C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial life meets entertainment: lifelike autonomous agents
Communications of the ACM
Journal of Mathematical Psychology - Special issue on experimental economics
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Predicting people's bidding behavior in negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Resolving crises through automated bilateral negotiations
Artificial Intelligence
Understanding how people design trading agents over time
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Modeling human behavior for virtual training systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Can automated agents proficiently negotiate with humans?
Communications of the ACM - Amir Pnueli: Ahead of His Time
Modeling agents through bounded rationality theories
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
A cultural sensitive agent for human-computer negotiation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Creating agents that properly simulate and interact with people is critical for many applications. Towards creating these agents, models are needed that quickly and accurately predict how people behave in a variety of domains and problems. This paper explores how one bounded rationality theory, Aspiration Adaptation Theory (AAT), can be used to aid in this task. We extensively studied two types of problems -- a relatively simple optimization problem and two complex negotiation problems. We compared the predictive capabilities of traditional learning methods with those where we added key elements of AAT and other optimal and bounded rationality models. Within the extensive empirical studies we conducted, we found that machine learning models combined with AAT were most effective in quickly and accurately predicting people's behavior.