Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds
Hierarchical reasoning about inequalities
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Hierarchical reasoning about inequalities
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Integrating metric and qualitative temporal reasoning
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Metric constraints for maintaining appointments: dates and repeated activities
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
A constraint decomposition method for spatio-temporal configuration problems
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Planning and temporal reasoning under uncertainty
PKWBS-W'84 Proceedings of the 1984 IEEE conference on Principles of knowledge-based systems
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This paper describes a new approach to representing and reasoning with temporal and spatial information. A wide variety of temporal and spatial specifications can be converted into linear inequalities relating the midpoints of events or boundary surfaces of objects respectively, Linear programming is then used to represent these constraints and perform deductions. The temporal information is modularized into semantically related clusters of events each with its own tableau and related to each other by a reference frame transformation. A similar grouping can be done for objects making the system computationally efficient. For temporal reasoning, the system is formally adequate except for linguistic fuzziness. For geometric reasoning, polyhedra can be represented by allowing paramvtiization. The uniformity of the time and space representation makes this approach particularly attractive.