The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …

We investigate the use of machine learning to create effective heuristics for search
algorithms such as IDA⁎ or heuristic-search planners such as FF. Our method aims to …

We propose a novel relational state representation and an action-value function learning
algorithm that learns from planning experience for geometric task-and-motion planning …

We present a framework for learning to guide geometric task-and-motion planning (g-tamp).
g-tamp is a subclass of task-and-motion planning in which the goal is to move multiple …

Both global methods and on-line trajectory optimization methods are powerful techniques for
solving optimal control problems; however, each has limitations. In order to mitigate the …

Suboptimal search algorithms offer shorter solving times by sacrificing guaranteed solution
optimality. While optimal searchalgorithms like A* and IDA* require admissible heuristics …

Abstract search algorithms such as IDA* or heuristic-search planners. Our method aims to
generate a strong heuristic from a given weak heuristic h 0 through bootstrapping. The" …

In complexity theory, there is a widely studied grouping of optimization problems that
belongs to the non-deterministic polynomial-time hard set. One of them is the set covering …

Several known heuristic functions can capture the input at different levels of precision, and
support relaxation-refinement operations guaranteeing to converge to exact information in a …

Automated planning deals with the problem of finding a sequence of actions leading from a
given state to a desired state. The state-of-the-art automated planning techniques exploit …