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Simultaneously modeling humans' preferences and their beliefs about others' preferences
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International Conference on Autonomous Agents archive
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 table of contents
Estoril, Portugal
SESSION: Agent and multi-agent learning table of contents
Pages 323-330  
Year of Publication: 2008
ISBN:978-0-9817381-0-9
Authors
Sevan G. Ficici  Harvard University, Cambridge, Massachusetts
Avi Pfeffer  Harvard University, Cambridge, Massachusetts
Sponsors
ACM: Association for Computing Machinery
AAAI : Association for the Advancement of Artifical Intelligence
Publisher
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Downloads (6 Weeks): 5,   Downloads (12 Months): 54,   Citation Count: 2
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ABSTRACT

In strategic multiagent decision making, it is often the case that a strategic reasoner must hold beliefs about other agents and use these beliefs to inform its decision making. The behavior thus produced by the reasoner involves an interaction between the reasoner's beliefs about other agents and the reasoner's own preferences. A significant challenge faced by model designers, therefore, is how to model such a reasoner's behavior so that the reasoner's preferences and beliefs can each be identified and distinguished from each other. In this paper, we introduce a model of strategic reasoning that allows us to distinguish between the reasoner's utility function and the reasoner's beliefs about another agent's utility function as well as the reasoner's beliefs about how that agent might interact with yet other agents. We show that our model is uniquely identifiable. That is, no two different parameter settings will cause the model to give the same behavior over all possible inputs. We then illustrate the performance of our model in a multiagent negotiation game played by human subjects. We find that our subjects have slightly incorrect beliefs about other agents in the game.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Sevan G. Ficici: colleagues
Avi Pfeffer: colleagues