Chan Wai-Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 119-121). / Abstracts in English and Chinese. / Chapter 1 --- Introduction / Chapter 1.1 --- Double Auction --- p.1 / Chapter 1.1.1 --- One-to-One Negotiation Model --- p.1 / Chapter 1.2 --- Sequential Equilibrium of One-to-One Negotiation Model --- p.3 / Chapter 1.3 --- Result --- p.4 / Chapter 2 --- Modeling the One-to-One Negotiation / Chapter 2.1 --- Nature of One-to-One Negotiation --- p.6 / Chapter 2.2 --- Basic Assumptions in the One-to-One Negotiation Model --- p.6 / Chapter 2.2.1 --- Rationality Assumption --- p.7 / Chapter 2.2.2 --- Private Valuation Assumption --- p.8 / Chapter 2.2.3 --- Subjective Belief on Opponent's Private Valuation --- p.9 / Chapter 2.3 --- Rules of the One-to-One Negotiation Model --- p.9 / Chapter 2.4 --- Payoff of Players in One-to-One Negotiation Model --- p.11 / Chapter 2.5 --- Possible Action Space of Players in One-to-One Negotiation Model --- p.12 / Chapter 2.5.1 --- Possible Action Space of the Seller Agent --- p.12 / Chapter 2.5.2 --- Possible Action Space of the Buyer Agent --- p.13 / Chapter 2.6 --- Random Vector Model for the One-to-One Negotiation Model --- p.14 / Chapter 2.6.1 --- Problems of Sequential Expectation Model --- p.14 / Chapter 2.6.2 --- Random Vector Model of the One-to-One Negotiation Game --- p.15 / Chapter 2.6.3 --- Existence of Objective Belief in Random Vector Model --- p.17 / Chapter 2.7 --- Information Set in a One-to-One Negotiation Model --- p.18 / Chapter 2.7.1 --- Game Tree of the One-to-One Negotiation Model --- p.19 / Chapter 2.7.2 --- Information Set in One-to-One Negotiation Model --- p.23 / Chapter 2.7.2.1 --- Seller's Information Set in One-to-One Negotiation Model --- p.24 / Chapter 2.7.2.2 --- Buyer's Information Set in One-to-One Negotiation Model --- p.26 / Chapter 2.8 --- Strategies of Players in One-to-One Negotiation Model --- p.28 / Chapter 2.8.1 --- Pure Strategies in the One-to-One Negotiation Model --- p.29 / Chapter 2.8.1.1 --- Payoff Function --- p.30 / Chapter 2.8.2 --- Mixed Strategies in One-to-One Negotiation Model --- p.30 / Chapter 2.8.3 --- Behavior Strategies --- p.32 / Chapter 2.9 --- Realization Probabilities in One-to-One Negotiation Model --- p.33 / Chapter 2.9.1 --- Realization Probabilities for Buyer's Information Sets and Nodes --- p.34 / Chapter 2.9.2 --- Realization Probabilities for Seller's Information Sets and Nodes --- p.35 / Chapter 2.10 --- Beliefs of Players in One-to-One Negotiation Model --- p.36 / Chapter 2.10.1 --- Seller's Belief in One-to-One Negotiation Model --- p.37 / Chapter 2.10.2 --- Buyer's Belief in One-to-One Negotiation Model --- p.38 / Chapter 2.11 --- Sequential Equilibrium of One-to-One Negotiation Model --- p.40 / Chapter 2.12 --- Applying GT for Solving Negotiation Problem --- p.41 / Chapter 3 --- Two stage One-to-One Negotiation Model / Chapter 3.1 --- Notation Used --- p.44 / Chapter 3.1.1 --- Physical Interpretation of Seller's and Buyer's Valuation --- p.44 / Chapter 3.1.2 --- Discount Factor in One-to-One Negotiation --- p.45 / Chapter 3.2 --- Formulation of Two Stage Negotiation --- p.46 / Chapter 3.2.1 --- First Stage of Negotiation Process --- p.47 / Chapter 3.2.2 --- Second Stage of Negotiation Process --- p.47 / Chapter 3.3 --- Buyer Strategies in Two Stage Negotiation --- p.49 / Chapter 3.3.1 --- Property of Equilibrium Strategy in Second Round of Negotiation --- p.49 / Chapter 3.3.2 --- Property of Equilibrium Strategy in First Round of Negotiation --- p.50 / Chapter 3.4 --- Strategic Combination of Seller Agent --- p.52 / Chapter 3.4.1 --- Three Major Types of Strategic Combination --- p.52 / Chapter 3.5 --- Properties of Type A Restricted Equilibrium Solution --- p.54 / Chapter 3.6 --- Properties of Type C Restricted Equilibrium Solution --- p.58 / Chapter 3.7 --- Properties of Type B Restricted Equilibrium Solution --- p.60 / Chapter 3.7.1 --- Relations between α1 and α2 in Type B Combinations --- p.61 / Chapter 3.7.2 --- Behavior Strategy of Buyer Agent --- p.63 / Chapter 3.7.3 --- Seller Agent's Belief in Second Round of Negotiation --- p.64 / Chapter 3.7.4 --- Seller's Payoff Function in Second Round of Negotiation --- p.65 / Chapter 3.7.5 --- Seller's Payoff Function in First Round of Negotiation --- p.67 / Chapter 3.8 --- Best Response of Seller Agent to Buyer Agent's Optimal Strategies when cb Uniformly Distributed --- p.68 / Chapter 3.8.1 --- Solutions of Type A Restricted Equilibrium Solution --- p.69 / Chapter 3.8.2 --- Solutions of Type C Restricted Equilibrium Solution --- p.71 / Chapter 3.8.3 --- Type B Restricted Equilibrium Solution of Seller Agent --- p.72 / Chapter 3.8.3.1 --- Seller's Second Round Payoff Function when cb Uniformly Distributed --- p.73 / Chapter 3.8.3.2 --- Monotonicity of Seller's Second Round Payoff Function --- p.75 / Chapter 3.8.3.3 --- Second Offer Prescribed by Equilibrium Strategy when l≥h+cs --- p.83 / Chapter 3.8.3.4 --- Second Offer Prescribed by Equilibrium Strategy when l<h+cs --- p.88 / Chapter 3.8.3.5 --- Optimization of Payoff in First Round Negotiation --- p.94 / Chapter 3.8.3.5.1 --- Type B Restricted Equilibrium Solution when l≥h+cs --- p.96 / Chapter 3.8.3.5.2 --- Type B Restricted Equilibrium Solution when l<h+cs --- p.99 / Chapter 3.9 --- Numerical Example --- p.111 / Chapter 3.9.1 --- Example 1: Type A Combination --- p.111 / Chapter 3.9.2 --- Example 2: Type B Combination --- p.113 / Chapter 3.9.3 --- Example 3: Type C Combination --- p.114 / Chapter 4 --- Conclusion and Future Works / Chapter 4.1 --- Summary of Strategies --- p.114 / Chapter 4.2 --- Future Work --- p.118 / Bibliography --- p.119

???Agent-based systems are one of the most vibrant and important areas of research and development to have emerged in information technology in the 1990s??? (Luck et al. 2003). The use of agents as a metaphor for designing and constructing software systems represents an innovative movement in the field of software engineering: ???Agent- Oriented Software Engineering (AOSE)??? (Lind 2000; Luck et al. 2003). This research contributes to the evolution of AOSE by proposing a comprehensive ontology-based methodology for the analysis and design of Multi-Agent Systems (MAS). The methodology is named MOBMAS, which stands for ???Methodology for Ontology-Based MASs???. A major improvement of MOBMAS over the existing agentoriented MAS development methodologies is its explicit and extensive support for ontology-based MAS development. Ontologies have been widely acknowledged for their significant benefits to interoperability, reusability, MAS development activities (such as system analysis and agent knowledge modelling) and MAS operation (such as agent communication and reasoning). Recognising these desirable ontology???s benefits, MOBMAS endeavours to identify and implement the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In so doing, MOBMAS has exploited ontologies to enhance its MAS development process and MAS development product with various strengths. These strengths include those ontology???s benefits listed above, and those additional benefits uncovered by MOBMAS, e.g. support for verification and validation, extendibility, maintainability and reliability. Compared to the numerous existing agent-oriented methodologies, MOBMAS is the first that explicitly and extensively investigates the diverse potential advantages of ontologies in MAS development, and which is able to implement these potential advantages via an ontology-based MAS development process and a set of ontology-based MAS model definitions. Another major contribution of MOBMAS to the field of AOSE is its ability to address all key concerns of MAS development in one methodological framework. The methodology provides support for a comprehensive list of methodological requirements, which are important to agent-oriented analysis and design, but which may not be wellsupported by the current methodologies. These methodological requirements were identified and validated by this research from three sources: the existing agent-oriented methodologies, the existing evaluation frameworks for agent-oriented methodologies and conventional system development methodologies, and a survey of practitioners and researchers in the field of AOSE. MOBMAS supports the identified methodological requirements by combining the strengths of the existing agent-oriented methodologies (i.e. by reusing and enhancing the various strong techniques and model definitions of the existing methodologies where appropriate), and by proposing new techniques and model definitions where necessary. The process of developing MOBMAS consisted of three sequential research activities. The first activity identified and validated a list of methodological requirements for an Agent Oriented Software Engineering methodology as mentioned above. The second research activity developed MOBMAS by specifying a development process, a set of techniques and a set of model definitions for supporting the identified methodological requirements. The final research activity evaluated and refined MOBMAS by collecting expert reviews on the methodology, using the methodology on an application and conducting a feature analysis of the methodology.

Providing a logical characterisation of rational agent reasoning has been a long standing challenge in artificial intelligence (AI) research. It is a challenge that is not only of interest for the construction of AI agents, but is of equal importance in the modelling of agent behaviour. The goal of this thesis is to contribute to the formalisation of agent reasoning by showing that the computational limitations of agents is a vital component of modelling rational behaviour. To achieve this aim, both motivational and formal aspects of resource-bounded agents are examined. It is a central argument of this thesis that accounting for computational limitations is critical to the success of agent reasoning, yet has received only limited attention from the broader research community. Consequently, an important contribution of this thesis is in its advancing of motivational arguments in support of the need to account for computational limitations in agent reasoning research. As a natural progression from the motivational arguments, the majority of this thesis is devoted to an examination of propositional approximate logics. These logics represent a step towards the development of resource-bounded agents, but are also applicable to other areas of automated reasoning. This thesis makes a number of contributions in mapping the space of approximate logics. In particular, it draws a connection between approximate logics and knowledge compilation, by developing an approximate knowledge compilation method based on Cadoli and Schaerf??s S-3 family of approximate logics. This method allows for the incremental compilation of a knowledge base, thus reducing the need for a costly recompilation process. Furthermore, each approximate compilation has well-defined logical properties due to its correspondence to a particular S-3 logic. Important contributions are also made in the examination of approximate logics for clausal reasoning. Clausal reasoning is of particular interest due to the efficiency of modern clausal satisfiability solvers and the related research into problem hardness. In particular, Finger's Logics of Limited Bivalence are shown to be applicable to clausal reasoning. This is subsequently shown to logically characterise the behaviour of the well-known DPLL algorithm for determining boolean satisfiability, when subjected to restricted branching.

???Agent-based systems are one of the most vibrant and important areas of research and development to have emerged in information technology in the 1990s??? (Luck et al. 2003). The use of agents as a metaphor for designing and constructing software systems represents an innovative movement in the field of software engineering: ???Agent- Oriented Software Engineering (AOSE)??? (Lind 2000; Luck et al. 2003). This research contributes to the evolution of AOSE by proposing a comprehensive ontology-based methodology for the analysis and design of Multi-Agent Systems (MAS). The methodology is named MOBMAS, which stands for ???Methodology for Ontology-Based MASs???. A major improvement of MOBMAS over the existing agentoriented MAS development methodologies is its explicit and extensive support for ontology-based MAS development. Ontologies have been widely acknowledged for their significant benefits to interoperability, reusability, MAS development activities (such as system analysis and agent knowledge modelling) and MAS operation (such as agent communication and reasoning). Recognising these desirable ontology???s benefits, MOBMAS endeavours to identify and implement the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In so doing, MOBMAS has exploited ontologies to enhance its MAS development process and MAS development product with various strengths. These strengths include those ontology???s benefits listed above, and those additional benefits uncovered by MOBMAS, e.g. support for verification and validation, extendibility, maintainability and reliability. Compared to the numerous existing agent-oriented methodologies, MOBMAS is the first that explicitly and extensively investigates the diverse potential advantages of ontologies in MAS development, and which is able to implement these potential advantages via an ontology-based MAS development process and a set of ontology-based MAS model definitions. Another major contribution of MOBMAS to the field of AOSE is its ability to address all key concerns of MAS development in one methodological framework. The methodology provides support for a comprehensive list of methodological requirements, which are important to agent-oriented analysis and design, but which may not be wellsupported by the current methodologies. These methodological requirements were identified and validated by this research from three sources: the existing agent-oriented methodologies, the existing evaluation frameworks for agent-oriented methodologies and conventional system development methodologies, and a survey of practitioners and researchers in the field of AOSE. MOBMAS supports the identified methodological requirements by combining the strengths of the existing agent-oriented methodologies (i.e. by reusing and enhancing the various strong techniques and model definitions of the existing methodologies where appropriate), and by proposing new techniques and model definitions where necessary. The process of developing MOBMAS consisted of three sequential research activities. The first activity identified and validated a list of methodological requirements for an Agent Oriented Software Engineering methodology as mentioned above. The second research activity developed MOBMAS by specifying a development process, a set of techniques and a set of model definitions for supporting the identified methodological requirements. The final research activity evaluated and refined MOBMAS by collecting expert reviews on the methodology, using the methodology on an application and conducting a feature analysis of the methodology.

???Agent-based systems are one of the most vibrant and important areas of research and development to have emerged in information technology in the 1990s??? (Luck et al. 2003). The use of agents as a metaphor for designing and constructing software systems represents an innovative movement in the field of software engineering: ???Agent- Oriented Software Engineering (AOSE)??? (Lind 2000; Luck et al. 2003). This research contributes to the evolution of AOSE by proposing a comprehensive ontology-based methodology for the analysis and design of Multi-Agent Systems (MAS). The methodology is named MOBMAS, which stands for ???Methodology for Ontology-Based MASs???. A major improvement of MOBMAS over the existing agentoriented MAS development methodologies is its explicit and extensive support for ontology-based MAS development. Ontologies have been widely acknowledged for their significant benefits to interoperability, reusability, MAS development activities (such as system analysis and agent knowledge modelling) and MAS operation (such as agent communication and reasoning). Recognising these desirable ontology???s benefits, MOBMAS endeavours to identify and implement the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In so doing, MOBMAS has exploited ontologies to enhance its MAS development process and MAS development product with various strengths. These strengths include those ontology???s benefits listed above, and those additional benefits uncovered by MOBMAS, e.g. support for verification and validation, extendibility, maintainability and reliability. Compared to the numerous existing agent-oriented methodologies, MOBMAS is the first that explicitly and extensively investigates the diverse potential advantages of ontologies in MAS development, and which is able to implement these potential advantages via an ontology-based MAS development process and a set of ontology-based MAS model definitions. Another major contribution of MOBMAS to the field of AOSE is its ability to address all key concerns of MAS development in one methodological framework. The methodology provides support for a comprehensive list of methodological requirements, which are important to agent-oriented analysis and design, but which may not be wellsupported by the current methodologies. These methodological requirements were identified and validated by this research from three sources: the existing agent-oriented methodologies, the existing evaluation frameworks for agent-oriented methodologies and conventional system development methodologies, and a survey of practitioners and researchers in the field of AOSE. MOBMAS supports the identified methodological requirements by combining the strengths of the existing agent-oriented methodologies (i.e. by reusing and enhancing the various strong techniques and model definitions of the existing methodologies where appropriate), and by proposing new techniques and model definitions where necessary. The process of developing MOBMAS consisted of three sequential research activities. The first activity identified and validated a list of methodological requirements for an Agent Oriented Software Engineering methodology as mentioned above. The second research activity developed MOBMAS by specifying a development process, a set of techniques and a set of model definitions for supporting the identified methodological requirements. The final research activity evaluated and refined MOBMAS by collecting expert reviews on the methodology, using the methodology on an application and conducting a feature analysis of the methodology.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisors: Sandra M. Carberry and Keith S. Decker, Dept. of Computer & Information Sciences. Includes bibliographical references.

A Multiagent Framework for a Diagnostic and Prognostic System Irtaza Barlas 124 Pages Directed By: Dr. George Vactsevanos The shortcomings of the current diagnostic and prognostic systems stem from the limitations of their frameworks. The framework is typically designed on the passive, open loop, static, and isolated notions of diagnostics, in that the framework does not observe its diagnostic results (open-looped), hence can not improve its performance (static). Its passivity is attributed to the fact that an external event triggers the diagnostic or prognostic action. There is also no effort in place to team-up the diagnostic systems for a collective learning, hence the implementation is isolated. In this research we extend the current approaches of the design and implementation of diagnostic and prognostic systems by presenting a framework based upon Multiagent systems. This research created novel architectures by providing such unique features to the framework, as learning, reasoning, and coordination. As the primary focus of the research the concept of Case-Based Reasoning was exploited to reason in the temporal domain to generate better prognosis, and improve the accuracy of detection as well as prediction. It was shown that the dynamic behavior of the intelligent agent helps it to learn over time, resulting in improved performance. An analysis is presented to show that a coordinated effort to diagnose also makes sense in uncertain situations when there are certain number of systems attempting to communicate certain number of failures, since there can be high probability of finding a shareable experience.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 297-309). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--University of Florida, 2000. / Title from first page of PDF file. Document formatted into pages; contains viii, 81 p.; also contains graphics. Vita. Includes bibliographical references (p. 77-80).

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2004. / Thesis advisor(s): Alex Bordetsky, Gurminder Singh. Includes bibliographical references (p. 49). Also available online.