Coordination Without Negotiation in Teams of Heterogeneous Robots

Isik, Michael; Stulp, Freek; Mayer, Gerd; Utz, Hans

doi:10.1007/978-3-540-74024-7_33

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…One of the characteristics of the human organizations framework used in this work is communication or negotiation. However, coordination without negotiation [22] is more appropriate if an ad hoc approach is required. Tracking multiple soccer players' trajectories can for example be done using the camera feed, in combination with an analyzing system that segments motions into classified ball actions [2].…”

Section: Related Researchmentioning

confidence: 99%

A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Coordination is an important aspect of multi-agent teamwork. In the context of robot soccer in the RoboCup Standard Platform League, our focus is on the coach as an external observer of the team, aiming to provide his teammates with effective tactical advice during matches. The coach problem can be approached from different angles: in order to adapt the behaviour of his teammates, he should at first be able to perform plan recognition on their observable actions. Furthermore, in providing them with appropriate advice, he should still adhere to the norms and regulations of the match to prevent penalties for his team. Also, when teammates' profiles and attributes are unknown or the system is only partially observable, coordination should be more 'ad hoc' to ensure robustness of the Multi-Agent System (MAS). In this work, we present a formalization of the problem of designing a coach in robot soccer, employing a temporal deontic logical framework. The framework is based on agent organizations [14], in which social coordination and norms play an important part.

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Section: Related Researchmentioning

confidence: 99%

A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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“…Robots can estimate the state of a teammate in order to decide which robot should approach the ball in the robot soccer domain, and while the robots agree on the policy > 90% of the time, communication is necessary for situations where errors in state estimation may cause policy fluctuations in the team [18].…”

Section: Ad Hoc Teamsmentioning

confidence: 99%

Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents

Liemhetcharat

Veloso

2014

Artificial Intelligence

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Previous approaches to select agents to form a team rely on single-agent capabilities, and team performance is treated as a sum of such known capabilities. Motivated by complex team formation situations, we address the problem where both single-agent capabilities may not be known upfront, e.g., as in ad hoc teams, and where team performance goes beyond single-agent capabilities and depends on the specific synergy among agents. We formally introduce a novel weighted synergy graph model to capture new interactions among agents. Agents are represented as vertices in the graph, and their capabilities are represented as Normally-distributed variables. The edges of the weighted graph represent how well the agents work together, i.e., their synergy in a team. We contribute a learning algorithm that learns the weighted synergy graph using observations of performance of teams of only two and three agents. Further, we contribute two team formation algorithms, one that finds the optimal team in exponential time, and one that approximates the optimal team in polynomial time. We extensively evaluate our learning algorithm, and demonstrate the expressiveness of the weighted synergy graph in a variety of problems. We show our approach in a rich ad hoc team formation problem capturing a rescue domain, namely the RoboCup Rescue domain, where simulated robots rescue civilians and put out fires in a simulated urban disaster. We show that the weighted synergy graph outperforms a competing algorithm, thus illustrating the efficacy of our model and algorithms.

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“…To set up team cooperation a suitable communication infrastructure had to be established first. The Ulm Sparrows relied on an IP multicast-based group communication scheme over which the SharedBelief (Utz et al, 2004;Isik et al, 2007) data structure was exchanged. In order to talk to the other team, Carpe Noctem thus needed to provide a corresponding implementation along with suitable data conversion techniques.…”

Section: Problem Descriptionmentioning

confidence: 99%