In this paper, we present a novel all-solutions preimage SAT solver, SOLALL, with the following features: (1) a new success-driven learning algorithm employing smaller cut sets; (...
We study the relative best-case performance of DPLL-based structure-aware SAT solvers in terms of the power of the underlying proof systems. The systems result from (i) varying th...
For applications with consecutive incoming training examples, on-line learning has the potential to achieve a likelihood as high as off-line learning without scanning all availabl...
In this paper, we propose an agent-centric approach to resource description and selection in a multiagent information retrieval (IR). In the multiagent system, each agent learns f...
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...