Agent-organized networks for dynamic team formation

Gaston, Matthew E.; desJardins, Marie

doi:10.1145/1082473.1082508

Cited by 123 publications

(93 citation statements)

References 35 publications

(30 reference statements)

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“…We also have the additional requirement of optimizing the coordination cost of formed teams. Agent-organized team formation: Gaston et al [11] study the team-formation problem in a setting where networked agents form teams in a decentralized manner. Agents are able to locally rewire their social network.…”

Section: Related Workmentioning

confidence: 99%

Online team formation in social networks

Anagnostopoulos

Becchetti

Castillo

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

273

206

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We study the problem of online team formation. We consider a setting in which people possess different skills and compatibility among potential team members is modeled by a social network. A sequence of tasks arrives in an online fashion, and each task requires a specific set of skills. The goal is to form a new team upon arrival of each task, so that (i) each team possesses all skills required by the task, (ii) each team has small communication overhead, and (iii) the workload of performing the tasks is balanced among people in the fairest possible way.We propose efficient algorithms that address all these requirements: our algorithms form teams that always satisfy the required skills, provide approximation guarantees with respect to team communication overhead, and they are online-competitive with respect to load balancing. Experiments performed on collaboration networks among film actors and scientists, confirm that our algorithms are successful at balancing these conflicting requirements. This is the first paper that simultaneously addresses all these aspects. Previous work has either focused on minimizing coordination for a single task or balancing the workload neglecting coordination costs.

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Section: Related Workmentioning

confidence: 99%

Online team formation in social networks

Anagnostopoulos

Becchetti

Castillo

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

273

206

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“…Graphs can be used to represent relationships among agents, where a subgraph of connected agents are selected to complete a task [14]. Similarly, by using social networks of agents, where agents have different skills, and edge weights represent communication costs, the optimal team to complete the task has to cover all the required skills [22,23], or trade off between skills and connectivity [11].…”

Section: Team Formationmentioning

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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“…In the traditional model, certain skills are necessary to accomplish a task, and we must select a team that has all the necessary skills with the minimum cost [11,12]. More recent work go beyond a simple sum of skills and also models the synergy of a group [13], how to lead a group to the optimal joint action with a new ad-hoc agent [14] or how to automatically configure a network of agents [15]. In [16], a team formation procedure is presented for a class of online football prediction games, and the system is able to play successfully against a large number of human players.…”

Section: Related Workmentioning

confidence: 99%

A Detailed Analysis of a Multi-agent Diverse Team

Marcolino

Zhang

Jiang

et al. 2014

Coordination, Organizations, Institutions, and Norms in Agent Systems IX

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Abstract.In an open system we can have many different kinds of agents. However, it is a challenge to decide which agents to pick when forming multi-agent teams. In some scenarios, agents coordinate by voting continuously. When forming such teams, should we focus on the diversity of the team or on the strength of each member? Can a team of diverse (and weak) agents outperform a uniform team of strong agents? We propose a new model to address these questions. Our key contributions include: (i) we show that a diverse team can overcome a uniform team and we give the necessary conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that both diversity and strength contribute to the performance of a team; (iv) we show experiments that demonstrate the usefulness of our model in one of the most difficult challenges for Artificial Intelligence: Computer Go 1 .

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Agent-organized networks for dynamic team formation

Cited by 123 publications

References 35 publications

Online team formation in social networks

Online team formation in social networks

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

A Detailed Analysis of a Multi-agent Diverse Team

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