A theory of mind or intelligence that derives from elements of philosophical, biological, linguistic, and psychological thought as well as of physics and information theory is presented. The hypothesis is defended that intelligence is not a thing but a composite of activities and attributes that must be described in terms of the evolution and interactions of systems, emerging into the environments in which they are embedded. It is proposed that a machine intelligence that emulates human intelligence must conform to certain restrictions that derive from accepting this hypothesis. In particular an implication is that for machine intelligence to be accepted as human-like intelligence it must be produced by a machine that functions in a manner substantially similar to a man and that interacts, grows or learns in and with an environment similar to that in which a man grows and learns. It is proposed that it should be possible to create machines and programs capable of this and that they can achieve intelligence with a large, but not arbitrarily large, degree of human-like characteristics. One system (of many possible systems), in development, based on grammars, and that satisfies some of those requirements is described. It consists of an artificial environment in which a grammar like program based on augmented transition networks, interacts with a human teacher. The purpose of KARA is to learn about the environment by being told and by imitation. / Source: Dissertation Abstracts International, Volume: 50-12, Section: B, page: 5732. / Major Professor: Lois Hawkes. / Thesis (Ph.D.)--The Florida State University, 1989.

A general method is described for translating an expert system into a functionally equivalent neural network. The neural network may be retranslated back into a functionally equivalent expert system. An example of the translation methodology is applied to the expert system shell M.1. The primary motivation for the development of the translation process is to aid in knowledge acquisition, however, there may be other applications such as the migration of a finished expert system to a neural network chip. The expert system and neural network knowledge base may be modified by either traditional knowledge engineering methods, associative learning techniques, or a synthesis of the two methods. Two learning algorithms are described that are appropriate for learning in the hybrid system--a modified backpropagation method and Goal-Directed Monte Carlo learning. These methods are used to demonstrate the repair of damaged certainty factors in several small rule bases. / Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1547. / Major Professor: R. C. Lacher. / Thesis (Ph.D.)--The Florida State University, 1991.

Verification and validation are essential to providing quality assurance for expert systems. The same level of quality assurance is expected of expert systems as has been demanded in conventional software. Verification and validation provide a systematic, comprehensive approach to quality assurance for conventional software. Such quality assurance is lacking in the development of expert systems. Current practice in verification and validation consists of the informal, haphazard testing of a few test cases. A lesson from the software crisis of conventional software is that quality can not be measured by such testing. Therefore, a systematic, comprehensive approach to verification and validation is necessary if quality assurance is to be provided for expert systems. / A methodology for the verification and validation of rule-based expert systems is developed in this research. The research objectives are to define verification and validation for expert systems, to delineate the activities required for thorough verification and validation of rule-based expert systems, and to develop a comprehensive set of techniques and tools for validation of rule-based expert systems. To this end, the contributions of this research are the recommendations for a systematic, comprehensive approach to the verification and validation of rule-based expert systems and the development of a complete set of techniques and tools for effective validation of rule-based expert systems. / Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1557. / Major Professor: Abraham Kandel. / Thesis (Ph.D.)--The Florida State University, 1991.

