Connectionist learning of belief networks
Artificial Intelligence
The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Computers as Partners: A Technology Forecast for Decision-Making in the 21st Century
COMPSAC '99 23rd International Computer Software and Applications Conference
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Influence of Social Dependencies on Decision-Making: Initial Investigations with a New Game
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning social preferences in games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Bidding for customer orders in TAC SCM
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Prediction of Partners' Behaviors in Agent Negotiation under Open and Dynamic Environments
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Modeling how humans reason about others with partial information
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Simultaneously modeling humans' preferences and their beliefs about others' preferences
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
A heuristic personality-based bilateral multi-issue bargaining model in electronic commerce
International Journal of Human-Computer Studies
Optimal Multi-issue Negotiation in Open and Dynamic Environments
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Adaptive conceding strategies for automated trading agents in dynamic, open markets
Decision Support Systems
Modeling reciprocal behavior in human bilateral negotiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Incorporating BDI Agents into Human-Agent Decision Making Research
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
The influence of task contexts on the decision-making of humans and computers
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Predicting partners' behaviors in negotiation by using regression analysis
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Using aspiration adaptation theory to improve learning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Expectation of trading agent behaviour in negotiation of electronic marketplace
Web Intelligence and Agent Systems
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This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists multiple 2-player negotiation scenarios where bids of multi-valued goods can be accepted or rejected. The bidding task is formalized as a selection process in which a proposer player chooses a single bid to offer to a responder player from a set of candidate proposals. Each candidate is associated with features that affect whether not it is the chosen bid. These features represent social factors that affect people's play. We present and compare several algorithms for predicting the chosen bid and for learning a model from data. Data collection and evaluation of these algorithms is performed on both human and synthetic data sets. Results on both data sets show that an algorithm that reasons about dependencies between the features of candidate proposals is significantly more successful than an algorithm which assumes that candidates are independent. In the synthetic data set, this algorithm achieved near optimal performance. We also study the problem of inferring the features of a proposal given the fact that it was the chosen bid. A baseline importance sampling algorithm is first presented, and then compared with several approximations that attain much better performance.