Stefano V. Albrecht – Smart Society Project http://www.smart-society-project.eu "Hybrid and Diversity-Aware Collective Adaptive Systems: When People Meet Machines to Build a Smarter Society" Fri, 10 Feb 2017 14:56:03 +0000 en-US hourly 1 https://wordpress.org/?v=4.5.2 http://www.smart-society-project.eu/wp-content/uploads/2014/01/favicon1.png Stefano V. Albrecht – Smart Society Project http://www.smart-society-project.eu 32 32 Are you doing what I think you are doing? Criticising uncertain agent models http://www.smart-society-project.eu/areyoudongwhatithinkyouaredoing/ http://www.smart-society-project.eu/areyoudongwhatithinkyouaredoing/#respond Thu, 12 Jan 2017 23:05:10 +0000 http://www.smart-society-project.eu/?p=3214 Continue reading ]]>

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.

Citation: S. Albrecht, S. Ramamoorthy, Are you doing what I think you are doing? Criticising uncertain agent models, In Proc. Conference on Uncertainty in Artificial Intelligence (UAI), 2015.

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An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types http://www.smart-society-project.eu/anempiricalstudy/ http://www.smart-society-project.eu/anempiricalstudy/#respond Thu, 12 Jan 2017 22:50:01 +0000 http://www.smart-society-project.eu/?p=3210 Continue reading ]]>

Abstract: Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

Citation: S. Albrecht, J. Crandall, S. Ramamoorthy, An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types, In Proc. AAAI Conference on Artificial Intelligence (AAAI), 2015.

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