TibFit and Chameleon

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Table of contents

I introduction and state of the art 
1 introduction 
1.1 Raison d’Être
1.2 Weaving a Net
1.3 Separating Reasoning from Acting
1.4 Definitions and Working Hypotheses
1.5 Thesis Structure
2 state of the art 
2.1 The Tolerance of Unforeseen Faults
2.1.1 The Observer
2.1.2 Anomaly detection
2.1.3 TibFit and Chameleon
2.1.4 Mission Data System
2.1.5 Recovery Blocks
2.1.6 A Case for Automatic Exception Handling
2.1.7 Defensive Programming
2.1.8 Design by Contract and Executable Specifications
2.1.9 Let It Crash
2.1.10 The Mercury Programming Language
2.2 Fault Tolerance with and for Agents
2.2.1 A Perspective on Exceptions in Multi-Agent Systems .
2.2.2 Communication Standards for Agent Fault Tolerance
2.2.3 Replication
2.2.4 Detecting Errors Through Agent Disagreement
2.2.5 The Sentinels
2.2.6 Norms. Trust and Reputation
2.2.7 Agent Autonomy for Robust Agents
2.3 Goal-Driven Agents
2.3.1 Describing Goals
2.3.2 The Goal Life-Cycle
2.3.3 Reasoning on Agent Goals
2.3.4 The Goal-Plan Tree
2.4 ALMA: An Agent Language for Dependable Agents
2.4.1 ALMA Motivations
2.4.2 Problem Solvers and Truth Maintenance Systems
2.4.3 Parenthesis on Model Based Diagnosis
2.4.4 The Programming Language
2.5 Conclusion
II contribution to the fault tolerance 
3 a safety net approach to fault tolerance 
3.1 Expecting the Unexpected: Error Detection
3.1.1 Exception-Based Detection
3.1.2 Objective-Based Detection
3.2 Avoiding Further Error Propagation: Confinement
3.3 System Recovery
3.3.1 Dependency Handling
3.3.2 Reparation
3.3.3 Reconfiguration
3.4 The Programmer’s Guide for a Safety Net
3.4.1 Language Requirements
3.4.2 Platform Requirements
3.4.3 Design Requirements
3.5 Discussion
4 an instantiation of the safety net 
4.1 The Base Language
4.2 Extending ALMA for The Safety Net Approach
4.2.1 The unexpected Keyword
4.2.2 Goals
4.2.3 Plans
4.2.4 The ALMA+ Model and Language
4.3 The Three Fault Tolerance Phases in ALMA+
4.3.1 Detection
4.3.2 Confinement
4.3.3 Recovery
4.4 Extending the Platform
4.4.1 Language Extension Support
4.4.2 Safety Net Support
4.4.3 Agent Architecture
4.5 Discussion
5 experimenting 
5.1 The CNP+ Scenario
5.2 Modelling the Agents
5.2.1 The Initiator Agent
5.2.2 The Main Contractor Agent
5.2.3 The Worker Agent
5.2.4 Giving Unanticipated Errors a Thought
5.3 Adding The Safety Net Mechanisms
5.4 The Safety Net at Work
5.4.1 Study by Type of Confinement
5.4.2 Study by Location of Error Occurrence in the Agent Code .
5.4.3 Other Error Situations
5.5 Discussion
III contribution to goal programming 
6 the goal-plan separation 
6.1 Goal-Plan Trees to Goal-Plan Separation
6.2 The Goal Reasoning Level
6.3 Mars Rover Scenario
7 gps method implementation 
7.1 Examples of Possible Models for the Goal Reasoning Level
7.1.1 Reasoning through Rules
7.1.2 Reasoning Using a Planner
7.2 Reasoning through a Goal Plan
7.3 Reasoning through Multiple Goal Plans
7.4 Execution
7.5 Key Literature Aspects
8 experimenting with gps 
8.1 An Application for Maritime Surveillance
8.1.1 In the Lead Role: The Aircraft Agent
8.1.2 GPS for Modelling the Aircraft Agent
8.1.3 Discussion
8.2 The Deployment of Ambient Intelligence Applications
8.2.1 Scenario
8.2.2 Multi-agent Modelling
8.2.3 Design and Implementation
8.2.4 Discussion
8.3 Overview
IV conclusions 
9 conclusions 
9.1 The Safety Net Approach
9.2 The Goal-Plan Separation Approach
9.3 Putting It All Back Together
V appendix 
a controlling goal execution 
b models of the cnp+ agents 
b.1 The Initiator Agent
b.1.1 Agent Goals
b.1.2 Agent Plans
b.2 The Main Contractor Agent
b.2.1 Agent Goals
b.2.2 Agent Plans
b.3 The Worker Agent
b.3.1 Agent Goals
b.3.2 Agent Plans
c error response by location of occurrence in cnp+ 
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