Foundations of logic programming
Foundations of logic programming
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Introduction to algorithms
Logic programming
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fast planning through planning graph analysis
Artificial Intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Model checking
Logic and Databases: A Deductive Approach
ACM Computing Surveys (CSUR)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Sokoban: enhancing general single-agent search methods using domain knowledge
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Decomposable negation normal form
Journal of the ACM (JACM)
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Answer set programming and plan generation
Artificial Intelligence
Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Encoding Planning Problems in Nonmonotonic Logic Programs
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
ASSAT: computing answer sets of a logic program by SAT solvers
Eighteenth national conference on Artificial intelligence
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
Constraint Processing
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Planning as satisfiability with preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On the compilability and expressive power of propositional planning formalisms
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Decision-theoretic planning with non-Markovian rewards
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
A heuristic search approach to planning with temporally extended preferences
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
DPLL with a trace: from SAT to knowledge compilation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Structural relaxations by variable renaming and their compilation for solving MinCostSAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Friends or foes? on planning as satisfiability and abstract CNF encodings
Journal of Artificial Intelligence Research
Soft goals can be compiled away
Journal of Artificial Intelligence Research
Everything you always wanted to know about planning (but were afraid to ask)
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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The automatic derivation of heuristic functions for guiding the search for plans is a fundamental technique in planning. The type of heuristics that have been considered so far, however, deal only with simple planning models where costs are associated with actions but not with states. In this work we address this limitation by formulating a more expressive planning model and a corresponding heuristic where preferences in the form of penalties and rewards are associated with fluents as well. The heuristic, that is a generalization of the well-known delete-relaxation heuristic, is admissible, informative, but intractable. Exploiting a correspondence between heuristics and preferred models, and a property of formulas compiled in d-DNNF, we show however that if a suitable relaxation of the domain, expressed as the strong completion of a logic program with no time indices or horizon is compiled into d-DNNF, the heuristic can be computed for any search state in time that is linear in the size of the compiled representation. This representation defines an evaluation network or circuit that maps states into heuristic values in linear-time. While this circuit may have exponential size in the worst case, as for OBDDs, this is not necessarily so. We report empirical results, discuss the application of the framework in settings where there are no goals but just preferences, and illustrate the versatility of the account by developing a new heuristic that overcomes limitations of delete-based relaxations through the use of valid but implicit plan constraints. In particular, for the Traveling Salesman Problem, the new heuristic captures the exact cost while the delete-relaxation heuristic, which is also exponential in the worst case, captures only the Minimum Spanning Tree lower bound.