quantitative organizational modeling and design for multi-agent systems

QUANTITATIVE ORGANIZATIONAL MODELING AND DESIGN FOR MULTI-AGENT SYSTEMS A Dissertation Presented by BRYAN HORLING Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY February 2006 Department of Computer Science UMI Number: 3206188 3206188 2006 UMI Microform Copyright All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 by ProQuest Information and Learning Company. c  Copyright by Bryan Horling 2006 All Rights Reserved QUANTITATIVE ORGANIZATIONAL MODELING AND DESIGN FOR MULTI-AGENT SYSTEMS A Dissertation Presented by BRYAN HORLING Approved as to style and content by: Victor Lesser, Chair Brian Levine, Member Shlomo Zilberstein, Member Anna Nagurney, Member W. Bruce Croft, Department Chair Department of Computer Science To my wife and partner, Maura. ACKNOWLEDGMENTS This dissertation would not have been possible without the help, support and guidance from many people. No single person has influenced this process more than my adviser, Victor Lesser. Your encouragement, suggestions, comments and critiques have helped shape my work, and without your persistence I would not have even had the opportunity to do so. I would like to thank the members of my committee, Brian Levine, Anna Nagur- ney and Shlomo Zilberstein, for all their hard work. Your questions helped guide me towards interesting and challenging paths, and our discussions helped frame the broader context of this work. My family has been my foundation throughout my graduate career, giving me the opportunity and encouragement I needed to see it through. My wife and partner Maura deserves the most praise, as the time I needed to do my work frequently required sacrifice on her part. Your emotional support, patience, and willingness to listen as I ramble on about my research were all invaluable. I would like to thank Megan as well, for giving me the opportunity to get some writing done during your Monday afternoon naps, for only throwing my papers on the floor once, and for being there to play with when I didn’t feel like working. My mother, father and sister have supported me for more than thirty years now, and I clearly would not be where I am today without their help. There are too many points along the way that could be mentioned, but most of all it was your belief in me that provided the motivation I needed to see me through. v I would like to thank the Fennellys - Paul, Kathy, Jeff, Eileen and Kevin, for their encouragement, and for providing a place of refuge for Maura and I when one or both of us needed a break. Over the course of the last two years I had the opportunity to speak with many individuals about my work, and benefited greatly from these interactions. In no particular order I would like to thank Dan Corkill, Roger Mailler, Anita Raja, Sherief Abdallah, Mark Sims, Mark Fox, Neil Immerman, Les Gasser, Jim Kurose, Haizeng Zhang, Jiaying Shen, Raphen Becker, Katia Sycara, Frank Dignum, Virginia Dignum, Carl Hewitt and Tom Wagner for those conversations. I would like to thank Michele Roberts, without whom papers and forms would not reach their destinations, salaries would not be paid, and life in the lab would be generally less pleasant. I would also like to thank the past and present mem- bers of the Multi-Agent Systems Lab game night, which helped provide a needed distraction almost every week: Sherief Abdallah, Peter Amstutz, Mike Atighetchi, Raphen Becker, Brett Benyo, Ross Fairgrieve, Roger Mailler, Stephen Murtagh, Dan Neimann, Shichao Ou, Rodion Podorozhny, Kyle Rawlins, Jiaying Shen, Regis Vin- cent, Tom Wagner, and Ping Xuan. I have also benefited from my interactions with the many other members of the MAS lab, including Ana Bazzan, Andrew Fast, Nadia Ghamrawi, AnYuan Guo, Frank Klassner, Hala Mostafa, Mike O’Neill, John Ostwald, Anita Raja, Zach Rubinstein, Haizheng Zhang, and Shelley Zhang. I would like to single out Regis Vincent, Roger Mailler, Raphen Becker, Jiaying Shen and Kyle Rawlins for their help in designing and implementing the distributed sensor network platform analyzed in this work. I would also like to thank Haizheng Zhang for his research in information retrieval, which this work also exploits. The fine people at Freeverse Software (Ian, Colin & Steve) deserve thanks for giving me a creative outlet. Thanks also go to the Amherst College Computing Center and the authors of the JEP and JDOM software libraries. vi ABSTRACT QUANTITATIVE ORGANIZATIONAL MODELING AND DESIGN FOR MULTI-AGENT SYSTEMS FEBRUARY 2006 BRYAN HORLING B.Sc., TRINITY COLLEGE M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Victor Lesser As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants’ interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents particular and different roles, responsibilities and peers. These additional constraints can allow agents to operate effectively within a large-scale system, with little or no sacrifice in utility. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand and model the behavior of candidate designs. In the multi-agent systems community, relatively little attention has been paid to understanding and comparing organizations at a quantitative level. In this thesis, I vii show that it is possible to develop such an understanding, and in particular I show how quantitative information can form the basis of a predictive, proscriptive organizational model. This can in turn lead to more efficient, robust and context-sensitive systems by increasing the level of detail at which competing organizational designs are evaluated. To accomplish this, I introduce a new, domain-independent organizational design representation able to model and predict the quantitative performance characteris- tics of agent organizations. This representation, capable of capturing a wide range of multi-agent characteristics in a single, succinct model, supports the selection of an appropriate design given a particular operational context. I demonstrate the repre- sentational capabilities and efficacy of the language by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. In addition to their predictive ability, these same mod- els also describe the range of possible organizations in those domains. I show how general search techniques can be used to explore this space, using those quantitative predictions to evaluate alternatives and enable automated organizational design. viii TABLE OF CONTENTS Page ACKNOWLEDGMENTS v ABSTRACT vii LIST OF TABLES xiv LIST OF FIGURES xv CHAPTER 1. INTRODUCTION 1 1.1 Introduction 1 1.2 MajorIdeas 11 1.2.1 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.2 OrganizationalDesign 13 1.2.3 RepresentingOrganizations 15 1.3 GuidetotheDissertation 17 2. REPRESENTING ORGANIZATIONS 19 2.1 OrganizationalEffects 19 2.1.1 TheDistributedSensorNetworkDomain 20 2.1.2 Empirical Demonstration of Organizational Effects . . . . . . . . . . . . . 25 2.1.3 GeographicCoalitions 26 2.1.4 FunctionalDifferentiation 27 2.1.5 OrganizationalMaintenance 29 2.1.6 GeneralityofEffects 32 2.2 OrganizationalRepresentation 34 2.2.1 ODML 36 ix [...]... it is to make effective use of the resources and flexibility available to it I explore this tailoring through the system’s organizational design The notion of an organizational design is used in many different fields, and generally refers to how members of a society act and relate with one another This is true of multi-agent systems, where the organizational design of a system can include a description... alternatives in a quantitative way The latter may contain quantitative knowledge, but have difficulty relating that knowledge to specific organizational concepts, mitigating their usefulness if one is hoping to understand the effects a particular organizational design will have More specifically, existing organizational representations are either flexible and qualitative or inflexible and quantitative In this... create a representation that is both flexible and quantitative I introduce a new representation, the Organizational Design Modeling Language (ODML), designed to capture organizational information in a single unified, predictive structure This representation, described in detail in Chapter 2 has the capability to model a wide range of organizational paradigms and characteristics, at different levels of abstraction... system scales in number and scope [32] Imagine how difficult it would be for a large human organization, such as a corporation or government, to function if individuals lacked job descriptions and long-term peer relationships Agent systems face similar challenges, and can derive similar benefits from an explicit organizational design Consider the problem of designing a solution for a complex, resource-bounded... the feasibility of a highly quantitative representation and the increased utility that such a representation brings to the organizational design problem The following contributions will be made to that end 1 I show that a flexible, accurate organizational representation grounded in arbitrary quantitative information can be created This is accomplished through the design and implementation of the ODML... reasoned over, and guided by an organizational design 12 Later sections will present organizational models where additional assumptions may be made concerning the behaviors, conditions, or other features of the specific agents, environment or resources depicted in those models I describe any such assumptions within their respective contexts 1.2.2 Organizational Design The organizational design of a multi-agent. .. information retrieval templates Number of agents and utility are given for the optimal found organization 170 4.2 Results from organizational search in large-scale information retrieval templates Number of agents and utility are given for the optimal found organization 172 6.1 A comparison of the characteristics and. .. depiction of the performance predictions by the enhanced DSN model using non-uniform target paths (upper) and sensor locations (lower) The layouts (a,c) show the sensor and target arrangements for two different scenarios Sensors are circles, targets are triangles and their paths are dashed lines The graphs (b,d) show the demand levels by sensor (top, darker is target 0, lighter is target 1) and the measurement... communication and data processing takes time, and the number of sensors can be arbitrarily large, the weaknesses of this approach quickly become apparent A different strategy, in the form of a different organizational design, can compensate for these more challenging conditions For example, we might distribute the manager role among multiple agents to more evenly balance the communication and computational... outlined above Sections 1.2.2 and 1.2.3 expand upon the high-level concepts that support and motivate this work through additional discussion of the organizational design and representation problem 1.2.1 Basic Assumptions The strategy that I present to satisfy the objectives outlined above assumes that it is both possible to model the system in question and that different systems have measurable differences . Horling 2006 All Rights Reserved QUANTITATIVE ORGANIZATIONAL MODELING AND DESIGN FOR MULTI-AGENT SYSTEMS A Dissertation Presented by BRYAN HORLING Approved as to style and content by: Victor Lesser,. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand and model the behavior of candidate designs. In the multi-agent systems. QUANTITATIVE ORGANIZATIONAL MODELING AND DESIGN FOR MULTI-AGENT SYSTEMS A Dissertation Presented by BRYAN HORLING Submitted to the