Conflicting Agents - Conflict Management in Multi-Agent by Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller

By Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller

Sociology and MAS structures are where the place tough a tender sciences converge and this can be good illustrated in very good articles within the publication.

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Additional resources for Conflicting Agents - Conflict Management in Multi-Agent Systems (Multiagent Systems, Artificial Societies, and Simulated Organizations Volume 1)

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For our purposes it is sufficient to view action requests as a request to interact with a caregiver or Lois via one of the UI devices. The agents share several resources, namely the UI devices and indirectly the caregivers and Lois. In addition to the resource interaction, action requests potentially intersect on a temporal basis. In this case for there to be a resource coordination problem the requests must occur within a certain temporal proximity to one another or within a system state window (below).

For alerts contact authorized contactees on their devices sequentially in order of priority, stopping when one contactee accepts the alert. 50 Computational predictability – the ratio of responses to computational processing power will be small in general because all responses must even- An Application Science for Multi-Agent Systems 51 tually involve a human caregiver or client. The “distribution to avoid a bottleneck” view doesn’t apply here. When evaluating a distributed coordination approach, other factors to consider include: higher cost of technology development, application specific potential for higher computational cost in agent control problem solving overhead and network bandwidth, generally much harder to debug algorithms, and that in this application we can achieve a global view of the interaction space (the agents do not have privacy concerns as they do in e-commerce applications).

This usually means that the customers within a cluster are closely located to each other. In this way it is 36 possible to cope with discrepancies between predicted demand and the actual consumption. For instance, this happens if the demand of a customer changes while the resources are delivered. The customer would then be faced with either a lack or an excessive amount of resources, thus leaving the system in an undesired state. Based on the above insights, we used the GAIA methodology [15] to design the MAS.

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