Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fast planning through planning graph analysis
Artificial Intelligence
Act, and the rest will follow: exploiting determinism in planning as satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast planning through greedy action graphs
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
CPlan: a constraint programming approach to planning
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Artificial Intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Solving the Sports League Scheduling Problem with Tabu Search
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Beyond the Plan-Length Criterion
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
An Empirical Evaluation of the Effectiveness of Local Search for Replanning
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Detection and Exploitation of Symmetry in Planning Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The LPSAT Engine & Its Application to Resource Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Temporal Planning through Mixed Integer Programming: A Preliminary Report
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Lagrange Multipliers for Local Search on Planning Graphs
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
Learning to improve both efficiency and quality of planning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Learning from planner performance
Artificial Intelligence
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Planning in domains with derived predicates through rule-action graphs and local search
Annals of Mathematics and Artificial Intelligence
Stochastic enforced hill-climbing
Journal of Artificial Intelligence Research
Planning as satisfiability: Heuristics
Artificial Intelligence
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Graphplan-style of planning can be formulated as an incremental propositional CSP where the (boolean) variables correspond to operator instantiations (actions) that are or are not scheduled at certain time steps. In this paper we present a framework for solving this class of propositional CSPs using local search in planning graphs. The search space consists of particular subgraphs of a planning graph corresponding to (complete) variable assignments, and representing partial plans. The operators for moving from one search state to the next one are graph modifications corresponding to revisions of the current variable assignment (partial plan), or to an extension of the represented CSP.Our techniques are implemented in a planner called LPG using various types of heuristics based on a parametrized objective function, where the parameters weight different constraint violations, and are dynamically evaluated using Lagrange multipliers. LPG's basic heuristic was inspired by Walksat, which in Kautz and Selman's Blackbox can be used to solve the SAT-encoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SAT-problems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action execution costs to produce good quality plans. This is achieved by an “anytime” process minimizing an objective function based on the number of constraint violations in a plan and on its overall cost. Experimental results illustrate the efficiency of our approach, showing, in particular, that LPG is significantly faster than Blackbox and other planners based on planning graphs.