<html><head><meta http-equiv="Content-Type" content="text/html charset=windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div style="margin: 0px; font-family: Monaco;"><div style="margin: 0px;">Call for Papers:</div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">*****************************************************************************</div><div style="margin: 0px;">* Workshop on Multiagent Interaction without Prior Coordination (MIPC 2015) *</div><div style="margin: 0px;">* At the 29th Conference on Artificial Intelligence (AAAI-15) *</div><div style="margin: 0px;">* Website: <a href="http://conferences.inf.ed.ac.uk/mipc2015">http://conferences.inf.ed.ac.uk/mipc2015</a> *</div><div style="margin: 0px;">*****************************************************************************</div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">This workshop focuses on models and algorithms for multiagent interaction without prior 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, etc. The majority of these well studied methods depends 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 behavior and assumptions regarding prior knowledge and communication are problematic. </div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Effective MIPC is most likely to be achieved as we bring together work from many different areas, including work on intelligent agents, machine learning, game theory, and operations research. For instance, game theorists have considered what happens to equilibria when common knowledge assumptions must be violated, agent designers are faced with mixed teams of humans and agents in open environments and developing variations on planning methods in response to this, etc. 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. </div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px; color: rgb(85, 26, 139);"><span style="color: #000000">For more information visit: <a href="http://conferences.inf.ed.ac.uk/mipc2015/">http://conferences.inf.ed.ac.uk/mipc2015/</a></span></div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Topics </div><div style="margin: 0px;">------</div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">The workshop will discuss research related to multiagent interaction without prior coordination, as outlined in the workshop description above. A non-exclusive list of relevant topics includes: </div><div style="margin: 0px;">- Learning and adaptation in multiagent systems without prior coordination </div><div style="margin: 0px;">- Agent coordination and cooperation without prior coordination </div><div style="margin: 0px;">- Team formation and information sharing in ad hoc settings </div><div style="margin: 0px;">- Teammate/opponent modelling and plan recognition </div><div style="margin: 0px;">- Human-machine interaction without prior coordination </div><div style="margin: 0px;">- Game theory/incomplete information applied to ad hoc agent coordination </div><p style="margin: 0px;"> </p><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Important Dates </div><div style="margin: 0px;">--------------- </div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">- Submission deadline: November 9, 2014 </div><div style="margin: 0px;">- Notification of acceptance: November 23, 2014 </div><div style="margin: 0px;">- Camera-ready copies: November 25, 2015 </div><div style="margin: 0px;">- Workshop: January 26, 2015 </div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Organization</div><div style="margin: 0px;">------------</div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Program chairs: </div><div style="margin: 0px;">- Stefano Albrecht (University of Edinburgh) </div><div style="margin: 0px;">- Jacob Crandall (Masdar Institute of Science and Technology) </div><div style="margin: 0px;">- Somchaya Liemhetcharat (A*STAR Singapore) </div><div style="margin: 0px; min-height: 16px;"><br></div><div style="margin: 0px;">Advisory committee: </div><div style="margin: 0px;">- Subramanian Ramamoorthy (University of Edinburgh) </div><div style="margin: 0px;">- Peter Stone (University of Texas at Austin) </div><div style="margin: 0px;">- Manuela Veloso (Carnegie Mellon University) </div><div><br></div></div></body></html>