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
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
On the compilability and expressive power of propositional planning formalisms
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
Partial weighted MaxSAT for optimal planning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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
Preference-Based planning via MaxSAT
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
Automating the evaluation of planning systems
AI Communications
Planning as satisfiability with IPC simple preferences and action costs
AI Communications
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Soft goals extend the classical model of planning with a simple model of preferences. The best plans are then not the ones with least cost but the ones with maximum utility, where the utility of a plan is the sum of the utilities of the soft goals achieved minus the plan cost. Finding plans with high utility appears to involve two linked problems: choosing a subset of soft goals to achieve and finding a low-cost plan to achieve them. New search algorithms and heuristics have been developed for planning with soft goals, and a new track has been introduced in the International Planning Competition (IPC) to test their performance. In this note, we show however that these extensions are not needed: soft goals do not increase the expressive power of the basic model of planning with action costs, as they can easily be compiled away. We apply this compilation to the problems of the net-benefit track of the most recent IPC, and show that optimal and satisficing cost-based planners do better on the compiled problems than optimal and satisficing netbenefit planners on the original problems with explicit soft goals. Furthermore, we show that penalties, or negative preferences expressing conditions to avoid, can also be compiled away using a similar idea.