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Adapting reinforcement learning for trust: Effective modeling in dynamic environments: Short Paper

Kafalı, Özgur and Yolum, Pinar (2009) Adapting reinforcement learning for trust: Effective modeling in dynamic environments: Short Paper. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, pp. 383-386. ISBN 978-0-7695-3801-3. (doi:10.1109/WI-IAT.2009.67) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/WI-IAT.2009.67

Abstract

In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about whom to interact with can be done soundly. Traditional trust models are based on modeling specific properties of agents, such as their expertise or reliability. Building those models requires too many prior interactions to be accurate. This paper proposes an approach that is based on keeping track of outcomes of agent's actions towards others rather than modeling other agents' performances explicitly. Contrary to existing modeling approaches that require domain knowledge to build models, our proposed approach can be effectively realized in multiagent systems when the agent's actions are clearly identified. Comparisons with other modeling approaches in various environments reveal that our proposed approach can create more precise models in short time and can adjust its behavior quickly when other agents' behaviors change.

Item Type: Book section
DOI/Identification number: 10.1109/WI-IAT.2009.67
Uncontrolled keywords: trust; reinforcement learning; modeling; intelligent agent; subspace constraints; conferences; acoustical engineering; context modeling; quality of service; appraisal; web services
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ozgur Kafali
Date Deposited: 04 Feb 2018 20:28 UTC
Last Modified: 18 Sep 2019 08:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65895 (The current URI for this page, for reference purposes)
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