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Emergent Behavior Development and Control in Multi-Agent Systems Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 8-16-2019 Emergent Behavior Development and Control in Multi-Agent Systems David W. King Follow this and additional works at: https://scholar.afit.edu/etd Part of the Artificial Intelligence and Robotics Commons Recommended Citation King, David W., "Emergent Behavior Development and Control in Multi-Agent Systems" (2019). Theses and Dissertations. 2376. https://scholar.afit.edu/etd/2376 This Dissertation is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. Emergent Behavior Development and Control in Multi-Agent Systems DISSERTATION David W. King, Maj, USAF AFIT-ENG-DS-19-S-007 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENG-DS-19-S-007 EMERGENT BEHAVIOR DEVELOPMENT AND CONTROL IN MULTI-AGENT SYSTEMS DISSERTATION Presented to the Faculty Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science David W. King, B.S.C.S., M.S.C.O. Maj, USAF August 14, 2019 DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENG-DS-19-S-007 EMERGENT BEHAVIOR DEVELOPMENT AND CONTROL IN MULTI-AGENT SYSTEMS DISSERTATION David W. King, B.S.C.S., M.S.C.O. Maj, USAF Committee Membership: Dr. Gilbert L. Peterson Chair Dr. Douglas D. Hodson Member Dr. Christine M. Schubert Kabban Member AFIT-ENG-DS-19-S-007 Abstract Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and pro- vides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring sys- tem survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for cre- ating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations. iv Table of Contents Page Abstract . iv List of Figures . viii Acknowledgements . xiii I. Introduction . .1 1.1 Hypothesis . .3 1.2 Background . .3 1.3 Research Questions . .6 1.4 Contributions . .7 1.5 Limitations . .8 1.6 Outline . .8 II. Background . 13 2.1 Emergence . 13 2.2 Approaches . 16 2.3 Definitions . 18 2.4 Entropy . 22 2.5 Genetic Algorithms . 26 III. Informal Team Assignment Algorithm . 29 3.1 Introduction . 29 3.2 Background . 30 3.3 Algorithm . 32 3.4 Experiments . 33 3.5 Scenario . 33 3.6 Agent Implementation . 34 3.7 Control and Independent Variables . 36 3.8 Results . 37 3.9 Conclusion. 42 IV. A Macro-Level Order Metric for Complex Systems . 44 4.1 Introduction . 44 4.2 Motivation . 45 4.3 Related Work. 47 4.4 Approach / Methodology . 51 4.5 Experimental Analysis . 56 Autonomous UAV Simulation . 56 v Page Scenario 1 . 56 Scenario 2 . 58 Scenario 3 . 60 Game of Life . 61 4.6 Discussion . 65 4.7 Conclusion. 67 V. Entropy-Based Team Self-Organization with Signal Suppression . 69 5.1 Introduction . 69 5.2 Motivation . 69 5.3 Related Work. 71 5.4 Problem Statement . 74 5.5 Entropy-based Team Self-organization . 75 5.6 Experiment . 76 5.7 Results . 77 5.8 Discussion . 85 5.9 Conclusion. 86 VI. The Emergence of Division of Labor in Multi-Agent Systems . 88 6.1 Introduction . 88 6.2 Motivation . 89 6.3 Background . 91 6.4 Methodology . 94 Evolutionary Method . 94 Domain ......................................................... 96 6.5 Experimental Analysis . 97 Pin Factory . ..
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