research-article

Feature Construction for Controlling Swarms by Visual Demonstration

Authors:

Karan K. Budhraja,

Tim OatesAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 12, Issue 2

Article No.: 10, Pages 1 - 22

https://doi.org/10.1145/3084541

Published: 25 May 2017 Publication History

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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Cited By

Sun YZheng HPan JWu JLu GHuang JYin GLiu X(2024)A Design Method of Visual Demonstration Software2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00044(184-187)Online publication date: 12-Jan-2024
https://doi.org/10.1109/BDICN62775.2024.00044
Huang SLiu HChen BFang ZTan TKuo S(2019)A Gray Relational Analysis-Based Motion Detection Algorithm for Real-World Surveillance Sensor DeploymentIEEE Sensors Journal10.1109/JSEN.2018.287918719:3(1019-1027)Online publication date: 1-Feb-2019
https://doi.org/10.1109/JSEN.2018.2879187
Budhraja KOates T(2018)Implementing Feedback for Programming by Demonstration2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)10.1109/SASO.2018.00028(162-167)Online publication date: Sep-2018
https://doi.org/10.1109/SASO.2018.00028
Show More Cited By

Index Terms

Feature Construction for Controlling Swarms by Visual Demonstration
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
      2. Multi-agent systems
    2. Planning and scheduling
      1. Planning with abstraction and generalization

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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

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2017

Accepted: 01 March 2017

Received: 01 December 2016

Published in TAAS Volume 12, Issue 2

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Cited By

Sun YZheng HPan JWu JLu GHuang JYin GLiu X(2024)A Design Method of Visual Demonstration Software2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00044(184-187)Online publication date: 12-Jan-2024
https://doi.org/10.1109/BDICN62775.2024.00044
Huang SLiu HChen BFang ZTan TKuo S(2019)A Gray Relational Analysis-Based Motion Detection Algorithm for Real-World Surveillance Sensor DeploymentIEEE Sensors Journal10.1109/JSEN.2018.287918719:3(1019-1027)Online publication date: 1-Feb-2019
https://doi.org/10.1109/JSEN.2018.2879187
Budhraja KOates T(2018)Implementing Feedback for Programming by Demonstration2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)10.1109/SASO.2018.00028(162-167)Online publication date: Sep-2018
https://doi.org/10.1109/SASO.2018.00028
Seipp BBudhraja KOates T(2018)Optimizing Transitions between Abstract ABM Demonstrations2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)10.1109/SASO.2018.00021(100-109)Online publication date: Sep-2018
https://doi.org/10.1109/SASO.2018.00021
Budhraja KOates T(2018)Improved Reverse Mapping for Controlling Swarms by Visual Demonstration2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)10.1109/FAS-W.2018.00037(130-135)Online publication date: Sep-2018
https://doi.org/10.1109/FAS-W.2018.00037
Budhraja KOates T(2017)Dataset Selection for Controlling Swarms by Visual Demonstration2017 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2017.128(932-941)Online publication date: Nov-2017
https://doi.org/10.1109/ICDMW.2017.128

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