We present deterministic sequences for use in sampling-based approaches to motion planning. They simultaneously combine the qualities found in many other sequences: i) the increme...
We present a new planning algorithm that formulates the planning problem as a counting satisfiability problem in which the number of available solutions guides the planner determ...
GraphPlan-like and SATPLAN-like planners have shown to outperform classical planners for most of the classical planning domains. However, these two propositional approaches do not ...
This paper describes an approach to automatically learn planning operators by observing expert solution traces and to further refine the operators through practice in a learning-b...
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...