| Future Communication Networks Using Software Agents | p. 1 |
| Introduction | p. 1 |
| 1 Some Agent Related Definitions | p. 3 |
| Rationale for Multiple Software Agents | p. 6 |
| Agent Architectures | p. 8 |
| Open Systems and Heterogeneous Multi-agent Systems | p. 10 |
| Co-ordination in Multi-Agent Systems | p. 13 |
| Software Agent Communication | p. 14 |
| Mobile Software Agents | p. 17 |
| Trends in Agent-Based Computing | p. 20 |
| Communications Systems - Integration of Paradigms | p. 23 |
| Telecommunications Considerations | p. 23 |
| The Internet Architecture | p. 24 |
| General Network Complexity | p. 25 |
| Users and the Need for Mobility | p. 26 |
| The Merging of Traditional Telephony and Computing Paradigms | p. 27 |
| Centralized or Distributed Management | p. 28 |
| Software Agent Based Network Management | p. 29 |
| Service Management | p. 30 |
| Current Telecommunications Management Platforms | p. 31 |
| Agents and Economic Theory for Telecommunications Management | p. 36 |
| Guessing the Future of Telecommunications | p. 39 |
| Challenges of Software Agents in Telecommunications | p. 40 |
| Review of the Subsequent Chapters | p. 41 |
| Summary | p. 45 |
| Agents of Change in Future Communication Systems | p. 58 |
| Introduction | p. 58 |
| Trends, Lessons Learned and some Viewpoints on the Future of Communications Systems | p. 59 |
| Distributed Software: the Quintessential 'Glue' of Future Communication Systems | p. 63 |
| Agents in Future Communication Systems: A General Case | p. 64 |
| Agents Research at BT Labs and Future Communications Systems | p. 68 |
| Collaborative Agents | p. 69 |
| Interface Agents/Information Agents | p. 74 |
| Mobile Agents | p. 80 |
| Reactive & Hybrid Agents | p. 80 |
| Heterogeneous Agent Systems | p. 81 |
| What Society Should be Doing to Prepare for Emergence of FCS | p. 82 |
| Conclusion | p. 83 |
| A Multi-Agent System Architecture for Scalable Management of High Performance Networks: Applying Arms Length Autonomy | p. 86 |
| Introduction | p. 86 |
| Network Management Challenges Posed by ATM | p. 87 |
| Intelligent Agents | p. 88 |
| Distributed Management Using Intelligent Agents | p. 88 |
| The Hybrid System | p. 91 |
| Hierarchy of Authorities | p. 91 |
| Design of an Authority | p. 92 |
| The Design of Agents | p. 93 |
| Co-ordinating the Behavior of the Agent System | p. 94 |
| ASL - Open Agent Architecture | p. 97 |
| Concerns about the Agent Approach | p. 101 |
| Deliberative vs. Reactive Agent Architectures in Hybrid | p. 101 |
| Tradeoffs in the Choice of Content Language | p. 102 |
| Benefits of the Agent Approach | p. 103 |
| Benefits of Using Agent Technology | p. 103 |
| Agent Oriented Middleware for Software Integration | p. 104 |
| Analysis of Hybrid Performance | p. 105 |
| Conclusions | p. 109 |
| Distributed Intelligent Agents: A Solution for the Management of Complex Telecommunications Services | p. 112 |
| Introduction | p. 112 |
| The Agent Platform | p. 113 |
| Which Agents? | p. 113 |
| The Architecture Design | p. 114 |
| Generic Agent Model | p. 117 |
| The Prototype | p. 118 |
| The Implementation of Agent Types | p. 118 |
| Graphical User Interfaces | p. 120 |
| Service Scenarios | p. 121 |
| Lessons | p. 124 |
| Decomposition, Distribution and Allocation of Tasks | p. 125 |
| Communication and Interaction Languages | p. 126 |
| Coherent Collective Behavior | p. 126 |
| Inter-agent Negotiation | p. 127 |
| Future work | p. 128 |
| Organization and Coordination for On-Line Routingin Communications Networks | p. 130 |
| Introduction | p. 130 |
| A routing problem | p. 131 |
| Generic Problem Definition | p. 132 |
| How Difficult is it? | p. 132 |
| The Need for Agents | p. 133 |
| Applying current work in Distributed Artificial Intelligence | p. 135 |
| 1 The Network Routing Domain | p. 136 |
