REGRET: reputation in gregarious societies
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Trust and deception in virtual societies
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Proceedings of the workshop on Deception, Fraud, and Trust in Agent Societies held during the Autonomous Agents Conference: Trust in Cyber-societies, Integrating the Human and Artificial Perspectives
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MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
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ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
An integrated trust and reputation model for open multi-agent systems
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AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
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AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
Towards incentive-compatible reputation management
AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
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In this paper, starting from a socio-cognitive model of trust,we analyze how it is possible to predict how/when an agent whotrusts something/ someone will therefore trust something/someoneelse, before and without a direct experience. On the contrary ofmodels of trust just based on (or reduced to) a probability indexor a simple measure of experience and frequency, we are interestedto analyze the trust concept so that we are able to cope withproblems like: a) given X’s evaluation about Y’strustworthiness on a specific task τ, what can we say onX’s evaluation about Y’s trustworthiness on a differentbut analogous task τ’? What would we intend for ananalogous task? b) Given X’s evaluation about Y’strustworthiness on a specific task τ, what can we say onX’s evaluation about the trustworthiness of a different agentZ on the same task τ? In fact, in our view only a cognitivemodel of trust, with its analytical power, seems able to accountfor the inferential generalization of trustworthiness from task totask and from agent to agent not just based on specific experienceand/or learning.