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 behaviour and assumptions regarding prior knowledge and communication are problematic.
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.
This workshop is the fourth edition of the MIPC workshop series, previously held at AAAI-16, AAAI-15 and AAAI-14.
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:
At the suggestion of the AAMAS 2017 workshop chairs, we are opening the MIPC workshop to last minute short contributions. This is mainly because the AAMAS organizers recently decided to provide a registration discount if attendees register for the main conference as well at at least one workshop or tutorial. Interested authors are encouraged to send a 2-page extended abstract (in PDF or PS format) via e-mail to mipc2017 AT easychair.org by March 26, 2017. If the extended abstract is deemed relevant for the workshop, we will allocate a presentation slot for the authors in the workshop program.
We offer a talk-only option for authors of relevant papers that have been published in journals or conference proceedings. Interested authors are encouraged to send their paper (in PDF or PS format) and publication details via e-mail to mipc2017 AT easychair.org by March 26, 2017. If the paper is deemed relevant for the workshop, we will allocate a presentation slot for the authors in the workshop program.
| 09:00 - 11:00 | Session 1 (Chair: Tathagata Chakraborti) |
| 09:00 - 09:10 | Opening Remarks |
| 09:10 - 9:55 | Invited Talk — Matthias Schultz |
| 9:55 - 10:20 | Patrick MacAlpine, Peter Stone: Evaluating Ad Hoc Teamwork Performance in Drop-In Player Challenges |
| 10:20 - 10:45 | Thomas Bolander, Thorsten Engesser, Robert Mattmuller, Bernhard Nebel: Better Eager Than Lazy? How Agent Types Impact the Successfulness of Implicit Coordination |
| 10:45-11:00 | David Buckingham, Matthias Scheutz: Getting Help without Asking: Stigmergic Planning for Human-Robot Collaboration |
| 11:00 - 11:30 | Coffee Break |
| 11:30 - 1:00 | Session 2 (Chair: Katie Genter) |
| 11:30 - 12:15 | Discussion about the Future of MIPC |
| 12:15-12:40 | Stephen Cranefield, Felipe Meneguzzi, Nir Oren, Bastin Tony Roy Savarimuthu: A Bayesian Approach to Norm Identification |
| 12:40 - 12:55 | Paula Chocron: Vocabulary Alignment in Open and Heterogenous Interactions: Is it Possible? |
| 12:55 - 1:00 | Closing Remarks |
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Matthias Scheutz Department of Computer Science Tufts University Website |
Building Shared Mental Models on the Fly
Abstract:
Shared mental models are a hallmark of effective human teams. In part
they are built through prior training, but in part they are also
extended and updated during task performance. In this presentation, I
will argue that building and using shared mental models of other agents
in a multi-agent system during task performance might be a good way to
overcome limitations imposed by lack of prior coordination. In
particular for human-robot teams, such dynamic shared mental models
will be essential for ensuring team effectiveness.