Human cognitive processes and defense mechanisms, as described in psychoanalysis, bring about new notions and paradigms for artificial intelligence systems. One key reason is that the human cognitive processes and defense mechanisms in question can accomplish conflict detection functionalities, filter functionalities, and other system stabilizing tasks within artificial intelligence systems. Yet artificial cognitive architectures lack the capability to analyze complex situations as well as the universal competencies needed to orientate themselves in complex environments in various domains. Psychoanalytic Defense Mechanisms in Cognitive Multi-Agent Systems addresses this dilemma by exploring how to describe, model, and implement psychoanalytic defense mechanisms in the course of a project that provides a functional model of the human mind.
With discussions focusing on the development of a mathematical description for the implementation of conflict detection, the activation and selection of defense mechanisms, and the processing of defense mechanisms, Psychoanalytic Defense Mechanisms in Cognitive Multi-Agent Systems describes the decisive points for the application of defense mechanisms in artificial intelligence. Formulae that treat defense mechanisms as transformations are also provided. Interdisciplinary cooperation between the scientific fields of psychoanalysis and artificial intelligence is highlighted as the foundation of new research findings throughout the book.
Innovative and exciting, this book will be of great interest to academics, researchers, and postgraduates in the fields of cognitive science, artificial intelligence, and psychoanalysis.
Table of Contents
1. Motivation, Requirements, and Methodology 1.1 Context and Overview 1.2 Motivation 1.3 Requirements 1.4 Assignment 1.5 Methodology 1.5.1 Development Process 1.5.2 Interdisciplinary Work 1.5.3 Technical Description of Defense Processes 1.6 Conclusion 1.7 Literature 2. Context of the Field of Research 2.1 Cognitive Architectures 2.1.1 Biologically Inspired Cognitive Architectures 2.1.2 Large-Scale Brain Simulation 2.1.3 Neurocognitive Informatics 2.1.4 Valued Perception, Focus of Attention 2.1.5 Categorization of Cognitive Architectures 2.2 Technical Models of Psychoanalytic Defense Mechanisms 2.2.1 Formal Description of Defense Mechanisms 2.2.2 Model of Super-Ego, Ego, and Id 2.2.3 Psychodynamic Architecture 2.2.4 Theory of Defense Processes 2.3 Six-Layer Model of the Human Mind 2.4 Deductions for the Present Work 2.5 Conclusion 2.6 Literature 3. Concepts to Develop a Technical Model of the Human Mind 3.1 From the Second Topographical Model to an Implementable Model 3.1.1 Ego, Id, and Super-Ego 3.1.2 Psychoanalytic Defense Mechanisms 3.2 Key Concepts to Develop the Technical Model of Defense Processes 3.2.1 Super-Ego Strength and Ego Strength 3.2.2 Conflict Strength 3.2.3 Psychic Intensity 3.3 Definitions of Psychoanalytic Terms Used in the Introduced Model 3.4 Definitions of Technical Terms Used in the Introduced Model 3.5 The ARS Project 3.5.1 Decision Unit for Autonomous Agents 3.5.2 Bionically Inspired Information Representation 3.5.3 Human Bionically Inspired Autonomous Agents 3.5.4 The Functional Model of ARS 3.6 Conclusion 3.7 Literature 4. Technical Model of the Defense Processes 4.1 Top-Down Model of Defense Processes 4.2 Defense Mechanisms Used in the Functional Model of ARS 4.2.1 Defense Mechanisms for Drives 4.2.2 Defense Mechanisms for Emotions 4.2.3 Defense Mechanisms for Perceptions 4.3 Additional Defense Mechanisms not Used in the ARS Functional Model 4.4 Selection of Defense Mechanisms 4.4.1 Selection of Defense Mechanisms for Drive Wishes 4.4.2 Selection of Defense Mechanisms for Emotions 4.4.3 Selection of Defense Mechanisms for Perceptions 4.5 Functionality of Defense Mechanisms for Drive Wishes 4.6 Functionality of Defense Mechanisms for Perceptions 4.7 Different Cases of Defense 4.7.1 Defense full, new drive aim/object already in list of associations 4.7.2 Defense full, new drive aim/object not in list of associations 4.7.3 Defense partly, new drive aim/object already in list of associations 4.7.4 Defense partly, new drive aim/object not in list of associations 4.8 Schematic Diagram of Defense Mechanisms 4.9 Technical Filter Mechanisms Compared to Psychoanalytic Defense Mechanisms 4.9.1 Repression 4.9.2 Displacement, Sublimation 4.9.3 Projection 4.9.4 Denial or Disavowal 4.9.5 Isolation 4.9.6 Splitting Idealization 4.9.7 Splitting Depreciation 4.10 Conclusion 4.11 Literature 5. Implementation of the Defense Processes 5.1 Psychoanalytic Module Descriptions 5.1.1 Defense Mechanisms 5.1.2 Super-Ego 5.1.3 Blocked Drive Content 5.2 Technical Module Descriptions 5.2.1 Defense Mechanisms 5.2.2 Super-Ego 5.2.3 Blocked Drive Content 5.3 Function Parameters 5.3.1 Description of Data Flow 5.3.2 Description of Data Structures 5.4 Defense Mechanisms Described as Transformations 5.4.1 Data Structures 5.4.2 Kinds of Transformations 5.4.3 Drive Transformations 5.4.4 Perception Transformations 5.5 UML Diagrams for Implementation 5.5.1 Class diagram of F6_DefenseMechanismsForDrives 5.5.2 Class Diagram of F19_DefenseMechanismsForPerception 5.5.3 Sequence Diagram 5.5.4 Data Flow Diagram 5.6 Conclusion 5.7 Literature 6. Simulation Environment and Performed Experiments 6.1 Embodiment versus Virtual Embodiment 6.2 Simulation Framework 6.2.1 Super-Ego Rules for the Use Cases 6.2.2 Principle of the Use Cases 6.2.3 Data Structures in the Simulation 6.2.4 Simulation Steps and Application Step 6.3 Super-Ego Rules in the Simulation Environment 6.3.1 User Interface for Super-Ego Rules 6.3.2 Super-Ego Rules File 6.4 Performed Experiments 6.4.1 Personality Parameters of Defense Mechanisms 6.4.2 Prerequisites for the Performed Experiments 6.4.3 Results of Experiment 1 6.4.4 Results of Experiment 2 6.4.5 Results of Experiment 3 6.4.6 Results of Experiment 4 6.5 Conclusion 6.6 Literature 7. Conclusion and Outlook 7.1 Achievements 7.2 Requirements Revisited 7.3 Future Improvements 7.4 Literature About the Author
Dr Friedrich Gelbard has been a researcher in the fields of cognitive science and multi-agent systems for more than ten years. He holds degrees in telecommunications and software engineering, and he has been a research assistant at the Vienna University of Technology for the last eight years. He worked in New York City for one year.
‘This book provides a refreshing, interdisciplinary perspective on how to build an Artificial General Intelligence. Friedrich Gelbard very systematically not only conceptualizes, but also implements a cognitive architecture based on theoretical contributions from psychoanalysis -- a much neglected approach in mainstream artificial intelligence research. I highly recommend this contribution as a 'must read' for any interdisciplinary scholar at the intersection between multi-agent systems, cognitive science, and affective computing.’ - Christian Becker-Asano, Corporate Sector Research and Advance Engineering, Robert Bosch GmbH, Germany"