AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A Value-Driven System for Autonomous Information Gathering
Journal of Intelligent Information Systems
Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Acquiring an Optimal Amount of Information for Choosing from Alternatives
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Reputation Management Framework and Its Use as Currency in Large-Scale Peer-to-Peer Networks
P2P '04 Proceedings of the Fourth International Conference on Peer-to-Peer Computing
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Learning trust strategies in reputation exchange networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Dynamically learning sources of trust information: experience vs. reputation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Reputation in the joint venture game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A statistical decision-making model for choosing among multiple alternatives
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An asymptotically optimal algorithm for the max k-armed bandit problem
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The max K-armed bandit: a new model of exploration applied to search heuristic selection
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Choosing between heuristics and strategies: an enhanced model for decision-making
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Combining Cognitive with Computational Trust Reasoning
Trust in Agent Societies
Dynamic information source selection for intrusion detection systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
On the modeling of honest players in reputation systems
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
On the Choice of Obtaining and Disclosing the Commonvalue in Auctions
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Web Intelligence and Agent Systems
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Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about alternative possibilities through gossip (i.e., by consulting others) or using a reputation system (a centralized repository of information about past behavior). This paper defines a statistical model, the "Information-Acquisition Source Utility model" (IASU) by which agents operating in an uncertain world can determine the amount of information to collect about potential partners before choosing one and which information sources they should consult (gossip, reputation system, or additional personal interaction with the agent). The IASU model explicitly represents the cost of information, which may vary by information source. To maximize the expected gain from a choice, it estimates the utility of choosing a partner by iteratively estimating the benefit of additional information. The paper reports empirical studies that compare the effectiveness of the IASU model with a baseline in which only prior experience with a potential partner is used as the basis of the decision and with a model that determines in advance both the amount of information and its allocation among the different sources. Two different application domains are used in these empirical studies, the Surrogate Venture Game model, which concerns choosing an optimal partner for a business venture, and a restaurant domain. The results of the experiments show that the use of the model significantly increases the agents' overall utility.