On Automated Agents ’ Rationality

Agents that interact with humans are known to benefit from integrating behavioral science and exploiting the fact that humans are irrational. Therefore, when designing agents for interacting with automated agents, it is crucial to know whether the other agents are acting irrationally and if so to what extent. However, little is known about whether irrationality is found in automated agent design. Do automated agents suffer from irrationality? If so, is it similar in nature and extent to human irrationality? How do agents act in domains where human irrationality is motivated by emotion? This is the first time that extensive experimental evaluation was performed in order to resolve these questions. We evaluated agent rationality (for non-expert agents) in several environments and compared agent actions to human actions. We found that automated agents suffer from the same irrationality that humans display, although to a lesser degree.

[1]  J. Claxton,et al.  A Taxonomy of Prepurchase Information Gathering Patterns , 1974 .

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

[3]  A. Tversky,et al.  The framing of decisions and the psychology of choice. , 1981, Science.

[4]  R. Selten,et al.  End behavior in sequences of finite prisoner's dilemma supergames , 1986 .

[5]  Girish N. Punj,et al.  A Typology of Individual Search Strategies Among Purchasers of New Automobiles , 1984 .

[6]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[7]  G. Loewenstein Anticipation and the Valuation of Delayed Consumption , 1987 .

[8]  Colin Camerer,et al.  EXPERIMENTAL TESTS OF A SEQUENTIAL EQUILIBRIUM REPUTATION MODEL , 1988 .

[9]  David Carmel,et al.  Opponent Modeling in Multi-Agent Systems , 1995, Adaption and Learning in Multi-Agent Systems.

[10]  Nicolò Cesa-Bianchi,et al.  Gambling in a rigged casino: The adversarial multi-armed bandit problem , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[11]  Joyce E. Berg,et al.  Trust, Reciprocity, and Social History , 1995 .

[12]  Joyce E. Berg,et al.  Trust, reciprocity and social history’, Games and Economic Behaviour, . , 1995 .

[13]  Shou-De Lin,et al.  Designing the Market Game for a Trading Agent Competition , 2001, IEEE Internet Comput..

[14]  Manuela M. Veloso,et al.  Planning for Distributed Execution through Use of Probabilistic Opponent Models , 2002, AIPS.

[15]  D. Ariely,et al.  “Coherent Arbitrariness”: Stable Demand Curves Without Stable Preferences , 2003 .

[16]  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..

[17]  Peter McCracken,et al.  Safe Strategies for Agent Modelling in Games , 2004, AAAI Technical Report.

[18]  Daniel Schunk,et al.  The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment , 2009 .

[19]  Dan Ariely,et al.  Keeping Doors Open: The Effect of Unavailability on Incentives to Keep Options Viable , 2004, Manag. Sci..

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

[21]  Koen V. Hindriks,et al.  Opponent modelling in automated multi-issue negotiation using Bayesian learning , 2008, AAMAS.

[22]  Jorma Sajaniemi Guest Editor's Introduction: Psychology of Programming : Looking into Programmers Heads , 2008 .

[23]  Alessandro Lazaric,et al.  On the usefulness of opponent modeling: the Kuhn Poker case study , 2008, AAMAS.

[24]  S. Gächter Behavioral Game Theory , 2008, Encyclopedia of Evolutionary Psychological Science.

[25]  Sarit Kraus,et al.  Facing the challenge of human-agent negotiations via effective general opponent modeling , 2009, AAMAS.

[26]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[27]  Amos Azaria,et al.  Strategic Information Disclosure to People with Multiple Alternatives , 2011, AAAI.

[28]  Sarit Kraus,et al.  Using aspiration adaptation theory to improve learning , 2011, AAMAS.

[29]  Amos Azaria,et al.  Giving Advice to People in Path Selection Problems , 2012, Interactive Decision Theory and Game Theory.

[30]  Jordi Brandts,et al.  The strategy versus the direct-response method: a first survey of experimental comparisons , 2011 .

[31]  Amos Azaria,et al.  Automated Strategies for Determining Rewards for Human Work , 2012, AAAI.

[32]  Martin Zinkevich,et al.  The Annual Computer Poker Competition , 2013, AI Mag..

[33]  ปิยดา สมบัติวัฒนา Behavioral Game Theory: Experiments in Strategic Interaction , 2013 .

[34]  Ya'akov Gal,et al.  A study of computational and human strategies in revelation games , 2014, Autonomous Agents and Multi-Agent Systems.

[35]  Sarit Kraus,et al.  The Development of the Strategic Behavior of Peer Designed Agents , 2014, Language, Culture, Computation.