Abstract
In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.
Highlights
In multi-agent systems, agents often need to cooperate to fulfil their individual or common goals.For example, an agent may not be able to perform a task on its own or may lack a resource that is required for a task, which can be provided by another agent
We use an example from robotics
We propose a novel approach to the problem, which draws on methods and tools for contextual reasoning and Multi-Context Systems
Summary
In multi-agent systems, agents often need to cooperate to fulfil their individual or common goals. We extend our approach with features of possibilistic reasoning: We extend the definition of dependence relations with a certainty degree; we use the model and algorithms of possibilistic MCS to compute the potential coalitions under uncertainty In this case, we evaluate the different coalitions based on the certainty degree with which each coalition achieves the different goals, using multiple-criteria decision-making methods. This article is an extended version of [28], where we presented our methodology for formalizing multi-agent systems and computing coalitions among agents under the perfect world assumption, i.e., actions are always carried out with success by the agents that they have been assigned to.
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