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.