Programming agents as a means of capturing self-strategy

In this paper we report results of an extensive evaluation of people's ability to reproduce the strategies they use in simple real-life settings. Having the ability to reliably capture people's strategies in different environments is highly desirable in Multi-Agent Systems (MAS). However, as trivial and daily as these strategies are, the process is not straightforward and people often have a different belief of how they act. We describe our experiments in this area, based on the participation of a pool of subjects in four different games with variable complexity and characteristics. The main measure used for determining the closeness between the two types of strategies used is the level of similarity between the actions taken by the participants and those taken by agents they programmed in identical world states. Our results indicate that generally people have the ability to reproduce their game strategies for the class of games we consider. However, this process should be handled carefully as some individuals tend to exhibit a behavior different from the one they program into their agents. The paper evaluates one possible method for enhancing the process of strategy reproduction.

[1]  Nalini Venkatasubramanian,et al.  Synthetic humans in emergency response drills , 2006, AAMAS '06.

[2]  Sarit Kraus,et al.  The influence of social dependencies on decision-making: initial investigations with a new game , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[3]  Clare Harries,et al.  Measuring Doctors' Self-insight into their Treatment Decisions , 2000 .

[4]  Nalini Venkatasubramanian,et al.  Multi-Agent Simulation of Disaster Response , 2006 .

[5]  Yohei Murakami,et al.  Multi-agent simulation for crisis management , 2002, Proceedings. IEEE Workshop on Knowledge Media Networking.

[6]  Tomoichi Takahashi,et al.  Agent Based Approach in Disaster Rescue Simulation - From Test-Bed of Multiagent System to Practical Application , 2001, RoboCup.

[7]  Pattie Maes,et al.  Kasbah: An Agent Marketplace for Buying and Selling Goods , 1996, PAAM.

[8]  Michael Luck,et al.  Coalition formation through motivation and trust , 2003, AAMAS '03.

[9]  B. Zikmund‐Fisher De-escalation after repeated negative feedback: Emergent expectations of failure , 2004 .

[10]  Sarit Kraus,et al.  Mass programmed agents for simulating human strategies in large scale systems , 2007, AAMAS '07.

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

[12]  Nigel Harvey,et al.  Learning to Use and Assess Advice about Risk , 2006 .

[13]  J Ormel,et al.  Factors affecting contrasting results between self-reported and performance-based levels of physical limitation. , 1996, Age and ageing.

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

[15]  S. Mullainathan,et al.  Do People Mean What They Say? Implications for Subjective Survey Data , 2001 .

[16]  Hitoshi Furuta,et al.  Evacuation simulation in underground mall by artificial life technology , 2003, Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. ISUMA 2003..

[17]  W. Güth,et al.  Attitudes Towards Risk: An Experiment , 2003 .

[18]  Daniel Thalmann,et al.  Towards Interactive Real‐Time Crowd Behavior Simulation , 2002, Comput. Graph. Forum.

[19]  Gita Reese Sukthankar,et al.  Policy recognition for multi-player tactical scenarios , 2007, AAMAS '07.

[20]  John Yen,et al.  A Distributed Intelligent Agent Architecture for Simulating Aggregate-Level Behavior and Interactions on the Battlefield , 2001 .

[21]  M. Rabin Psychology and Economics , 1997 .