable nondeterministic planning domains. Our experiments show that when using our technique with FF and SGPlan (two well-known classical planners), its(More)