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
Multi-Agent Simulation for Crisis Management
KMN '02 Proceedings of the IEEE Workshop on Knowledge Media Networking
Programming agents as a means of capturing self-strategy
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
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
Using aspiration adaptation theory to improve learning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Less is more: restructuring decisions to improve agent search
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient bidding strategies for Cliff-Edge problems
Autonomous Agents and Multi-Agent Systems
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Effectively modeling an agent's cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many elements from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive empirical results from over a hundred agents programmed to perform in optimization problems involving solving for one and two variables.