Towards a general theory of action and time
Artificial Intelligence
Reasoning about partially ordered events
Artificial Intelligence
On Lamport's interprocessor communication model
ACM Transactions on Programming Languages and Systems (TOPLAS)
Temporal reasoning based on semi-intervals
Artificial Intelligence
Effective solution of qualitative interval constraint problems
Artificial Intelligence
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
Time, Tense and Relativity Revisited
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
An Analysis of the Temporal Relations of Intervals in Relativistic Space-Time
IPMU '92 Proceedings of the 4th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems: Advanced Methods in Artificial Intelligence
Linear Time, Branching Time and Partial Order in Logics and Models for Concurrency, School/Workshop
Point algebras for temporal reasoning: algorithms and complexity
Artificial Intelligence
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Numerous AI problems in planning, robot motion, distributedsystems, cooperating agents, and intelligence gathering havedomains with sub-collections of events or actions over time whichare measured on incomparable or unsynchronized time scales fromone to another. In such situations, a temporal model providingonly a partial order on time moments is appropriate. Unlike abranching-time model, no sense of a common past and divergentfutures occurs; unlike a ’’parallel worlds‘‘ model, check pointsor intercommunication between the sub-collections of events mayexist, providing a true, rich partially ordered set of temporalinformation. In applications for which temporal intervals andtheir relations are appropriate, constraint propagation is arecognized reasoning tool. We discuss several temporal intervalmodels and their relationship to one another but particularlyfocus on the general partial order model. In each model the emphasisis on the atomic relations, so we amplify on the meaning ofatomic and show that what is atomic in one model may not be soin another. Utilizing results established earlier about the latticeproperties of the models, the paper describes the closure ofthe atomic relations under composition and conjunction, relatingthe structures of relations in linear time, partially orderedtime, and relativistic time. Lattice and algebraic isomorphismsexplain what appeared to be only coincidental similarities betweendifferent models. The results are shown to provide especiallyefficient representations for constraint propagation algorithms.