Cooperative multi-agent planning (MAP) is a relatively recent research field that combines
technologies, algorithms, and techniques developed by the Artificial Intelligence Planning …

Loosely coupled multi-agent systems are perceived as easier to plan for because they
require less coordination between agent sub-plans. In this paper we set out to formalize this …

We introduce a width parameter that bounds the complexity of classical planning problems
and domains, along with a simple but effective blind-search procedure that runs in time that …

This paper deals with the problem of classical planning for multiple cooperative agents who
have private information about their local state and capabilities they do not want to reveal …

We present a fully distributed multi-agent planning algorithm. Our methodology uses
distributed constraint satisfaction to coordinate between agents, and local planning to …

Abstract pddl4j (Planning Domain Description Library for Java) is an open source toolkit for
Java cross-platform developers meant (1) to provide state-of-the-art planners based on the …

We present a new algorithm for probabilistic planning with no observability. Our algorithm,
called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to …

We introduce a simple variation of the additive heuristic used in the HSP planner that
combines the benefits of the original additive heuristic, namely its mathematical formulation …

If the complexity of planning for a single agent is described by some function f of the input,
how much more difficult is it to plan for a team of n cooperating agents? If these agents are …

State space search is a basic method for analyzing reachability in discrete transition
systems. To tackle large compactly described transition systems–the state space explosion …