In this work, we explore how local interactions can simplify the process of decision-making in multiagent systems, particularly in multirobot problems. We review a recent decision-...
Most work on AI planning has focused on the development of fully automated methods for generating plans that satisfy user-speci ed goals. However, users in many domains want the a...
In the RETSINA multi-agent system, each agent is provided with an internal planning component—the RETSINA planner. Each agent, using its internal planner, formulates detailed pla...
Massimo Paolucci, Onn Shehory, Katia P. Sycara, Di...
The purpose of this paper is to introduce a dialectical theory for plan synthesis based on a multi-agent approach. This approach is a promising way to devise systems based on agent...
This paper presents snlp+ebl, the first implementation of explanation based learning techniques for a partial order planner. We describe the basic learning framework of snlp+ebl, ...