Using aspiration adaptation theory to improve learning

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.

[1]  Ya'akov Gal,et al.  Predicting people's bidding behavior in negotiation , 2006, AAMAS '06.

[2]  Sarit Kraus,et al.  Modeling Agents through Bounded Rationality Theories , 2009, IJCAI.

[3]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[4]  Sarit Kraus,et al.  Negotiating with bounded rational agents in environments with incomplete information using an automated agent , 2008, Artif. Intell..

[5]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[6]  Milind Tambe,et al.  The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models , 2011, J. Artif. Intell. Res..

[7]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[8]  Peter Stone,et al.  Bidding for customer orders in TAC SCM , 2004, AAMAS'04.

[9]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[10]  H. Simon,et al.  Models of Man. , 1957 .

[11]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[12]  M. Weitzman Optimal search for the best alternative , 1978 .

[13]  Toru Ishida,et al.  Modeling Human Behavior for Virtual Training Systems , 2005, AAAI.

[14]  Pattie Maes,et al.  Artificial life meets entertainment: lifelike autonomous agents , 1995, CACM.

[15]  Sarit Kraus,et al.  Resolving crises through automated bilateral negotiations , 2008, Artif. Intell..

[16]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[17]  Sarit Kraus,et al.  Understanding how people design trading agents over time , 2008, AAMAS.

[18]  R. Selten,et al.  Aspiration Adaptation Theory. , 1998, Journal of mathematical psychology.

[19]  Sarit Kraus,et al.  Can automated agents proficiently negotiate with humans? , 2010, CACM.

[20]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[21]  Daniel P. Loucks,et al.  Computer-Assisted Negotiations of Water Resources Conflicts , 1998 .

[22]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[23]  Ian Witten,et al.  Data Mining , 2000 .

[24]  P. Kline Models of man , 1986, Nature.

[25]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[26]  J. Wilkenfeld,et al.  Mediating International Crises , 2003 .