Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Planning for conjunctive goals
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Planning in polynomial time: the SAS-PUBS class
Computational Intelligence
Acquiring search-control knowledge via static analysis
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Automatically generating abstractions for planning
Artificial Intelligence
Complexity, decidability and undecidability results for domain-independent planning
Artificial Intelligence - Special volume on planning and scheduling
An autonomous spacecraft agent prototype
AGENTS '97 Proceedings of the first international conference on Autonomous agents
State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
Tractable plan existence does not imply tractable plan generation
Annals of Mathematics and Artificial Intelligence
A reactive planner for a model-based executive
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Admissible pruning strategies based on plan minimality for plan-space planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The 'MECIMPLAN' Approach to Agent-Based Strategic Planning
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The Causal Graph Revisited for Directed Model Checking
SAS '09 Proceedings of the 16th International Symposium on Static Analysis
Journal of Artificial Intelligence Research
The fast downward planning system
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
The computational complexity of dominance and consistency in CP-Nets
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
The role of macros in tractable planning over causal graphs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The computational complexity of dominance and consistency in CP-nets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
Causal graphs and structurally restricted planning
Journal of Computer and System Sciences
MECIMPLAN: an agent-based methodology for planning
International Journal of Intelligent Information and Database Systems
Analyzing search topology without running any search: on the connection between causal graphs and h+
Journal of Artificial Intelligence Research
The influence of k-dependence on the complexity of planning
Artificial Intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Algorithms and limits for compact plan representations
Journal of Artificial Intelligence Research
On the complexity of planning for agent teams and its implications for single agent planning
Artificial Intelligence
Computing upper bounds on lengths of transition sequences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A refined view of causal graphs and component sizes: SP-closed graph classes and beyond
Journal of Artificial Intelligence Research
Limitations of acyclic causal graphs for planning
Artificial Intelligence
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Unary operator domains - i.e., domains in which operators have a single effect - arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem - both are PSPACE-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG. More generally, we show that the number of paths in the causal graph is closely related to the complexity of planning in the associated domain. Finally we relate our results to the question of complexity of planning with serializable subgoals.