Multiagent Interaction without Prior Coordination
Papers from the 2014 AAAI Workshop
Stefano Albrecht, Samuel Barrett, Jacob Crandall, Somchaya Liemhetcharat, Workshop Organizers
Technical Report WS-14-09
68 pp.
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ISBN 978-1-57735-670-7
This workshop will focus on models and algorithms for multiagent interaction without prior (preset) coordination (MIPC). Interaction between agents is the defining attribute of multiagent systems, encompassing problems of planning in a decentralized setting, learning other agent models, composing teams with high task performance, and selected resource-bounded communication and coordination. There is significant variety in methodologies used to solve such problems, including symbolic reasoning about negotiation and argumentation, distributed optimization methods, machine learning methods such as multiagent reinforcement learning, and others. The majority of these well studied methods depend on some form of prior coordination. Often, the coordination is at the level of problem definition, for example, learning algorithms may assume that all agents share a common learning method or prior beliefs, distributed optimization methods may assume specific structural constraints regarding the partition of state space or cost/rewards, and symbolic methods often make strong assumptions regarding norms and protocols. In realistic problems, these assumptions are easily violated — calling for new models and algorithms that specifically address the case of ad hoc interactions. Similar issues are also becoming increasingly more pertinent in human-machine interactions, where there is a need for intelligent adaptive behaviour and assumptions regarding prior knowledge and communication are problematic. The community of researchers addressing such issues is diverse, drawing on many different speciality areas and corresponding methods. The goal of this workshop is to bring together these diverse viewpoints in an attempt to consolidate the common ground and identify new lines of attack.