Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Tree clustering for constraint networks (research note)
Artificial Intelligence
Divide and conquer in multi-agent planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Controlling communication in distributed planning using irrelevance reasoning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient Approximation for Triangulation of Minimum Treewidth
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
Conformant planning via symbolic model checking
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Theorem proving with structured theories
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Scope and abstraction: two criteria for localized planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Logic-based subsumption architecture
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Loosely coupled formulations for automated planning: an integer programming perspective
Journal of Artificial Intelligence Research
The role of macros in tractable planning over causal graphs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Factored planning using decomposition trees
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hierarchical heuristic forward search in Stochastic domains
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental heuristic search for planning with temporally extended goals and uncontrollable events
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Completeness and optimality preserving reduction for planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decomposition of Multi-player Games
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Combining planning and motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
The role of macros in tractable planning
Journal of Artificial Intelligence Research
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
The influence of k-dependence on the complexity of planning
Artificial Intelligence
Synthesizing plans for multiple domains
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
On the complexity of planning for agent teams and its implications for single agent planning
Artificial Intelligence
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We present a general-purpose method for dynamically factoring a planning domain, whose structure is then exploited by our generic planning method to find sound and complete plans. The planning algorithm's time complexity scales linearly with the size of the domain, and at worst exponentially with the size of the largest subdomain and interaction between subdomains. The factorization procedure divides a planning domain into subdomains that are organized in a tree structure such that interaction between neighboring subdomains in the tree is minimized. The combined planning algorithm is sound and complete, and we demonstrate it on a representative planning domain. The algorithm appears to scale to very large problems regardless of the black box planner used.