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Noise Strategies for Improving Local Search
TLDR
We show that mixed random walk improves upon the best known methods for solving MAX-SAT and circuit synthesis and circuit diagnosis problems. Expand
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Pushing the Envelope: Planning, Propositional Logic and Stochastic Search
Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithmExpand
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Local search strategies for satisfiability testing
TLDR
We introduce a new strategy, called mixed random walk, for escaping from local minima that allows us to handle formulas that are substantially larger than those that can be solved with basic local search. Expand
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Planning as Satisfiability
TLDR
SATPLAN04 is a updated version of the planning as satisfiability approach originally proposed in (Kautz & Selman 1992; 1996) using hand-generated translations, and implemented for PDDL input in the blackbox system. Expand
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Constraint Propagation Algorithms for Temporal Reasoning
TLDR
This paper considers computational aspects of several temporal representation languages. Expand
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Activity recognition using the velocity histories of tracked keypoints
TLDR
We present an activity recognition feature inspired by human psychophysical performance that performs comparably to local spatio-temporal features on the KTH activity recognition dataset. Expand
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Evidence for Invariants in Local Search
TLDR
This paper presents empirical evidence that such useful invariants (i.e.,properties that hold across strategies and domains) do indeed exist. Expand
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Unifying SAT-based and Graph-based Planning
TLDR
The Blackbox planning system unifies the planning as satisfiability framework (Kautz and Selman 1992, 1996) with the plan graph approach to STRIPS planning (Blum and Furst 1995). Expand
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Towards a theory of natural language interfaces to databases
TLDR
The need for Natural Language Interfaces (NLIs) to databases has become increasingly acute as more nontechnical people access information through their web browsers, PDAs and cell phones. Expand
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Learning and inferring transportation routines
TLDR
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. Expand
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