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Planning is a notoriously hard combinatorial search problem. In many i n teresting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning(More)
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 video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In addition,(More)
It has recently been shown that local search is surprisingly good at nding satisfying assignments for certain classes of CNF formulas (Sel-man et al. 1992). In this paper we demonstrate that 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. We(More)
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. Yet NLIs are only usable if they map natural language questions to SQL queries <i>correctly</i>. We introduce the <sc>Precise</sc> NLI [2], which reduces the semantic(More)
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's destination and mode of transportation. To achieve efficient inference, we(More)