Sampling beats fixed estimate predictors for cloning stochastic behavior in multiagent systems

Abstract

Modeling stochastic multiagent behavior such as fish schooling is challenging for fixed-estimate prediction techniques because they fail to reliably reproduce the stochastic aspects of the agents behavior. We show how standard fixed-estimate predictors fit within a probabilistic framework, and suggest the reason they work for certain classes of behaviors and not others. We quantify the degree of mismatch and offer alternative sampling-based modeling techniques. We are specifically interested in building executable models (as opposed to statistical or descriptive models) because we want to reproduce and study multiagent behavior in simulation. Such models can be used by biologists, sociologists, and economists to explain and predict individual and group behavior in novel scenarios, and to test hypotheses regarding group behavior. Developing models from observation of real systems is an obvious application of machine learning. Learning directly from data eliminates expensive hand processing and tuning, but introduces unique challenges that violate certain assumptions common in standard machine learning approaches. Our framework suggests a new class of sampling-based methods, which we implement and apply to simulated deterministic and stochastic schooling behaviors, as well as the observed schooling behavior of real fish. Experimental results show that our implementation performs comparably with standard learning techniques for deterministic behaviors, and better on stochastic behaviors.