| Current Agent Based Approaches to Routing Problems | p. 140 |
| Non Agent Based Approaches to the Routing | p. 142 |
| Adaptive Control Structures | p. 142 |
| Blocking Islands and Space Partitioning | p. 143 |
| How Agents Run Blocking Islands | p. 146 |
| Description and Discussion | p. 152 |
| Conclusions | p. 154 |
| Multi-Agent Interactions for a Network Management System(Tele-Macs Approach) | p. 160 |
| Introduction | p. 160 |
| ATM Network Management - The Problems | p. 162 |
| Virtual Path Connection Management | p. 162 |
| Why Adopt Agent Technology? | p. 163 |
| Building Modularized Software Systems | p. 164 |
| Distributed Multi-Agent Communications Control | p. 166 |
| Distributed Multi-layered Agent Control Architecture (Tele-MACS) | p. 167 |
| Functionality of Each Agent | p. 167 |
| Tele-MACS Approach for Network Management | p. 168 |
| Suppressed Views of the World | p. 169 |
| Application of Tele-MACS to a Network Management Problem | p. 169 |
| Control of the Bandwidth Resource Configuration in ATM Networks | p. 170 |
| Network Management Multi-Agent System Implementation | p. 172 |
| Multi-Agent Model (IMPACT System) | p. 174 |
| Physical Location of the Principle Agents | p. 175 |
| Multi-threading and Agent Implementation | p. 176 |
| Agent Communications | p. 177 |
| Multi-Agent Interactions | p. 178 |
| Agent Software System | p. 178 |
| Future Work | p. 179 |
| Conclusion | p. 180 |
| Resource Adaptation for a Scalable Agent Society in the MoTiV-PTA Domain | p. 183 |
| Introduction | p. 183 |
| A Generic Resource Allocation Procedure | p. 184 |
| A Hybrid Representation of an Agent Society | p. 185 |
| Adaptation through Monitoring and Resource Allocation | p. 187 |
| The SIF Simulation Environment | p. 189 |
| MECCA | p. 190 |
| FIPA | p. 190 |
| The MECCA System | p. 192 |
| Personal Travel Assistance | p. 193 |
| Simulation Studies in the PTA Scenario | p. 196 |
| Simulation | p. 196 |
| Optimization | p. 198 |
| Conclusion and Future Work | p. 202 |
| Appendix | p. 203 |
| Economic Agents for Automated Trading | p. 207 |
| Commerce meets the Web | p. 207 |
| Agents in Electronic Commerce | p. 208 |
| Agents which Negotiate | p. 209 |
| Negotiating Strategies | p. 210 |
| Marketplaces where Agents Meet | p. 211 |
| The Persistent Shout Double Auction | p. 212 |
| Supply and Demand | p. 212 |
| Agents which Adapt to the Market | p. 213 |
| Stable Marketplaces of Agents | p. 215 |
| Moving to Realistic Markets | p. 217 |
| Agent-Oriented Middleware for Integrating Customer NetworkServices | p. 221 |
| Introduction | p. 221 |
| Background: Network and Service Level Integration | p. 223 |
| Network Level Integration | p. 223 |
| Service Integration | p. 224 |
| Use of Middleware to aid Service Integration | p. 226 |
| Software Agents to Aid Service Integration (Enhance Access) | p. 228 |
| Design Issues for Agent-Oriented Middleware | p. 233 |
| 1 The Importance of Standards | p. 233 |
| Basic Architectural Designs | p. 235 |
| The Degree to which the AOM should be Agentised | p. 236 |
| How to Integrate the Agents with an Existing Infrastructure | p. 237 |
| Common Agent Service Brokering Architecture (CASBAh) | p. 238 |
| Relationship of CASBAh to Other Work | p. 240 |
| System Prototype and Scenario | p. 241 |
| Conclusions | p. 242 |
| Cooperative Distributed Problem Solving for Communication Network Management | p. 247 |
| Introduction: Toward Cooperative Expert Systems | p. 247 |
| Application of DAI to Telecommunications | p. 249 |
| Customer Network Control | p. 250 |
| Cooperation in the Customer Network Control Domain | p. 252 |
| Nature of the Agents and Organization of the Domain | p. 252 |
| Basic Three-Agent Example | p. 253 |
| Cooperation to Solve a Facility Failure Problem | p. 256 |
| Agent Knowledge and Capabilities | p. 260 |
| Local Expertise | p. 260 |