Esta tese propõe um modelo probabilístico geral para a localização cooperativa de multirrobôs. O problema da localização multirrobôs pode ser definido como: dado um modelo do ambiente, estimar a localização de cada robô em um grupo atuando em um mesmo ambiente, com base nas informações sensoriais oferecidas pelas medidas de odometria, medidas do ambiente e detecções. Detecção é a habilidade de um robô identificar outro e determinar a distância relativa entre eles. A idéia principal da localização cooperativa de multirrobôs consiste em integrar medidas coletadas por diferentes robôs, de modo que todos possam se beneficiar dos dados adquiridos pelos outros robôs do grupo. Desta forma, detecções podem ser usadas para refinar a estimativa de postura de cada robô com base nas estimativas dos outros. Comunicação fornece aos robôs a habilidade de compartilhar suas crenças de postura de forma que possam cooperar para melhorar a acurácia da localização. Aqui é explorado o uso de diferentes tipos de informação para comunicar entre os robôs: propagação da detecção positiva, detecção negativa e multidetecção, os quais são integrados em um novo algoritmo, chamado Localização de Markov para Multirrobôs Cooperativos (LMMC). Também é proposto um protocolo de comunicação para a troca de dados entre os robôs e um conjunto de critérios que possibilitam a redução da comunicação por meio da diminuição da quantidade de dados trocados entre robôs, de um modo eficaz e eficiente. Os experimentos realizados em ambientes simulados demonstram que a abordagem proposta pode conduzir a resultados significativamente melhores de localização quando comparada à abordagem com detecção única e ainda com uma menor quantidade de mensagens trocadas entre os robôs. / In this thesis we propose a general probabilistic model to cooperative multirobot localization. The multirobot localization problem can be stated as follows: given a model of the environment, estimate the location of each robot in a group within the same environment based on sensors information that provides odometric measurements, environment measurements, and detections. Detection is the ability of one robot to identify others and to determine the relative location of other robots relative to its own. The key idea behind the multirobot localization approach is to integrate measurements taken by different robots, so that each one can benefit from data gathered by other robots in a group. In this sense, detections can be used to refine the pose estimates of a robot based on the other\'s estimate. Communication provides robots with the ability to exchange their pose beliefs, so that they can cooperate in order to improve their localization accuracy. We explore the use of different types of information to exchange among robots: propagation of positive detection, negative detection, and multidetection, which are integrated in a new algorithm, called Cooperative Multirobot Markov Localization, CMML. We also contribute a communication protocol that deals with the data transmitted among robots, and a set of communication rules that aims at reducing the amount of data exchanged among robots in an effective and efficient way. Experimental results, carried out in simulated environments, demonstrate that our approach can yield better localization results than a single-detection approach, at significantly smaller communication overhead.

by Lo Man Keung. / Abstract in Chinese and English. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1987. / Bibliography: leaves 80-81.

