Artificial Intelligence - Special issue on knowledge representation
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Fast planning through planning graph analysis
Artificial Intelligence
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AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Temporal Planning through Mixed Integer Programming: A Preliminary Report
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Processes and continuous change in a SAT-based planner
Artificial Intelligence
External functions hosting by Graphplan
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
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Artificial Intelligence Review
On-line planning and scheduling: an application to controlling modular printers
Journal of Artificial Intelligence Research
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Many planning domains require a richer notion of time in which actions can overlap and have different durations. The key to fast performance in classical planners (e.g., Graphplan, IPP, and Blackbox) has been the use of a disjunctive representation with powerful mutual exclusion reasoning. This paper presents TGP, a new algorithm for temporal planning. TGP operates by incrementally expanding a compact planning graph representation that handles actions of differing duration. The key to TGP performance is tight mutual exclusion reasoning which is based on an expressive language for bounding mutexes and includes mutexes between actions and propositions. Our experiments demonstrate that mutual exclusion reasoning remains valuable in a rich temporal setting.