Total-order and partial-order planning: a comparative analysis

Authors:
Steven Minton;John Bresina;Mark Drummond
Affiliations:
Recom Technologies, NASA Ames Research Center, Moffett Field, CA;Recom Technologies, NASA Ames Research Center, Moffett Field, CA;Recom Technologies, NASA Ames Research Center, Moffett Field, CA
Venue:
Journal of Artificial Intelligence Research
Year:
1995

Citing 11
Cited 6

Depth-first iterative-deepening: an optimal admissible tree search

Artificial Intelligence
Planning for conjunctive goals

Artificial Intelligence
Planning as search: a quantitative approach

Artificial Intelligence
Reasoning about partially ordered events

Artificial Intelligence
Heuristic sampling on backtrack trees

Heuristic sampling on backtrack trees
Iterative broadening

Artificial Intelligence
Systematic and nonsystematic search strategies

Proceedings of the first international conference on Artificial intelligence planning systems
Partial-order planning: evaluating possible efficiency gains

Artificial Intelligence
Multi-contributor causal structures for planning: a formalization and evaluation

Artificial Intelligence
Experimental results on the application of satisfiability algorithms to scheduling problems

AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Complete Determination of Parallel Actions and Temporal Optimization in Linear Plans of Action

EWSP '91 Proceedings of the European Workshop on Planning

Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds

Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds
Embedding landmarks and scenes in a computational model of institutions

COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
Refinement planning: status and prospectus

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Heuristic-biased stochastic sampling

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Model driven provisioning: bridging the gap between declarative object models and procedural provisioning tools

Middleware'06 Proceedings of the 7th ACM/IFIP/USENIX international conference on Middleware
Context-Aware Multi-Agent Planning in intelligent environments

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

For many years, the intuitions underlying partial-order planning were largely taken for granted. Only in the past few years has there been renewed interest in the fundamental principles underlying this paradigm. In this paper, we present a rigorous comparative analysis of partial-order and total-order planning by focusing on two specific planners that can be directly compared. We show that there are some subtle assumptions that underly the wide-spread intuitions regarding the supposed efficiency of partial-order planning. For instance, the superiority of partial-order planning can depend critically upon the search strategy and the structure of the search space. Understanding the underlying assumptions is crucial for constructing efficient planners.