Improved Reverse Mapping for Controlling Swarms by Visual 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. Our work extends that framework by refining the data that is aggregated to produce the agent-level parameters that the framework provides to the demonstrator. This is done using pruning and outlier detection based on information that is intrinsic to those data points (their source). Using pruning and outlier detection shows potential to refine the aggregation data to a fraction of its size, while maintaining or potentially improving performance in replication of demonstrations.