Siu Fai Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- (Weighted) Constraint Satisfaction --- p.1 / Chapter 1.2 --- Set Variables --- p.2 / Chapter 1.3 --- Motivations and Goals --- p.3 / Chapter 1.4 --- Overview of the Thesis --- p.4 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.6 / Chapter 2.1.1 --- Backtracking Tree Search --- p.8 / Chapter 2.1.2 --- Consistency Notions --- p.10 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.14 / Chapter 2.2.1 --- Branch and Bound Search --- p.17 / Chapter 2.2.2 --- Consistency Notions --- p.19 / Chapter 2.3 --- Classical CSPs with Set Variables --- p.23 / Chapter 2.3.1 --- Set Variables and Set Domains --- p.24 / Chapter 2.3.2 --- Set Constraints --- p.24 / Chapter 2.3.3 --- Searching with Set Variables --- p.26 / Chapter 2.3.4 --- Set Bounds Consistency --- p.27 / Chapter 3 --- Weighted Constraint Satisfaction with Set Variables --- p.30 / Chapter 3.1 --- Set Variables --- p.30 / Chapter 3.2 --- Set Domains --- p.31 / Chapter 3.3 --- Set Constraints --- p.31 / Chapter 3.3.1 --- Zero-arity Constraint --- p.33 / Chapter 3.3.2 --- Unary Constraints --- p.33 / Chapter 3.3.3 --- Binary Constraints --- p.36 / Chapter 3.3.4 --- Ternary Constraints --- p.36 / Chapter 3.3.5 --- Cardinality Constraints --- p.37 / Chapter 3.4 --- Characteristics --- p.37 / Chapter 3.4.1 --- Space Complexity --- p.37 / Chapter 3.4.2 --- Generalization --- p.38 / Chapter 4 --- Consistency Notions and Algorithms for Set Variables --- p.41 / Chapter 4.1 --- Consistency Notions --- p.41 / Chapter 4.1.1 --- Element Node Consistency --- p.41 / Chapter 4.1.2 --- Element Arc Consistency --- p.43 / Chapter 4.1.3 --- Element Hyper-arc Consistency --- p.43 / Chapter 4.1.4 --- Weighted Cardinality Consistency --- p.45 / Chapter 4.1.5 --- Weighted Set Bounds Consistency --- p.46 / Chapter 4.2 --- Consistency Enforcing Algorithms --- p.47 / Chapter 4.2.1 --- "Enforcing Element, Node Consistency" --- p.48 / Chapter 4.2.2 --- Enforcing Element Arc Consistency --- p.51 / Chapter 4.2.3 --- Enforcing Element Hyper-arc Consistency --- p.52 / Chapter 4.2.4 --- Enforcing Weighted Cardinality Consistency --- p.54 / Chapter 4.2.5 --- Enforcing Weighted Set Bounds Consistency --- p.56 / Chapter 5 --- Experiments --- p.59 / Chapter 5.1 --- Modeling Set Variables Using 0-1 Variables --- p.60 / Chapter 5.2 --- Softening the Problems --- p.61 / Chapter 5.3 --- Steiner Triple System --- p.62 / Chapter 5.4 --- Social Golfer Problem --- p.63 / Chapter 5.5 --- Discussions --- p.66 / Chapter 6 --- Related Work --- p.68 / Chapter 6.1 --- Other Consistency Notions in WCSPs --- p.68 / Chapter 6.1.1 --- Full Directional Arc Consistency --- p.68 / Chapter 6.1.2 --- Existential Directional Arc Consistency --- p.69 / Chapter 6.2 --- Classical CSPs with Set Variables --- p.70 / Chapter 6.2.1 --- Bounds Reasoning --- p.70 / Chapter 6.2.2 --- Cardinality Reasoning --- p.70 / Chapter 7 --- Concluding Remarks --- p.72 / Chapter 7.1 --- Contributions --- p.72 / Chapter 7.2 --- Future Work --- p.74 / List of Symbols --- p.76 / Bibliography --- p.79

Woo Hiu Chun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 91-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Weighted Constraint Satisfaction Problems --- p.3 / Chapter 1.3 --- Redundant Modeling --- p.4 / Chapter 1.4 --- Motivations and Goals --- p.5 / Chapter 1.5 --- Outline of the Thesis --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.8 / Chapter 2.1.1 --- Backtracking Tree Search --- p.9 / Chapter 2.1.2 --- Local Consistencies --- p.12 / Chapter 2.1.3 --- Local Consistencies in Backtracking Search --- p.17 / Chapter 2.1.4 --- Permutation CSPs --- p.19 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.20 / Chapter 2.2.1 --- Branch and Bound Search --- p.23 / Chapter 2.2.2 --- Local