Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Graph minors: X. obstructions to tree-decomposition
Journal of Combinatorial Theory Series B
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Planning in polynomial time: the SAS-PUBS class
Computational Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth
SIAM Journal on Computing
State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Journal of Combinatorial Theory Series B
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Constraint Processing
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Digraph measures: Kelly decompositions, games, and orderings
Theoretical Computer Science
Concise finite-domain representations for PDDL planning tasks
Artificial Intelligence
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Accuracy of admissible heuristic functions in selected planning domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
New Islands of tractability of cost-optimal planning
Journal of Artificial Intelligence Research
Planning over chain causal graphs for variables with domains of size 5 Is NP-hard
Journal of Artificial Intelligence Research
Approximation Algorithms for Treewidth
Algorithmica
Causal graphs and structurally restricted planning
Journal of Computer and System Sciences
Planning with h+in theory and practice
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
A general, fully distributed multi-agent planning algorithm
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Strengthening Landmark Heuristics via Hitting Sets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
Analyzing search topology without running any search: on the connection between causal graphs and h+
Journal of Artificial Intelligence Research
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
The dag-width of directed graphs
Journal of Combinatorial Theory Series B
Multi-agent A* for parallel and distributed systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Parameterized Complexity
On the complexity of planning for agent teams and its implications for single agent planning
Artificial Intelligence
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For almost two decades, monotonic, or "delete free," relaxation has been one of the key auxiliary tools in the practice of domain-independent deterministic planning. In the particular contexts of both satisficing and optimal planning, it underlies most state-of-theart heuristic functions. While satisficing planning for monotonic tasks is polynomial-time, optimal planning for monotonic tasks is NP-equivalent. Here we establish both negative and positive results on the complexity of some wide fragments of optimal monotonic planning, with the fragments being defined around the causal graph topology. Our results shed some light on the link between the complexity of general optimal planning and the complexity of optimal planning for the respective monotonic relaxations.