Sharing experiences to learn user characteristics in dynamic environments with sparse data

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 c...

• Type: Conference
• Year: 2007
• Where: ATAL
• Authors: David Sarne, Barbara J. Grosz
• Keywords: Artificial Intelligence, ATAL 2007, Direct Observations, Mechanism Outperforms Estimations