Implementing Feedback for Programming by Demonstration

Abstract

Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Each query to the framework is independent of previous queries. Our work addresses this information communication deficit and incorporates a feedback mechanism to iteratively improve the quality of the reproduced behavior. This is explored by variation of regression parameters and data points used. Using data point selection to improve demonstration replication is established as a means of iterative optimization. Using optimization also shows potential for improved demonstration replication capability for the framework.