Abstract. In this paper we study the complexity of STRIPS planning when operators have a single effect. In particular, we show how the structure of the domain's causal graph influences the complexity of planning. Causal graphs relate between preconditions and effects of domain operators. They were introduced by Williams and Nayak, who studied unary operator domains because of their direct applicability to the control of NASA's Deep-Space One spacecraft. Williams and Nayak's reactive planner can be trivially extended into a polynomial time plan generator in the context of tree-structured causal graphs. In this paper, we treat more complex causal graph structures, such as undirected polytrees, singly-connected networks, and general DAGs. We show that a polynomial time plan generation algorithm exists for graphs that induce an undirected polytree. More generally, we show that a certain relation exists between the number of paths in the causal graph and the complexity of planning in the associated domain. 1
|
579
|
Planning for conjunctive goals
– Chapman
- 1987
|
|
389
|
UCPOP: A sound, complete, partial order planner for ADL
– Penberthy, Weld
- 1992
|
|
234
|
An Introduction to Least Commitment Planning
– Weld
- 1994
|
|
199
|
The computational complexity of propositional STRIPS planning
– Bylander
- 1994
|
|
181
|
Planning as Search: A Quantitative Approach
– Korf
- 1987
|
|
157
|
A Model-based Approach to Reactive Self-Configuring Systems
– Williams, Nayak
- 1996
|
|
150
|
Automatically generating abstractions for planning
– Knoblock
- 1994
|
|
124
|
Macro operators: A weak method for learning
– Korf
- 1985
|
|
102
|
Complexity, decidability and undecidability results for domain-indepenedent planning
– Erol, Nau, et al.
- 1994
|
|
88
|
Acquiring search-control knowledge via static analysis
– Etzioni
- 1993
|
|
85
|
Recent advances in AI planning
– Weld
- 1999
|
|
72
|
Reasoning with conditional ceteris paribus preference statements
– Boutilier, Brafman, et al.
- 1999
|
|
69
|
Complexity results for SAS + planning
– Bäckström, Nebel
- 1995
|
|
62
|
An Autonomous Spacecraft Agent Prototype
– Pell, Bernard, et al.
- 1998
|
|
52
|
Postponing threats in partial-order planning
– Smith, Peot
- 1993
|
|
52
|
A reactive planner for a model-based executive
– Williams, Nayak
- 1997
|
|
43
|
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
– Koehler, Hoffmann
|
|
33
|
Parallel non-binary planning in polynomial time
– Backstrom, Klein
- 1991
|
|
27
|
Planning in polynomial time: the sas-pubs class
– Backstrom, Klein
- 1991
|
|
22
|
Complexity Results for Serial Decomposability
– Bylander
- 1992
|
|
19
|
Incremental planning
– Jonsson, Backstrom
- 1995
|
|
14
|
State-variable planning under structural restrictions: Algorithms and complexity
– Jonsson, Bäckström
- 1998
|
|
10
|
A Management Guide to PERT/CPM
– Wiest, Levy
- 1974
|
|
7
|
Tractable plan existence does not imply tractable plan generation
– Jonsson, Bäckström
- 1998
|
|
7
|
Admissible pruning strategies based on plan minimality for planspace planning
– Kambhampati
- 1995
|
|
7
|
The Computational Complexity of
– Bylander
- 1994
|
|
6
|
Multi-agent off-line coordination: Structure and complexity
– Domshlak, Dinitz
- 2001
|
|
6
|
Complexity results for
– Backstrom, Nebel
- 1995
|
|
3
|
Efficient probabilistic reasoning in Bayes nets with mutual exclusion and context specific independence
– Domshlak, Shimony
- 2003
|
|
2
|
E#cient probabilistic reasoning in Bayes nets with mutual exclusion and context specific independence
– Shimony
- 2003
|
|
2
|
Complexity of probabilistic reasoning in (directedpath) singly connected (not polytree!) Bayes networks. submitted for publication
– Shimony
- 2002
|
|
2
|
Integrating Efficient Model-Learning and Problem-Solving Algorithms in Permutation Environments
– Chalasani, Etzioni, et al.
- 1991
|
|
2
|
Macro-operators: AWeak Method for Learning
– Korf
- 1985
|
|
1
|
Integrating e#cient model-learning and problem-solving algorithms in permutation environments
– Chalasani, Etzioni
- 1991
|
|
1
|
Multi-agent o#-line coordination: Structure and complexity
– Domshlak
- 2001
|
|
1
|
Complexity in Planning with Unary Operators
– Structure
- 1994
|
|
1
|
A A Short Review of POP, Causal Links and Threats We represent a plan as a tuple: hA; O; Li, where A is a set of unary operators, O is a set of ordering constraints over A, and L is a set of causal links. For example, if A = fA 1 ; A 2 ; A 3 g then O migh
– Weld
- 1999
|
|