| What Agents Know About Each Other | p. 260 |
| Interagent Language to Support Cooperation | p. 261 |
| Agent Control Functions | p. 264 |
| TEAM-CPS: A Research Testbed | p. 265 |
| Planning Issues in Local Agent Problem Solving | p. 265 |
| Mechanism for Cooperation | p. 266 |
| Conclusion and Ongoing Work | p. 267 |
| Mobile Software Agents for Control in Telecommunications Networks | p. 270 |
| Introduction | p. 270 |
| Rules for Robustness | p. 271 |
| Programming a Mobile Agent | p. 272 |
| Testing Mobile Agents | p. 273 |
| An Example Application | p. 274 |
| The Load Management Agent | p. 274 |
| The Parent Agent | p. 277 |
| Parent Agent Population Management | p. 279 |
| Testing the System | p. 280 |
| The Network | p. 280 |
| The Traffic Profile | p. 281 |
| Measuring Agent Performance | p. 281 |
| The Nature of the Control Provided by Mobile Software Agents | p. 284 |
| Conclusions | p. 285 |
| Cooperating Mobile Agents for Dynamic Network Routing | p. 287 |
| Introduction | p. 287 |
| Managing Networks via Populations of Mobile Agents | p. 288 |
| The Importance of Decentralization | p. 288 |
| Mobile Agents | p. 289 |
| Flexible Systems | p. 289 |
| Cognitive Tools for Systems Design | p. 291 |
| Related Work | p. 292 |
| Why Not Mobile Agents? | p. 292 |
| Experimental Model | p. 294 |
| Nodes | p. 295 |
| Agents | p. 295 |
| Experimental Measurements | p. 297 |
| Results | p. 297 |
| First Result: Performance of a System Over Time | p. 297 |
| Analysis of Agent Algorithm and Parameters | p. 299 |
| Overhead analysis | p. 301 |
| Conclusions & Future Work | p. 303 |
| Minimal Agents for Communications Network Routing: The Social Insect Paradigm | p. 305 |
| Introduction | p. 305 |
| Load balancing | p. 306 |
| Least Cost Routing | p. 307 |
| Ants in Nature | p. 308 |
| 1 Basic Principles of Trail Laying | p. 309 |
| Previous Work on Ants | p. 310 |
| Ant-Based Control (ABC) for Network Management | p. 311 |
| Pheromone Tables | p. 312 |
| Using Ants for Network Management | p. 313 |
| Ageing and Delaying Ants | p. 315 |
| How Calls are Routed | p. 315 |
| Initialization | p. 316 |
| Noise | p. 317 |
| Summary of the Ant-Based Load Balancing System | p. 318 |
| Parameters | p. 318 |
| Results of Experiments | p. 319 |
| Adaptation to Network Topology | p. 320 |
| Adaptation to the Call Statistics within a Given Topology | p. 321 |
| Adaptation to Temporary Situations | p. 321 |
| Adaptation in General | p. 321 |
| Ants for Load Balancing in Packet Switched Networks | p. 321 |
| Conclusions | p. 323 |
| Grasshopper - An Agent Platform for Mobile Agent-Based Servicesin Fixed and Mobile Telecommunications Environments | p. 326 |
| Introduction | p. 326 |
| Grasshopper - The Agent Platform | p. 327 |
| Concepts | p. 327 |
| Agent Development | p. 336 |
| Grasshopper Conclusion | p. 339 |
| Using Grasshopper for Telecommunications | p. 339 |
| Using Grasshopper within Intelligent Networks | p. 341 |
| Using Grasshopper within Mobile Communication Systems | p. 347 |
| One Step Beyond: Active Networking | p. 352 |
| Conclusion | p. 354 |
| Mobile Agents for Managing Networks: The MAGENTA Perspective | p. 358 |
| Introduction | p. 358 |
| State of the Art of Mobile Code in Network Management | p. 359 |
| 1 Network Management Systems | p. 359 |
| Mobile Code in Network Management | p. 360 |
| Mobile Agents in Network Management? | p. 362 |
| The Astrolog Network Management System | p. 364 |
| MAGENTA Environment | p. 365 |
| Components of the Environment | p. 366 |
| Characteristics of the Environment | p. 367 |
| Functionalities of the Astrolog Network Management System | p. 370 |
| Performance Evaluation | p. 371 |
| Context | p. 371 |
| Comparison between Client Server and Mobile Agents | p. 371 |
| Conclusion | p. 379 |
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