Default reasoning about spatial occupancy
Artificial Intelligence
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A spatio-temporal logic for the specification and refinement of mobile systems
FASE'03 Proceedings of the 6th international conference on Fundamental approaches to software engineering
An HMM/MRF-based stochastic framework for robust vehicle tracking
IEEE Transactions on Intelligent Transportation Systems
An AGM-style belief revision mechanism for probabilistic spatio-temporal logics
Artificial Intelligence
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There are numerous cases where we need to reason about vehicles whose intentions and itineraries are not known in advance to us. For example, Coast Guard agents tracking boats don't always know where they are headed. Likewise, in drug enforcement applications, it is not always clear where drug-carrying airplanes (which do often show up on radar) are headed, and how legitimate planes with an approved flight manifest can avoid them. Likewise, traffic planners may want to understand how many vehicles will be on a given road at a given time. Past work on reasoning about vehicles (such as the "logic of motion" by Yaman et. al. [Yaman et al., 2004]) only deals with vehicles whose plans are known in advance and don't capture such situations. In this paper, we develop a formal probabilistic extension of their work and show that it captures both vehicles whose itineraries are known, and those whose itineraries are not known. We show how to correctly answer certain queries against a set of statements about such vehicles. A prototype implementation shows our system to work efficiently in practice.