Consistencies --- p.26 / Chapter 2.2.3 --- Local Consistencies in Branch and Bound Search --- p.32 / Chapter 2.3 --- Redundant Modeling --- p.34 / Chapter 3 --- Generating Redundant WCSP Models --- p.37 / Chapter 3.1 --- Model Induction for CSPs --- p.38 / Chapter 3.1.1 --- Stated Constraints --- p.39 / Chapter 3.1.2 --- No-Double-Assignment Constraints --- p.39 / Chapter 3.1.3 --- At-Least-One-Assignment Constraints --- p.40 / Chapter 3.2 --- Generalized Model Induction for WCSPs --- p.43 / Chapter 4 --- Combining Mutually Redundant WCSPs --- p.47 / Chapter 4.1 --- Naive Approach --- p.47 / Chapter 4.2 --- Node Consistency Revisited --- p.51 / Chapter 4.2.1 --- Refining Node Consistency Definition --- p.52 / Chapter 4.2.2 --- Enforcing m-NC* c Algorithm --- p.55 / Chapter 4.3 --- Arc Consistency Revisited --- p.58 / Chapter 4.3.1 --- Refining Arc Consistency Definition --- p.60 / Chapter 4.3.2 --- Enforcing m-AC*c Algorithm --- p.62 / Chapter 5 --- Experiments --- p.67 / Chapter 5.1 --- Langford's Problem --- p.68 / Chapter 5.2 --- Latin Square Problem --- p.72 / Chapter 5.3 --- Discussion --- p.75 / Chapter 6 --- Related Work --- p.77 / Chapter 6.1 --- Soft Constraint Satisfaction Problems --- p.77 / Chapter 6.2 --- Other Local Consistencies in WCSPs --- p.79 / Chapter 6.2.1 --- Full Arc Consistency --- p.79 / Chapter 6.2.2 --- Pull Directional Arc Consistency --- p.81 / Chapter 6.2.3 --- Existential Directional Arc Consistency --- p.82 / Chapter 6.3 --- Redundant Modeling and Channeling Constraints --- p.83 / Chapter 7 --- Concluding Remarks --- p.85 / Chapter 7.1 --- Contributions --- p.85 / Chapter 7.2 --- Future Work --- p.87 / List of Symbols --- p.88 / Bibliography

Lam Yee Gordon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 109-117). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Motivations and Goals --- p.2 / Chapter 1.3 --- Outline of the Thesis --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- CSP --- p.5 / Chapter 2.2 --- Classes of Variable --- p.6 / Chapter 2.3 --- Solution of a CSP --- p.7 / Chapter 2.4 --- Constraint Solving Techniques --- p.8 / Chapter 2.4.1 --- Local Consistencies --- p.8 / Chapter 2.4.2 --- Constraint Tightness --- p.10 / Chapter 2.4.3 --- Tree Search --- p.10 / Chapter 2.5 --- Graph --- p.14 / Chapter 3 --- Common Channeling Constraints --- p.16 / Chapter 3.1 --- Models --- p.16 / Chapter 3.2 --- Channeling Constraints --- p.17 / Chapter 3.2.1 --- Int-Int Channeling Constraint (II) --- p.18 / Chapter 3.2.2 --- Set-Int Channeling Constraint (SI) --- p.21 / Chapter 3.2.3 --- Set-Set Channeling Constraint (SS) --- p.24 / Chapter 3.2.4 --- Int-Bool Channeling Constraint (IB) --- p.25 / Chapter 3.2.5 --- Set-Bool Channeling Constraint (SB) --- p.27 / Chapter 3.2.6 --- Discussions --- p.29 / Chapter 4 --- Realization in Existing Solvers --- p.31 / Chapter 4.1 --- Implementation by if-and-only-if constraint --- p.32 / Chapter 4.1.1 --- "Realization of iff in CHIP, ECLiPSe, and SICStus Prolog" --- p.32 / Chapter 4.1.2 --- Realization of iff in Oz and ILOG Solver --- p.32 / Chapter 4.2 --- Implementations by Element Constraint --- p.38 / Chapter 4.2.1 --- "Realization of ele in CHIP, ECLiPSe, and SICStus Prolog" --- p.40 / Chapter 4.2.2 --- Realization of ele in Oz and ILOG Solver --- p.40 / Chapter 4.3 --- Global Constraint Implementations --- p.41 / Chapter 4.3.1 --- "Realization of glo in CHIP, SICStus Prolog, and ILOG Solver" --- p.42 / Chapter 5 --- Consistency Levels --- p.43 / Chapter 5.1 --- Int-Int Channeling (II) --- p.44 / Chapter 5.2 --- Set-Int Channeling (SI) --- p.49 / Chapter 5.3 --- Set-Set Channeling Constraints (SS) --- p.53 / Chapter 5.4 --- Int-Bool Channeling (IB) --- p.55 / Chapter 5.5 --- Set-Bool Channeling (SB) --- p.57 / Chapter 5.6 --- Discussion --- p.59 / Chapter 6 --- Algorithms and Implementation --- p.61 / Chapter 6.1 --- Source of Inefficiency --- p.62 / Chapter 6.2 --- Generalized Element Constraint Propagators --- p.63 / Chapter 6.3 --- Global Channeling Constraint --- p.66 / Chapter 6.3.1 --- Generalization of Existing Global Channeling Constraints --- p.66 / Chapter 6.3.2 --- Maintaining GAC on Int-Int Channeling Constraint --- p.68 / Chapter 7 --- Experiments --- p.72 / Chapter 7.1 --- Int-Int Channeling Constraint --- p.73 / Chapter 7.1.1 --- Efficient AC implementations --- p.74 / Chapter 7.1.2 --- GAC Implementations --- p.75 / Chapter 7.2 --- Set-Int Channeling Constraint --- p.83 / Chapter 7.3 --- Set-Set Channeling Constraint --- p.89 / Chapter 7.4 --- Int-Bool Channeling Constraint --- p.89 / Chapter 7.5 --- Set-Bool Channeling Constraint --- p.91 / Chapter 7.6 --- Discussion --- p.93 / Chapter 8 --- Related Work --- p.101 / Chapter 8.1 --- Empirical Studies --- p.101 / Chapter 8.2 --- Theoretical Studies --- p.102 / Chapter 8.3 --- Applications --- p.103 / Chapter 8.4 --- Other Kinds of Channeling Constraints --- p.104 / Chapter 9 --- Concluding Remarks --- p.106 / Chapter 9.1 --- Contributions --- p.106 / Chapter 9.2 --- Future Work --- p.108 / Bibliography --- p.109

by Cheang Sin Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 137-140. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Lisp as an AI Programming Language --- p.1 / Chapter 1.2 --- Assisting List Processing with Hardware --- p.2 / Chapter 1.3 --- Simulation Study --- p.2 / Chapter 1.4 --- Implementation --- p.3 / Chapter 1.4.1 --- Hardware --- p.3 / Chapter 1.4.2 --- Software --- p.3 / Chapter 1.5 --- Performance --- p.4 / Chapter CHAPTER 2 --- LISP AND EXISTING LISP MACHINES --- p.5 / Chapter 2.1 --- Lisp and its Internal Structure --- p.5 / Chapter 2.1.1 --- The List Structure in Lisp --- p.5 / Chapter 2.1.2 --- Data Types in Lisp --- p.7 / Chapter 2.1.3 --- Lisp Functions --- p.8 / Chapter 2.1.4 --- Storage Management of Lisp --- p.9 / Chapter 2.2 --- Existing Lisp Machines --- p.11 / Chapter 2.2.1 --- Types of AI Architecture --- p.11 / Language-Based architecture --- p.11 / Knowledge-Based architecture --- p.12 / Semantic networks --- p.12 / Chapter 2.2.2 --- Lisp Machines --- p.12 / Solving problems of Lisp --- p.13 / Chapter 2.2.3 --- Classes of Lisp Machines --- p.14 / Two M Lisp machine examples --- p.15 / A class P machine example --- p.17 / A class S machine example --- p.17 / The best class for Lisp --- p.19 / Chapter 2.3 --- Execution Time Analysis of a Lisp System --- p.20 / Chapter 2.3.1 --- CPU Time Statistics --- p.20 / Chapter 2.3.2 --- Statistics Analysis --- p.24 / Chapter CHAPTER 3 --- OVERALL ARCHITECTURE OF THE ASLP --- p.27 / Chapter 3.1 --- An Arithmetical & Symbolical List Processor --- p.27 / Chapter 3.2 --- Multiple Memory Modules --- p.30 / Chapter 3.3 --- Large Number of Registers --- p.31 / Chapter 3.4 --- Multiple Buses --- p.34 / Chapter 3.5 --- Special Function Units --- p.35 / Chapter CHAPTER 4 --- PARALLELISM IN THE ASLP --- p.36 / Chapter 4.1 --- Parallel Data Movement --- p.36 / Chapter 4.2 --- Wide Memory Modules --- p.37 / Chapter 4.3 --- Parallel Memory Access --- p.39 / Chapter 4.3.1 --- Parallelism and Pipelining --- p.39 / Chapter 4.4 --- Pipelined Micro-Instructions --- p.40 / Chapter 4.4.1 --- Memory access pipelining --- p.41 / Chapter 4.5 --- Performance Estimation --- p.44 / Chapter 4.6 --- Parallel Execution with the Host Computer --- p.45 / Chapter CHAPTER 5 --- SIMULATION STUDY OF THE ASLP --- p.47 / Chapter 5.1 --- Why Simulation is needed for the ASLP? --- p.47 / Chapter 5.2 --- The Structure of the HOCB Simulator --- p.48 / Chapter 5.2.1 --- Activity-Oriented Simulation for the ASLP --- p.50 / Chapter 5.3 --- The Hardware Object Declaration Method --- p.50 / Chapter 5.4 --- A Register-Level Simulation of the ASLP --- p.53 / Chapter 5.4.1 --- A List Function Simulation --- p.54 / Chapter CHAPTER 6 --- DESIGN AND IMPLEMENTATION OF THE ASLP --- p.57 / Chapter 6.1 --- Hardware --- p.57 / Chapter 6.1.1 --- Microprogrammable Controller --- p.57 / The instruction cycle of the micro-controller --- p.59 / Chapter 6.1.2 --- Chip Selection and Allocation --- p.59 / Chapter 6.2 --- Software --- p.61 / Chapter 6.2.1 --- Instruction Passing --- p.61 / Chapter 6.2.2 --- Microprogram Development --- p.62 / Microprogram field definition --- p.64 / Micro-assembly language --- p.65 / Macro-instructions --- p.65 / Down-loading of Micro-Codes --- p.66 / Interfacing to C language --- p.66 / A Turbo C Function Library --- p.67 / Chapter CHAPTER 7 --- PERFORMANCE EVALUATION OF THE ASLP …… --- p.68 / Chapter 7.1 --- Micro-Functions in the ASLP --- p.68 / Chapter 7.2 --- Functions in the C Library --- p.71 / Chapter CHAPTER 8 --- FUNCTIONAL EVALUATION OF THE ASLP --- p.77 / Chapter 8.1 --- A Relational Database on the ASLP --- p.77 / Chapter 8.1.1 --- Data Representation --- p.77 / Chapter 8.1.2 --- Performance of the Database System --- p.79 / Chapter 8.2 --- Other Potential Applications --- p.80 / Chapter CHAPTER 9 --- FUTURE DEVELOPMENT OF THE ASLP --- p.81 / Chapter 9.1 --- An Expert System Shell on the ASLP --- p.81 / Chapter 9.1.1 --- Definition of Objects --- p.81 / Chapter 9.1.2 --- Knowledge Representation --- p.84 / Chapter 9.1.3 --- Knowledge Representation in the ASLP --- p.85 / Chapter 9.1.4 --- Overall Structure --- p.88 / Chapter 9.2 --- Reducing the Physical Size by Employing VLSIs --- p.89 / Chapter CHAPTER 10 --- CONCLUSION --- p.92 / Chapter APPENDIX A --- BLOCK DIAGRAM --- p.95 / Chapter APPENDIX B --- ASLP CIRCUIT DIAGRAMS --- p.97 / Chapter APPENDIX C --- ASLP PC-BOARD LAYOUTS --- p.114 / Chapter APPENDIX D --- MICRO-CONTROL SIGNAL ASSIGNMENT --- p.121 / Chapter APPENDIX E --- MICRO-FIELD DEFINITION --- p.124 / Chapter APPENDIX F --- MACRO DEFINITION --- p.133 / Chapter APPENDIX G --- REGISTER ASSIGNMENT --- p.134 / PUBLICATIONS --- p.136 / REFERENCES --- p.137

Wong, Wing-ming Eric. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 46-47. / ACKNOWLEDGMENTS / ABSTRACT / Chapter Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Advantages and Disadvantages --- p.1 / Chapter 1.2 --- Satellite System Engineering --- p.2 / Chapter 1.3 --- Channel Allocation Methods --- p.3 / Chapter 1.4 --- Outline of this Thesis --- p.5 / Chapter Chapter 2 --- DELAY BOUNDS --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- The Packet Satellite System --- p.7 / Chapter 2.3 --- The Idealized Protocol with Contention-Free Reservation --- p.8 / Chapter 2.4 --- Delay Lower Bound for Protocols with Contention-Free Reservation --- p.9 / Chapter 2.5 --- Delay Lower Bound for Protocols with Contention-Based Reservation --- p.14 / Chapter Chapter 3 --- IN SEARCH OF A MINIMUM DELAY PROTOCOL --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- The Packet Satellite System --- p.25 / Chapter 3.3 --- The Transmission Protocol --- p.26 / Chapter 3.4 --- Throughput Analysis --- p.27 / Chapter 3.5 --- Delay Analysis --- p.28 / Chapter 3.6 --- Minimization of DI --- p.31 / Chapter 3.7 --- Minimization of DII --- p.38 / Chapter 3.8 --- Numerical Examples --- p.38 / Chapter Chapter 4 --- CONCLUSIONS --- p.45 / REFERENCES --- p.46 / APPENDIX --- p.48