PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sharing Experiences to Learn User Characteristics in Dynamic Environments with Sparse Data
David Sarne and Barbara Grosz
In: 6th International Joint Conference on Autonomous Agents, May 14-18, 2007, Honolulu, Hawaii.


This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agent system, has no a priori information about the structure of the distribution of parameter values. The agent must be able to produce estimations even when it may have made only a small number of direct observations, and thus it must be able to operate with sparse data. The paper describes a mechanism that enables the agent to significantly improve its estimation by augmenting its direct observations with those obtained by other agents with which it is coordinating. To avoid undesirable bias in relatively heterogeneous environments while effectively using relevant data to improve its estimations, the mechanism weighs the contributions of other agents’ observations based on a real-time estimation of the level of similarity between each of these agents and itself. The “coordination autonomy” module of a coordination-manager system provided an empirical setting for evaluation. Simulation-based evaluations demonstrated that the proposed mechanism outperforms estimations based exclusively on an agent’s own observations as well as estimations based on an unweighted aggregate of all other agents’ observations.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
ID Code:3853
Deposited By:David Sarne
Deposited On:25 February 2008