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Noise Strategies for Improving Local Search
TLDR
It is shown that mixed random walk is the superior strategy for solving MAX-SAT problems, and results demonstrating the effectiveness of local search with walk for solving circuit synthesis and circuit diagnosis problems are presented.
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Planning as Satisfiability
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…
Pushing the Envelope: Planning, Propositional Logic and Stochastic Search
TLDR
Stochastic methods are shown to be very effective on a wide range of scheduling problems, but this is the first demonstration of its power on truly challenging classical planning instances.
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Local search strategies for satisfiability testing
TLDR
The power of local search for satissability testing can be further enhanced by employing a new strategy, called mixed random walk, for escaping from local minima, which allows us to handle formulas that are substantially larger than those that can be solved with basic local search.
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Constraint Propagation Algorithms for Temporal Reasoning
TLDR
Computing the consequences of temporal assertions is shown to be computationally intractable in the interval-based representation, but not in the point-based one, but a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.
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Activity recognition using the velocity histories of tracked keypoints
We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for…
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Evidence for Invariants in Local Search
TLDR
This work presents two statistical measures of the local search process that allow one to quickly find the optimal noise settings, and applies these principles to the problem of evaluating new search heuristics, and discovered two promising new strategies.
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Towards a theory of natural language interfaces to databases
TLDR
This paper proves that, for a broad class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL query, and shows that Precise compares favorably with Mooney's learning NLI and with Microsoft's English Query product.
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Unifying SAT-based and Graph-based Planning
TLDR
It is shown that STRIPS problems can be directly translated into SAT and efficiently solved using new randomized systematic solvers and that polynomialtime SAT simplification algorithms applied to the encoded problem instances are a powerful complement to the "mutex" propagation algorithm that works directly on the plan graph.
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Learning and inferring transportation routines
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