Artificial Intelligence
The implementation of a model-based belief revision system
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
Propositional knowledge base revision and minimal change
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning
Applied Intelligence
Sweetening Ontologies with DOLCE
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Axiomatic characterization of the AGM theory of belief revision in a temporal logic
Artificial Intelligence
Information-gathering: from sensor data to decision support in three simple steps
Intelligent Decision Technologies
Minimal change and maximal coherence: a basis for belief revision and reasoning about actions
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
FireGrid: An e-infrastructure for next-generation emergency response support
Journal of Parallel and Distributed Computing
A resource enhanced HTN planning approach for emergency decision-making
Applied Intelligence
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Effective response to emergencies depends upon the availability of accurate and focused information. The goal of the FireGrid project is to provide an architecture by which the results of computer models of physical phenomena can be made available to decision-makers leading the response to fire emergencies in the built environment. In this paper we discuss the application of a number of AI techniques in the development of FireGrid systems, and include algorithms developed for reasoning about dynamic situations. It is intended that this paper will be of technical interest to those who have to construct agents that are able to reason about the complexities of the real world, and of more general appeal to those interested in the ontological and representational commitments and compromises that underlie this reasoning.