Using aspiration adaptation theory to improve learning

Avi Rosenfeld, Sarit Kraus

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations


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. Categories and Subject Descriptors 1.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence General Terms Experimentation.

Original languageEnglish
Number of pages8
StatePublished - 2011
Event10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China
Duration: 2 May 20116 May 2011


Conference10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
Country/TerritoryTaiwan, Province of China


  • Agent learning
  • Bounded rationality
  • Cognitive models


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