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Feature Construction for Controlling Swarms by Visual Demonstration

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Published:25 May 2017Publication History
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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. While many approaches involve manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the meta-modeling framework in Miner [2010] generates mapping functions between agent-level parameters and swarm-level parameters, which are re-usable once generated. This work builds on that framework by integrating demonstration by image or video. The demonstrator specifies spatial motion of the agents over time and retrieves agent-level parameters required to execute that motion. The framework, at its core, uses computationally cheap image-processing algorithms. Our work is tested with a combination of primitive visual feature extraction methods (contour area and shape) and features generated using a pre-trained deep neural network in different stages of image featurization. The framework is also evaluated for its potential using complex visual features for all image featurization stages. Experimental results show significant coherence between demonstrated behavior and predicted behavior based on estimated agent-level parameters specific to the spatial arrangement of agents.

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        • Published in

          cover image ACM Transactions on Autonomous and Adaptive Systems
          ACM Transactions on Autonomous and Adaptive Systems  Volume 12, Issue 2
          June 2017
          162 pages
          ISSN:1556-4665
          EISSN:1556-4703
          DOI:10.1145/3099619
          Issue’s Table of Contents

          Copyright © 2017 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 25 May 2017
          • Accepted: 1 March 2017
          • Received: 1 December 2016
          Published in taas Volume 12, Issue 2

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