Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Graph searching and a min-max theorem for tree-width
Journal of Combinatorial Theory Series B
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Automatically generating abstractions for planning
Artificial Intelligence
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Machine intelligence 14
Fast planning through planning graph analysis
Artificial Intelligence
Distributed problem solving and planning
Multiagent systems
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Fixed-parameter complexity in AI and nonmonotonic reasoning
Artificial Intelligence
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Constraint Processing
Concise finite-domain representations for PDDL planning tasks
Artificial Intelligence
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
Structure and complexity in planning with unary operators
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Abstract reasoning for planning and coordination
Journal of Artificial Intelligence Research
The complexity of planning problems with simple causal graphs
Journal of Artificial Intelligence Research
New Islands of tractability of cost-optimal planning
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Scope and abstraction: two criteria for localized planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Approximation Algorithms for Treewidth
Algorithmica
A general, fully distributed multi-agent planning algorithm
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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If the complexity of planning for a single agent is described by some function f of the input, how much more difficult is it to plan for a team of n cooperating agents? If these agents are completely independent, we can simply solve n single agent problems, scaling linearly with the number of agents. But if all the agents interact tightly, we really need to solve a single problem that is n times larger, which could be exponentially (in n) harder to solve. Is a more general characterization possible? To formulate this question precisely, we minimally extend the standard STRIPS model to describe multi-agent planning problems. Then, we identify two problem parameters that help us answer our question. The first parameter is independent of the precise task the multi-agent system should plan for, and it captures the structure of the possible direct interactions between the agents via the tree-width of a graph induced by the team. The second parameter is task-dependent, and it captures the minimal number of interactions by the ''most interacting'' agent in the team that is needed to solve the problem. We show that multi-agent planning problems can be solved in time exponential only in these parameters. Thus, when these parameters are bounded, the complexity scales only polynomially in the size of the agent team. These results also have direct implications for the single-agent case: by casting single-agent planning tasks as multi-agent planning tasks, we can devise novel methods for decomposition-based planning for single agents. We analyze one such method, and use the techniques developed to provide some of the strongest tractability results for classical single-agent planning to date.