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Agent-Based Ubiquitous Computing

Profile image of Gregory O'HareGregory O'Hare

2010, Atlantis Ambient and Pervasive Intelligence

Abstract

Ubiquitous computing names the third wave in computing, where the personal computing era appears when technology recedes into the background of our lives. The widespread use of new mobile technology implementing wireless communications such as personal digital assistants (PDAs) and smart phones enables a new type of advanced applications. In the past years, the main focus of research in mobile services has aimed at the anytime-anywhere principle (ubiquitous computing). However, there is more to it. The increasing demand for distributed problem solving led to the development of multi-agent systems. The latter are formed from a collection of independent software entities whose collective skills can be applied in complex and real-time domains. The target of such systems is to demonstrate how goal directed, robust and optimal behavior can arise from interactions between individual autonomous intelligent software agents. These software entities exhibit characteristics like autonomy, responsiveness, pro-activeness and social ability. Their functionality and effectiveness has proven to be highly depended on the design and development and the application domain. In fact, in several cases, the design and development of effective services should take into account the characteristics of the context from which a service is requested. Context is the set of suitable environmental states and settings concerning a user, which are relevant for a situation sensitive application in the process of adapting the services and information offered to the user. Agent technology seems to be the right technology to offer the possibility of exploring the dynamic context of the user in order to provide added-value services or to execute more and complex tasks. In this respect, agent-based ubiquitous computing can benefit from marrying the agent-based technology for the extensive usage of distributed functionality, to be deployed for lightweight devices and enable to combine ubiquity and intelligence in different application areas and challenge with questions the research communities in computer science, artificial intelligence and engineering. We noticed during the AAMAS workshop we organized about this issue in 2007 that, although a number of other books on ubiquitous computing have been published in the last years, none of these has focused on the agent-based perspective. So we opened a call for chapters to gather input and feedback concerning the above challenges, through the collection of the high-quality contributions that reflect and advance the state-of-the art in agent-based ubiquitous application systems. It brought together researchers, agent-based vii viii Agent-Based Ubiquitous Computing software developers, users and practitioners involved in the area of agent-based ubiquitous systems, coming from many disciplines, with the target to discuss the different fundamental principles for construction and design of agents for specific applications, how they cooperate and communicate, what tasks can be set and how different properties like coordination and communication have been implemented, and which are the different problems they had to cope with. Existing perspectives of ubiquitous agents within different application domains have been welcome, as well as the different mechanisms for design and cooperation that can be used within different agent building environments. Specifically, the book focused on the different disciplines contributing to the design, cooperation, coordination and implementation problems of ubiquitous computing applications and how these can be solved through the utilization of agents. Thanks are due to all contributors and referees for their kind cooperation and enthusiasm, and to Zeger Karssen (Editorial Atlantis Press) for his kind advice and help to publish this volume.

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If mobile computing is to become the predominant computer usage paradigm in the coming years as is widely anticipated, a substantial increase in the demand for mobile computing applications and services will arise. How best to engineer such applications remains an open question and is naturally the focus of much research. In this article, we propose the use of intelligent agents as the constituent entities around which such applications and services can be conceptualised and realised. We describe the design and implementation of Gulliver's Genie, an archetypal ubiquitous computing application that commissions a collaborative embedded agents approach.

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Citeseer, 2007

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References (82)

