The computational complexity of propositional STRIPS planning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
The LPSAT Engine & Its Application to Resource Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
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
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Framework for Hybrid Tractability Results in Boolean Weighted Constraint Satisfaction Problems
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
A heuristic search approach to planning with temporally extended preferences
Artificial Intelligence
Loosely coupled formulations for automated planning: an integer programming perspective
Journal of Artificial Intelligence Research
A logical study of partial entailment
Journal of Artificial Intelligence Research
Using the relaxed plan heuristic to select goals in oversubscription planning problems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Expert Systems with Applications: An International Journal
Towards reasoning with partial goal satisfaction in intelligent agents
ProMAS'10 Proceedings of the 8th international conference on Programming Multi-Agent Systems
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Work in partial satisfaction planning (PSP) has hitherto assumed that goals are independent thus implying that they have additive utility values. In many real-world problems, we cannot make this assumption. In this paper, we motivate the need for handling various types of goal utility dependence in PSP. We provide a framework for representing them using the General Additive Independence model and investigate two different approaches to handle this problem: (1) compiling PSP with utility dependencies to Integer Programming; (2) extending forward heuristic search planning to handle PSP goal dependencies. To guide the forward planning search, we introduce a novel heuristic framework that combines costpropagation and Integer Programming to select beneficial goals to find an informative heuristic estimate. The two implemented planners, iPUD and SPUDS, using the approaches discussed above, are compared empirically on several benchmark domains. While iPUD is more readily amendable to handle goal utility dependencies and can provide bounded optimality guarantees, SPUDS scales much better.