The computational complexity of propositional STRIPS planning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
On the Use of Integer Programming Models in AI Planning
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 FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Maximizing flexibility: a retraction heuristic for oversubscribed scheduling problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Solving Decentralized Continuous Markov Decision Problems with Structured Reward
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
A heuristic search approach to planning with temporally extended preferences
Artificial Intelligence
Contingent planning with goal preferences
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A heuristic search approach to planning with continuous resources in stochastic domains
Journal of Artificial Intelligence Research
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Planning with goal utility dependencies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Over-subscription planning with numeric goals
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning with continuous resources in stochastic domains
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Optimal symbolic planning with action costs and preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Quantifying privacy in multiagent planning
Multiagent and Grid Systems - Planning in multiagent systems
Planning in stochastic domains for multiple agents with individual continuous resource state-spaces
Autonomous Agents and Multi-Agent Systems
Over-subscription planning with boolean optimization: an assessment of state-of-the-art solutions
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
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
Goal-Driven autonomy with case-based reasoning
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Integrated learning for goal-driven autonomy
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
Simulating UAV Surveillance for Analyzing Impact of Commitments in Multi-Agent Systems
International Journal of Agent Technologies and Systems
Optimal interdiction of attack plans
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a subset of the goals. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modeling and handling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the planning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BENEFIT. We develop three techniques, (i) one based on integer programming, called OptiPlan, (ii) the second based on regression planning with reachability heuristics, called AltAltps, and (iii) the third based on anytime heuristic search for a forward state-space heuristic planner, called Sapaps. Our empirical studies with these planners show that the heuristic planners generate plans that are comparable to the quality of plans generated by OptiPlan, while incurring only a small fraction of the cost.