Dynamic coalition formation among rational agents

Klusch, Matthias; Gerber, Andreas

doi:10.1109/mis.2002.1005630

Cited by 143 publications

(96 citation statements)

References 10 publications

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“…[19,24]), the main purpose of forming a coalition is to perform a task that cannot be performed by a single agent. That work can be combined with our approach by addressing the problem of the allocation of agents to roles, after the change of the organizational model by our approach.…”

Section: Discussionmentioning

confidence: 99%

A generic architecture for redesign of organizations triggered by changing environmental circumstances

Hoogendoorn

Jonker

Treur

2011

Comput Math Organ Theory

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Artificial Intelligence has contributed (formal) design models and software support tools to application areas such as architecture, engineering and software design. This paper explores the effectiveness of applying design models to the area of organization (re)design. To that purpose a component-based model for (re)design of organizations is presented as a specialization of an existing generic design model. Using recently developed formalizations within Organization Theory organization models are described as design object descriptions, and organization goals as design requirements. A formal design process description is presented that models the redesign process for an organization that adapts to changes in the environment. The formally specified and implemented approach to organization redesign thus obtained has been tested for a well-known historical case study from the Organization Theory literature.

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Section: Discussionmentioning

confidence: 99%

A generic architecture for redesign of organizations triggered by changing environmental circumstances

Hoogendoorn

Jonker

Treur

2011

Comput Math Organ Theory

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“…[18,30]; and solutions from the field of robotics based on schema theory, e.g. [35,36] or synergy [22].…”

Section: Selecting the Best Coalitionmentioning

confidence: 99%

“…Which group of agents to join? The problem of assembling a group of cooperating agents in order for all agents to reach their goals, shared or not, is referred to as coalition formation, and has been on the focus of many recent works in the area of multiagent systems (e.g., [31,32,30,18,3,17,7]). This paper introduces a novel contextual reasoning approach to address the problem based on the use of Multi-Context Systems (MCS).…”

Section: Introductionmentioning

confidence: 99%

Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

Bikakis¹,

Caire

2015

Multi-Agent Systems

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Abstract. In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Some of the questions then raised, such as, which agent to cooperate with, are addressed in the field of coalition formation. In this paper we go further and first, address the question of how to compute the solution space for the formation of coalitions using a contextual reasoning approach. We model agents as contexts in Multi-Context Systems (MCS) and dependence relations among agents as bridge rules. We then systematically compute all potential coalitions using algorithms for MCS equilibria. Finally, given a set of functional and non-functional requirements, we propose ways to select the best solutions. We illustrate our approach with an example from robotics.

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“…Such dynamic tasks [37,32,51,54] are an important aspect of coalition and team formation. The ability of our RL framework to allow agents to learn about the types of other agents facilitates the transfer of knowledge between tasks.…”

Section: Dynamic Tasksmentioning

confidence: 99%

Sequentially optimal repeated coalition formation under uncertainty

Chalkiadakis

Boutilier

2010

Auton Agent Multi-Agent Syst

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Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming "new" coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.

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Dynamic coalition formation among rational agents

Cited by 143 publications

References 10 publications

A generic architecture for redesign of organizations triggered by changing environmental circumstances

A generic architecture for redesign of organizations triggered by changing environmental circumstances

Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

Sequentially optimal repeated coalition formation under uncertainty

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