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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

5. Individual Rationality in Competitive Multiagent Systems

verfasst von : Jianye Hao, Ho-fung Leung

Erschienen in: Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality

Verlag: Springer Berlin Heidelberg

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Abstract

In competitive MASs, each individual agent is usually interested in maximizing its personal benefits only, which may have conflicts with the utility of others and the overall system as well. Thus, one natural research direction in competitive MASs is to consider how an agent can learn to obtain as much utility as possible against different opponents based on its local information. Another important question is raised from the system designer’s perspective, i.e., how can the selfish agents be incentivized to coordinate their behaviors to maximize the system-level performance (i.e., maximizing social optimality)? In this chapter, we focus on the first research direction by considering an important competitive multiagent interaction scenario: bilateral negotiation [1]. The second research direction will be the focus of Chap. 6

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Vorheriges Kapitel Social Optimality in Cooperative Multiagent Systems
Nächstes Kapitel Social Optimality in Competitive Multiagent Systems
Fußnoten
1
This negotiation strategy won the champion of ANAC 2012.
 
2
GENIUS is short for General Environment for Negotiation with Intelligent multipurpose Usage Simulation.
 
3
Here \(\mathcal{V}_{i}\) can be either discrete values or continuous real values.
 
4
Here we only analyze the negotiation performance among these top eight negotiation strategies in the final round, but it is worth noticing that our CUHKAgent also ranks the 1st place in the qualifying round against a larger number of negotiation opponents (17 different teams).
 
5
The bold letters are the abbreviations for each strategy as follows: C, CUHKAgent; L, AgentLG; O, OMACAgent; R, TheNegotiatorReloaded; B, BRAMAgent2; M, Meta-agent; I, IAMHaggler2012; and A, AgentMR. These abbreviations will be used in the following descriptions.
 
6
This concept is similar to the concept of pure strategy Nash equilibrium in classical game theory, but it is called empirical pure strategy Nash equilibrium since the analysis is based on empirical results.
 
7
The reason that we choose the top four strategies instead of the top three is that both OMACagent and TheNegotiatorReloaded rank the third place.
 
8
This strategy wins the most social agent award in ANAC 2012 since it achieves the highest social payoff (the sum of its own and its opponent’s payoffs).
 
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2.
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Metadaten
Titel
Individual Rationality in Competitive Multiagent Systems
verfasst von
Jianye Hao
Ho-fung Leung
Copyright-Jahr
2016
Verlag
Springer Berlin Heidelberg
Buch
Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality

Print ISBN: 978-3-662-49468-4

Electronic ISBN: 978-3-662-49470-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-662-49470-7_5