Article

Are you doing what i think you are doing? criticising uncertain agent models

Authors:

Stefano V. Albrecht,

Subramanian RamamoorthyAuthors Info & Claims

UAI'15: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence

Pages 52 - 61

Published: 12 July 2015 Publication History

Abstract

The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there exist several methods for the construction of a behavioural hypothesis, there is currently no universal theory which would allow an agent to contemplate the correctness of a hypothesis. In this work, we present a novel algorithm which decides this question in the form of a frequentist hypothesis test. The algorithm allows for multiple metrics in the construction of the test statistic and learns its distribution during the interaction process, with asymptotic correctness guarantees. We present results from a comprehensive set of experiments, demonstrating that the algorithm achieves high accuracy and scalability at low computational costs.

References

[1]

S.V. Albrecht and S. Ramamoorthy. On convergence and optimality of best-response learning with policy types in multiagent systems. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages 12-21, 2014.

Digital Library

[2]

S.V. Albrecht, J.W. Crandall, and S. Ramamoorthy. An empirical study on the practical impact of prior beliefs over policy types. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, pages 1988-1994, 2015.

Digital Library

[3]

A. Azzalini. A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 12:171-178, 1985.

[4]

I.V. Basawa and D.J. Scott. Efficient tests for stochastic processes. Sankhyā: The Indian Journal of Statistics, Series A, pages 21-31, 1977.

[5]

M.J. Bayarri and J.O. Berger. P values for composite null models. Journal of the American Statistical Association, 95(452):1127-1142, 2000.

[6]

J.O. Berger and T. Sellke. Testing a point null hypothesis: the irreconcilability of p values and evidence (with discussion). Journal of the American Statistical Association, 82:112-122, 1987.

[7]

G.E.P. Box. Sampling and Bayes' inference in scientific modelling and robustness. Journal of the Royal Statistical Society. Series A (General), pages 383-130, 1980.

[8]

G.W. Brown. Iterative solution of games by fictitious play. Activity Analysis of Production and Allocation, 13(1):374-376, 1951.

[9]

S. Carberry. Techniques for plan recognition. User Modeling and User-Adapted Interaction, 11(1-2):31-48, 2001.

Digital Library

[10]

D. Carmel and S. Markovitch. Exploration strategies for model-based learning in multi-agent systems: Exploration strategies. Autonomous Agents and Multi-Agent Systems, 2(2):141-172, 1999.

Digital Library

[11]

E. Charniak and R.P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53-79, 1993.

Digital Library

[12]

E.M. Clarke, O. Grumberg, and D.A. Peled. Model Checking. MIT Press, 1999.

Digital Library

[13]

V. Conitzer and T. Sandholm. AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. Machine Learning, 67(1-2):23-43, 2007.

Digital Library

[14]

D.R. Cox. The role of significance tests (with discussion). Scandinavian Journal of Statistics, 4:49-70, 1977.

[15]

H. Fischer. A History of the Central Limit Theorem: From Classical to Modern Probability Theory. Springer Science & Business Media, 2010.

[16]

R.A. Fisher. The Design of Experiments. Oliver & Boyd, 1935.

[17]

D.P. Foster and H.P. Young. Learning, hypothesis testing, and Nash equilibrium. Games and Economic Behavior, 45(1):73-96, 2003.

[18]

A. Gelman and C.R. Shalizi. Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66(1):8-38, 2013.

[19]

I. Gilboa and D. Schmeidler. A Theory of Case-Based Decisions. Cambridge University Press, 2001.

[20]

P.J. Gmytrasiewicz and P. Doshi. A framework for sequential planning in multiagent settings. Journal of Artificial Intelligence Research, 24(1):49-79, 2005.

Digital Library

[21]

K.G. Larsen and A. Skou. Bisimulation through probabilistic testing. Information and Computation, 94(1):1-28, 1991.

Digital Library

[22]

X.-L. Meng. Posterior predictive p-values. The Annals of Statistics, pages 1142-1160, 1994.

[23]

A. O'Hagan and T. Leonard. Bayes estimation subject to uncertainty about parameter constraints. Biometrika, 63(1):201-203, 1976.

[24]

D.B. Rubin. Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics, 12(4):1151-1172, 1984.

[25]

D. Ryabko and B. Ryabko. On hypotheses testing for ergodic processes. In Proceedings of IEEE Information Theory Workshop, pages 281-283, 2008.

[26]

A. Vehtari and J. Ojanen. A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys, 6:142-228, 2012.

[27]

Y. Yue, Y. Gao, O. Chapelle, Y. Zhang, and T. Joachims. Learning more powerful test statistics for click-based retrieval evaluation. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 507-514, 2010.

Digital Library

Cited By

Chandrasekaran MDoshi PZeng YChen Y(2017)Can bounded and self-interested agents be teammates? Application to planning in ad hoc teamsAutonomous Agents and Multi-Agent Systems10.1007/s10458-016-9354-431:4(821-860)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10458-016-9354-4
Tian XZhuo HKambhampati SJonker CMarsella SThangarajah JTuyls K(2016)Discovering Underlying Plans Based on Distributed Representations of ActionsProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems10.5555/2936924.2937091(1135-1143)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.5555/2936924.2937091

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Information & Contributors

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Published In

cover image Guide Proceedings

UAI'15: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence

July 2015

981 pages

ISBN:9780996643108

Editors:
Marina Meila
University of Washington
,
Tom Heskes
Radboud University, Netherlands

Sponsors

Adobe
Google Inc.
Artificial Intelligence Journal
Microsoft Research: Microsoft Research
Facebook: Facebook

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AUAI Press

Arlington, Virginia, United States

Publication History

Published: 12 July 2015

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Cited By

Chandrasekaran MDoshi PZeng YChen Y(2017)Can bounded and self-interested agents be teammates? Application to planning in ad hoc teamsAutonomous Agents and Multi-Agent Systems10.1007/s10458-016-9354-431:4(821-860)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10458-016-9354-4
Tian XZhuo HKambhampati SJonker CMarsella SThangarajah JTuyls K(2016)Discovering Underlying Plans Based on Distributed Representations of ActionsProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems10.5555/2936924.2937091(1135-1143)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.5555/2936924.2937091

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