Dan Erusalimchik

AAMAS Conference 2013 Conference Paper

On the Use of Teamwork Software for Multi-Robot Formation Control

• Gal A. Kaminka
• Meytal Traub
• Yehuda Elmaliach
• Dan Erusalimchik
• Alex Fridman

ICRA Conference 2010 Conference Paper

Adaptive multi-robot coordination: A game-theoretic perspective

• Gal A. Kaminka
• Dan Erusalimchik
• Sarit Kraus

Multi-robot systems researchers have been investigating adaptive coordination methods for improving spatial coordination in teams. Such methods adapt the coordination method to the dynamic changes in density of the robots. Unfortunately, while their empirical success is evident, none of these methods has been understood in the context of existing formal work on multi-robot learning. This paper presents a reinforcement-learning approach to coordination algorithm selection, which is not only shown to work well in experiments, but is also analytically grounded. We present a reward function (Effectiveness Index, EI), that reduces time and resources spent coordinating, and maximizes the time between conflicts that require coordination. It does this by measuring the resource-spending velocity. We empirically show its success in simulations of multi-robot foraging. In addition, we analytically explore the reasons that EI works well. We show that under some assumptions, spatial coordination opportunities can be modeled as matrix games in which the payoffs are directly a function of EI estimates. The use of reinforcement learning leads to robots maximizing their EI rewards in equilibrium. This work is a step towards bridging the gap between the theoretical study of interactions, and their use in multi-robot coordination.

AAMAS Conference 2007 Conference Paper

Towards Collaborative Task and Team Maintenance

• Gal A. Kaminka
• Ari Yakir
• Dan Erusalimchik
• Nirom Cohen-Nov

There is significant interest in modeling teamwork in agents. In recent years, it has become widely accepted that it is possible to separate teamwork from taskwork, providing support for domainindependent teamwork at an architectural level, using teamwork models. However, existing teamwork models (both in theory and practice) focus almost exclusively on achievement goals, and ignore maintenance goals, where the value of a proposition is to be maintained over time. Such maintenance goals exist both in taskwork (i. e. , agents take actions to maintain a condition while a task is executing), as well as in teamwork (i. e. , agents take actions to maintain the team). This paper presents mechanisms for collaborative maintenance in both taskwork and teamwork, allowing for fiexible selection of the maintenance protocol. The mechanism is integrated and evaluated in two teamwork architectures for situated agent teams: DIESEL, an implemented teamwork and taskwork architecture, built on top of Soar, and BITE, an architecture for physical behavior-based robots. We provide details of these implementations, and the results from experiments demonstrating the benefits of support for collaborative maintenance processes, in several dynamic rich domains. We show that the use of collaborative maintenance leads to significant improvement in task performance in all domains.