Agent Compatibility and Coalition Formation: Investigating Two Interacting Negotiation Strategies

Merida-Campos, Carlos; Willmott, Steven

doi:10.1007/978-3-540-72502-2_6

Cited by 3 publications

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References 9 publications

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“…With the same idea but in a different context, Mérida-Campos et al [23,24] focus on the effects of heterogeneous tasks on the coalition adaptation process in a heterogeneous population of agents, namely: competitive, where agents try to be in a coalition with maximal competence; and conservative, where agents try to be in a coalition with optimal competence / size ratio in order to maximize their benefits. They investigate how agents in a heterogeneous population cluster together across multiple coalition formation episodes and varying tasks, and observe that the competitive strategy outper-forms the conservative one.…”

Section: Coalition Formation For Task Allocationmentioning

confidence: 99%

Using reputation and adaptive coalitions to support collaboration in competitive environments

Peleteiro

Burguillo

Lück

et al. 2015

Engineering Applications of Artificial Intelligence

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Internet-based scenarios, like co-working, e-freelancing, or crowdsourcing, usually need supporting collaboration among several actors that compete to service tasks. Moreover, the distribution of service requests, i.e., the arrival rate, varies over time, as well as the service workload required by each customer. In these scenarios, coalitions can be used to help agents to manage tasks they cannot tackle individually. In this paper we present a model to build and adapt coalitions with the goal of improving the quality and the quantity of tasks completed. The key contribution is a decision making mechanism that uses reputation and adaptation to allow agents in a competitive environment to autonomously enact and sustain coalitions, not only its composition, but also its number, i.e., how many coalitions are necessary. We provide empirical evidence showing that when agents employ our mechanism it is possible for them to maintain high levels of customer satisfaction. First, we show that coalitions keep a high percentage of tasks serviced on time despite a high percentage of unreliable workers. Second, coalitions and agents demonstrate that they successfully adapt to a varying distribution of customers' incoming tasks. This occurs because our decision making mechanism facilitates coalitions to disband when they become non-competitive, and individual agents detect opportunities to start new coalitions in scenarios with high task demand.

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Section: Coalition Formation For Task Allocationmentioning

confidence: 99%