HYBRID ACE: COMBINING SEARCH DIRECTIONS FOR HEURISTIC PLANNING

Hybrid ACE: Combining Search Directions for Heuristic Planning

One of the most promising trends in Domain-Independent AI Planning, nowadays, is state-space heuristic planning. The planners of this category construct general but efficient heuristic functions, which are used as a guide to traverse the state space either in a forward or in a backward direction. Although specific problems may favor one or the other direction, there is no clear evidence why any...

Heuristic Planning for Hybrid Systems

Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-li...

Heuristic Search for Manipulation Planning

Manipulation problems involving many objects present substantial challenges for planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. Existing approaches to integrated task and motion planning as well as manipula...

Heuristic Search in Planning

Combining Heuristic Estimators for Satisficing Planning

The problem of effectively combining multiple heuristic estimators has been studied extensively in the context of optimal planning, but not in the context of satisficing planning. To narrow this gap, we empirically examine several ways of exploiting the information of multiple heuristics in a satisficing best-first search algorithm, comparing their performance in terms of coverage, plan quality...