Temporal reasoning and planning
Reasoning about plans
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
State-space planning by integer optimization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Discovering State Constraints in DISCOPLAN: Some New Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
On the use of integer programming models in AI planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
The LPSAT engine & its application to resource planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Temporal planning with mutual exclusion reasoning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Processes and continuous change in a SAT-based planner
Artificial Intelligence
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Processes and continuous change in a SAT-based planner
Artificial Intelligence
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Temporal planning is an important problem, as in many real world planning domains actions have different durations and the goals should be achieved by a specified deadline, or as soon as possible. This paper presents a novel approach to temporal planning that is based on Mixed Integer Programming. In the new framework, a temporal planning domain is modeled by two sets of linear inequalities. The first set involves integer variables and is a Graphplan-like encoding of a simplification of the original problem where the duration of the actions is ignored. The second set involves both integer and real valued variables, and models the temporal aspects of the problem. The two sets interact through the common integer variables, and their combination can be solved by using available Mixed Integer Programming software. The new method aims at generating good solutions quickly, under different minimization objectives. Preliminary experimental results illustrate the effectiveness of our approach.