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(Pdf), 2011: Bionically Inspired Information Representation For DISSERTATION Bionically Inspired Information Representation for Embodied Software Agents Realizing Neuropsychoanalytic Concepts of Information Processing Within the Computational Framework ARSi10 Submitted at the Faculty of Electrical Engineering and Information Technology, Vienna University of Technology in partial fulfillment of the requirements for the degree of Doktor der technischen Wissenschaften under supervision of Prof. Dr. Dietmar Dietrich Institute number: 384 Institute of Computer Technology Vienna University of Technology and Prof. Etienne Barnard Ph.D. Faculty of Economic Sciences and Information Technology North-West University, Republic of South Africa and Dr. Dietmar Bruckner Institute number: 384 Institute of Computer Technology Vienna University of Technology by Dipl.-Ing. (FH) Heimo Zeilinger Matr.Nr. e0326407 November 29th, 2010 _________________________________ Additional Proofreaders Dipl.-Ing. Tobias Deutsch Mag. Dr. Klaus Doblhammer Dipl.-Ing. Friedrich Gelbard Dipl.-Ing. (FH) Dr. Roland Lang Dipl.-Ing. (FH) Clemens Muchitsch Stephan Stockinger Mag. Iris Wanek I II Kurzfassung Thema der Arbeit ist die bionisch inspirierte Repräsentation von Information in der Kontrolleinheit eines Software-Agenten. Im Zentrum steht die erstmalige technische Umsetzung von neuropsycho- analytischen Konzepten zur Generierung und Verarbeitung mentaler Datenstrukturen, die bekannten bionisch inspirierten Ansätzen gegenübergestellt wird. Dadurch wird ein völlig neuer Weg in der Künstlichen Intelligenz beschritten, der Systeme ermöglichen soll die Kontrollprinzipien des menschlichen mentalen Apparates implementieren und dadurch in einem dynamischen Umfeld ziel- gerichtet agieren können. In der Konzeptionierung wird eine bestehende Entscheidungsfindungseinheit durch ein Informati- onsmanagementsystem, das Informationsverwaltungsfunktionalitäten und einen Datenspeicher um- fasst, vervollständigt. Die sich daraus ergebenden Adaptierungen werden über den Top-Down- Design Ansatz in einem neuen Modell definiert, dessen Implementierung das technische Framework ARSi10 ergibt. Die Realisierung erfolgt in körperbasierten Software-Agenten. Als Testumgebung wird der auf ei- nem Multiagenten-Simulationssystem basierende „Bubble World“ Simulator entwickelt, in dem das Verhalten der Agenten über definierte Anwendungsfälle bewertet wird. Die Evaluierung erfolgt anhand interner und externer Leistungsindikatoren. Hier zeigen sich die Stärken des entwickelten Systems. Information aus internen und externen Sensordaten wird auf neuropsychoanalytisch inspi- rierte Datenstrukturen abgebildet und für den Entscheidungsfindungsprozess genutzt. Dies ermög- licht es den Agenten mit ihrer Umwelt in einer Art und Weise zu interagieren, die die Balance ihrer Systemressourcen und dadurch die Aufrechterhaltung ihrer Funktionsfähigkeit zur Folge hat. III IV Abstract This work describes the bionically inspired representation of information in a control unit for em- bodied software agents. It focuses on the first ever realization of neuropsychoanalytic concepts for generating and processing mental data structures in computer science and compares said approach to established bionically inspired methodologies. The approach is completely new to Artificial Intelli- gence and should allow the design of systems following the principles of the human being’s mental apparatus, thus enabling them to operate in dynamically changing environments. An existing decision unit is supplemented with an information representation system composed of an information representation concept, a data storage, and an information management unit. By use of a top-down design approach, the resulting adaptations are introduced into a new model whose implementation in embodied software agents produces the computational framework ARSi10. The multi-agent framework-based simulator ‘Bubble World’ is developed as a test-bed with prede- fined use cases, and the agents’ abilities are evaluated through internal and external performance indicators. These show the benefits of the developed system. Internal and external sensor data are mapped to neuropsychoanalytically inspired data structures and used for the decision making pro- cess. This allows the agents to interact with their environment while keeping their system resources balanced and thus retaining their functional abilities. V VI Acknowledgements The work with this dissertation has been extensive and challenging, but in the main exciting, instruc- tive, and fun. Without encouragement and support from several persons, I would never have been able to write this work. First of all, I thank my supervisor Prof. Dr. Dietmar Dietrich for his inspiring and encouraging way to guide me to a deeper understanding of scientific work, and his inestimable comments. This work would not have been possible without the expert guidance of my second advisor, Prof. Etienne Barnard, Ph.D. Not only was he available for me anytime but he always commented and responded to the drafts of each chapter of my work more quickly than I could have wished. Thanks also to all my colleagues at the Institute of Computer Technology for providing a fantastic working atmosphere and especially to the current and former members of the ARS project team who supported me with their knowledge and programming skills. My appreciation goes to my family who motivated me to write this work and whose love and sup- port still sustain me today. Most of all I thank my loving and patient partner in life Iris whose faithful support in managing dif- ficulties of this work is so appreciated. Thank you. VII VIII Table of Contents 1. Introduction .......................................................................................................................... 1 1.1 Heading Towards Intelligence ....................................................................................... 1 1.2 The Neuropsychoanalytic Approach for Engineering Applications .............................. 4 1.3 Methodology .................................................................................................................. 8 2. Related Work ..................................................................................................................... 13 2.1 Embodiment, Emotions, and Common Misunderstandings in Artificial Intelligence . 13 2.1.1 Embodiment in Autonomous Agents ................................................................... 13 2.1.2 Emotions in Autonomous Agents ......................................................................... 15 2.2 Psychoanalysis meets Artificial Intelligence ............................................................... 20 2.3 Information Representation in Autonomous Agents .................................................... 25 2.3.1 Knowledge Representation Techniques ............................................................... 25 2.3.2 Memory Based Control Architectures .................................................................. 28 2.3.3 Information Representation Concepts in Autonomous Agents ............................ 31 2.4 Evaluation of the Information Representation System ................................................ 37 2.4.1 Entering Artificial Worlds .................................................................................... 37 2.4.2 Multi-Agent Simulation Platforms ....................................................................... 39 2.4.3 Agent Performance Evaluation ............................................................................. 42 2.5 From Sensor Signals to Neuro-symbols ....................................................................... 43 2.6 Outcomes ..................................................................................................................... 46 3. Concept and Model ............................................................................................................ 48 3.1 Psychoanalytic Terms and Technical Definitions ........................................................ 48 3.2 Technical Conceptualization of Psychoanalytically Inspired Data Structures ............. 53 3.2.1 Atomic Data Structures ........................................................................................ 53 3.2.2 Images, Scenarios, and Acts ................................................................................. 58 3.3 The Artificial Recognition System Decision Unit ....................................................... 62 3.3.1 Top-down Design Approach ................................................................................ 62 3.3.2 Topological View of the Body ............................................................................. 64 3.3.3 Fourth Topological Layer ..................................................................................... 66 3.3.4 Third Topological Layer ....................................................................................... 67 3.3.5 Second Topological Layer .................................................................................... 70 3.3.6 First Topological Layer ........................................................................................ 72 3.4 Information Representation Management .................................................................... 80 IX 3.4.1 Information Management System ......................................................................... 80 3.4.2 Activation and Retrieval of Stored Data Structures .............................................. 84 4. Examined
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