  1. 3 Global structure of the agent-based generic model . . . . . . . . . . . . . . . . . . 37
  2. 4 Generic ambient agent and world model . . . . . . . . . . . . . . . . . . . . . . . 40
  3. 5 Case study 1: An ambient driver support system . . . . . . . . . . . . . . . . . . . 42
  4. 6 Case study 2: Ambient aggression handling system . . . . . . . . . . . . . . . . . 45
  5. 7 Case study 3: Ambient system for management of medicine usage . . . . . . . . . 48
  6. 8 Specification and verification of dynamic properties . . . . . . . . . . . . . . . . . 49
  7. 9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
  8. 10 Appendix 1: Driver case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.10.1 Driver assessment agent: Domain-specific temporal rules . . . . . . . . . 54 3.10.2 Cruise control agent: Domain-specific temporal rules . . . . . . . . . . . 54 3.10.3 Steering monitoring agent: Domain-specific temporal rules . . . . . . . . 54 3.10.4 Steering sensoring agent: Domain-specific temporal rules . . . . . . . . . 55 3.10.5 Gaze-focus sensoring agent: Domain-specific temporal rules . . . . . . . 55 3.10.6 Alcohol-level monitoring agent: Domain-specific temporal rules . . . . . 55 3.10.7 Alcohol sensoring agent: Domain-specific temporal rules . . . . . . . . . 56 3.10.8 Driver: Domain-specific temporal rules . . . . . . . . . . . . . . . . . . . 56 3.10.
  9. 9 Car and environment: Domain-specific temporal rules . . . . . . . . . . . 56
  10. 11 Appendix 2: Aggression handling case . . . . . . . . . . . . . . . . . . . . . . . . 56 3.11.1 Sound analysis agent: Domain-specific temporal rules . . . . . . . . . . . 56 3.11.2 Microphone agent: Domain-specific temporal rules . . . . . . . . . . . . 57 3.11.3 Persons in crowd: Domain-specific temporal rules . . . . . . . . . . . . . 58 3.11.4 Police officer at station: Domain-specific temporal rules . . . . . . . . . . 58 3.11.5 Police officer at street: Domain-specific temporal rules . . . . . . . . . . 58
  11. 12 Appendix 3: Medicine usage case . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.12.1 Medicine box agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.12.2 Usage support agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
  12. e-Assistance Support by Intelligent Agents over MANETs 63
  13. Eduardo Rodríguez, Juan C. Burguillo and Daniel A. Rodríguez
  14. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
  15. 1.1 Multi agent systems (MAS) . . . . . . . . . . . . . . . . . . . . . . . . . 64
  16. 1.2 Ubiquitous computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
  17. 1.3 Case based reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.1.4 Peer-to-peer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
  18. 1.5 Mobile ad-hoc networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
  19. 2 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
  20. 2.1 Reasoning process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
  21. 2.2 Communication process . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
  22. 3 A case of study: An intelligent gym . . . . . . . . . . . . . . . . . . . . . . . . . 77
  23. 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
  24. The Active Metadata Framework 85
  25. Christopher McCubbin, R. Scott Cost, John Cole, Nicholas Kratzmeier, Markus Dale, Daniel Bankman 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.1.1 Background: Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.1.2 Background: The active metadata concept . . . . . . . . . . . . . . . . . 87
  26. 2 SimAMF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2.4 Simulation visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3 SWARM-AMF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.3.2 System design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.3.3 An experiment using some swarming metrics . . . . . . . . . . . . . . . . 97 5.3.4 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.4 List of acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
  27. Coalition of Surveillance Agents. Cooperative Fusion Improvement in Surveillance Systems 103
  28. Federico Castanedo, Miguel A. Patricio, Jesús García and José M. Molina
  29. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
  30. 2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
  31. 3 Cooperative surveillance agents architecture . . . . . . . . . . . . . . . . . . . . . 105
  32. 3.1 Sensor and coalition layer . . . . . . . . . . . . . . . . . . . . . . . . . . 107
  33. 3.2 Coalition protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
  34. 4 Information fusion for tracking during coalition maintenance . . . . . . . . . . . . 109 6.4.1 Time-space alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 6.4.2 Map correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
  35. 5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
  36. 6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
  37. Designing a Distributed Context-Aware Multi-Agent System 117
  38. Virginia Fuentes, Nayat Sánchez-Pi, Javier Carbó and José M. Molina
  39. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
  40. 2 Context-aware multi-agent framework for heterogeneous domains . . . . . . . . . 118 7.2.1 Multi-agent architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
  41. 3 BDI model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
  42. 3.1 Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
  43. 3.2 Desires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
  44. 3.3 Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
  45. 4 Gaia methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
  46. 4.1 Analysis phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
  47. 5 Analysis and design using Gaia methodology . . . . . . . . . . . . . . . . . . . . 123 7.5.1 The environmental model . . . . . . . . . . . . . . . . . . . . . . . . . . 124 7.5.2 The organization structure . . . . . . . . . . . . . . . . . . . . . . . . . . 124
  48. 5.3 Role model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 7.5.4 Interaction model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 7.5.5 Organizational rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 7.5.6 Agent model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 7.5.7 Service model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
  49. 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
  50. Agent-Based Context-Aware Service in a Smart Space 131 Wan-rong Jih, Jane Yung-jen Hsu
  51. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
  52. 2 Background technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 8.2.1 Context models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 8.2.2 Context reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
  53. 3 Smart space infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
  54. 4 Context-aware service platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
  55. Context-awarereasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 8.4.2 Service planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 8.4.3 Context knowledge base . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
  56. 5 Demonstration scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 8.5.1 Context-aware reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . 142 8.5.2 Service planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
  57. 6 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
  58. 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
  59. Prototype for Optimizing Power Plant Operation 147 Christina Athanasopoulou and Vasilis Chatziathanasiou
  60. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
  61. 9.2 Problem domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
  62. 9.2.1 Electricity generation units . . . . . . . . . . . . . . . . . . . . . . . . . 148
  63. 9.2.2 Knowledge engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
  64. 9.3 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
  65. 9.3.1 Agent programming paradigm . . . . . . . . . . . . . . . . . . . . . . . 150
  66. 9.3.2 Intelligent Power Plant engineer Assistant MAS (IPPAMAS) . . . . . . . 151
  67. 9.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
  68. 9.4.1 Data mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
  69. 9.4.2 Multi-agent system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
  70. 9.4.3 Wireless transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
  71. 9.4.4 Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
  72. 9.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
  73. 9.5.1 MAS performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
  74. 9.5.2 User evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
  75. 9.6 Concluding remarks and future enhancements . . . . . . . . . . . . . . . . . . . . 161
  76. IUMELA: Intelligent Ubiquitous Modular Education Learning Assistant in Third Level Education 163
  77. