http://science.sciencemag.org/content/sci/201/4359/899.2.full.pdf

Phenomenology of perception.

物件導向軟體之架構(Object-Oriented Software Construction)探討

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

An Introduction to MultiAgent Systems.

Fault detection and isolation is a crucial and challenging task in the automatic control of large complex systems We propose a discrete-event system (DES) approach to the problem of failure diagnosis We introduce two related notions of diagnosability of DES's in the framework of formal languages and compare diagnosability with the related notions of observability and invertibility We present a systematic procedure for detection and isolation of failure events using diagnosers and provide necessary and sufficient conditions for a language to be diagnosable The diagnoser performs diagnostics using online observations of the system behavior; it is also used to state and verify off-line the necessary and sufficient conditions for diagnosability These conditions are stated on the diagnoser or variations thereof The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES's; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES's at a higher level of abstraction >

/pdf/diagnosability-of-discrete-event-systems-3vl31an6g2.pdf

Diagnosability of discrete-event systems

Chapter 1: Introduction to Model-based Diagnosis Introduction Model-based reasoning: Troubleshooting by Randall Davis and Walter Hamscher Chapter 2: Logical Foundations Introduction A theory of diagnosis from first principles by Raymond Reiter A correction to the algorithm in Reiter's theory of diagnosis by Russell Greiner, Barbara Smith, and Raymond Reiter The alibi principle Normality and faults in logic-based diagnosis A spectrum of logical definitions of model-based diagnosis On tests for hypothetical reasoning by Sheila McIlraith and Raymond Reiter Chapter 3: The General Diagnostic Engine Introduction Diagnosis multiple faults by Johan de Kleer and Brian C. Williams Using crude probability estimates to guide diagnosis by Johan de Kleer Diagnosis with behavioral modes by Johan de Kleer and Brian C. Williams Focusing on probable diagnoses One step lookahead is pretty good by Johan de Kleer, Olivier Raiman, and Mark Shirley MOMO - Model-based diagnosis for everybody Chapter 4: Fault Models Introduction Physical negation: Integrating fault models into the general diagnostic engine by Peter Struss and Oskar Dressler Physical impossibility instead of fault models by Gerhard Friedrich, Georg Gottlob, and Wolfgang Nejdl Diagnosing multiple failures using knowledge of component states by Lester Holtzblatt Characterizing non-intermittent faults by Olivier Raiman, Johan de Kleer, Vijay Saraswat, and Mark Shirley Chapter 5: Analog Systems Introduction Model-based diagnosis in SOPHIE III by Johan de Kleer and John Seely Brown Controlling inequality reasoning in a TMS-based analog diagnosis system by David Jerald Goldstone ACP: Reason maintenance and inference control for constraint propogation over intervals by Walter Hamscher Troubleshooting: When modeling is the trouble by Philippe Dague, Olivier Raiman, and Philippe Deves Chapter 6: Diagnosing Devices with State Introduction Analog systems diagnosis by Philippe Dague, P. Deves, P. Luciani, and P. Taillibert When oscillators stop oscillating Model-based monitoring of dynamic systems by Daniel Dvorak and Benjamin Kuipers Dynamic across-time measurement interpretation by Dennis DeCoste Qualitative modeling and fault diagnosis of dynamic processes by MIDAS by O.O. Oyeleye, F.E. Finch, and M.A. Kramer Diagnosing circuits with state: An inherently underconstrained problem by Walter Hamscher and Randall Davis Modeling digital circuits for troubleshooting by Walter Hamscher SIDIA: Extending prediction based diagnosis to dynamic models by Thomas Guckenbiehl and Gisela Schafer-Richter Qualitative reasoning with deep-level mechanism models for diagnoses of mechanism failures by Jeff Yung-Coa Pan Chapter 7: Hierarchies Introduction The use of design descriptions in automated diagnosis by Michael Genesereth Generating distinguishing tests based on hierarchical models and symptom information by Mark Shirley and Randall Davis SATURN: An automatic test generation system for digital circuits by Narinder Singh Hierarchical model-based diagnosis by Igor Mozetic Chapter 8: Relaxation of Diagnostic Assumptions Introduction Diagnostic reasoning based on structure and behavior by Randall Davis Diagnosis as a process What's in SD? Towards a theory of modeling for diagnosis by Peter Struss Chapter 9: Probabilistic Approaches Introduction Distributed diagnosis of systems with multiple faults by Hector Geffner and Judea Pearl An improved constraint-propogation algorithm for diagnosis by Hector Geffner and Judea Pearl Representing diagnostic knowledge for probabilistic Horn abduction by David Poole Annotated Bibliography Credits Author Index Subject Index

Readings in Model-Based Diagnosis

n a The aim of this paper is at analyzing from various points of view the relationships betwee bduction and deduction. In particular, we consider a meta-level definition of abduction in l d terms of deduction, similar to various definitions proposed in the literature, and an object-leve efinition in which abductive conclusions are expressed as a logical consequence of the obser. T vations and of a simple transformation of the domain theory based on predicate completion he equivalence between the two definitions is proved for domain theories of considerable s expressive power. The object-level characterization we propose uses very simple forms of rea oning and the equivalence result allows us to make explicit some of the assumptions underly-

On the Relationship Between Abduction and Deduction

In this paper, we analyze the logical definitions of model-based diagnosis recently presented in the literature, and we propose a unified framework (based on the integration of abductive and consistency-based reasoning) in which most of such definitions can be captured. This allows us to single out the existence of a spectrum of alternatives in the logical definition of diagnosis. A lot of attention in the paper is devoted to analyzing the differences among the definitions in the spectrum. In particular, we show that the definitions can be compared on the basis of their restrictive-ness and we relate such a restrictiveness with the completeness of the model of the system to be diagnosed.



Dans cet article, les auteurs analysent les definitions logiques de diagnostics bases sur un modele dont il a ete question recemment dans certains ouvrages. Ils proposent un cadre unifie (base sur l'integration du raisonnement abductif et du raisonnement base sur la consistance) a l'interieur duquel la plupart de ces definitions peuvent ětre regroupees. Cette particularite permet de mettre en lumiere l'existence d'un spectre d'alternatives dans la definition logique du diagnostic. Cet article accorde une attention toute particuliere a l'analyse des differences entre les definitions du spectre. En outre, les auteurs demontrent que les definitions peuvent ětre compareees en fonction de leur caractere restrictif, qui est ensuite mis en relation avec la completude du modele du systeme faisant l'objet d'un diagnostic.

A spectrum of logical definitions of model-based diagnosis

One of the problems of the recent approaches to problem solving based on deep knowledge is the lack of a formal treatment of incomplete knowledge. However, dealing with incomplete models is fundamental to many real-world domains. In this paper we propose a formal theory of causal diagnostic reasoning, dealing with different forms of incompleteness both in the general causal knowledge (missing or abstracted knowledge) and in the data describing a specific case under examination. Different forms of nonmonotonic reasoning (hypothetical and circumscriptive reasoning) are used in order to draw and confirm conclusions from incomplete knowledge. Multiple fault solutions are treated in a natural way and parsimony criteria arc used to rank alternative solutions.

/pdf/a-theory-of-diagnosis-for-incomplete-causal-models-3shivtm38e.pdf

Luca Console

Papers

Readings in Model-Based Diagnosis

On the Relationship Between Abduction and Deduction

A spectrum of logical definitions of model-based diagnosis

A theory of diagnosis for incomplete causal models

Model-Based Diagnosis Meets Error Diagnosis in Logic Programs.