Planning optimal paths for large numbers of robots is computationally expensive. In this
paper, we introduce a new framework for multirobot path planning called subdimensional …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

We describe and evaluate the algorithmic techniques that are used in the FF planning
system. Like the HSP system, FF relies on forward state space search, using a heuristic that …

Abstract Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents
such that each agent reaches its goal and the agents do not collide. In recent years, variants …

This book provides a comprehensive and much needed introduction to the field by one of its
foremost experts. It is beautifully written and presents a unifying framework capturing a wide …

It has been widely observed that there is no single" dominant" SAT solver; instead, different
solvers perform best on different instances. Rather than following the traditional approach of …

We present a new algorithm for task and motion planning (TMP) and discuss the
requirements and abstractions necessary to obtain robust solutions for TMP in general. Our …

The Boolean satisfiability problem (SAT) is a fundamental NP-complete decision problem in
automated reasoning and mathematical logic. As evidenced by the results of SAT …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

Plan recognition is the problem of inferring the goals and plans of an agent after observing
its behavior. Recently, it has been shown that this problem can be solved efficiently, without …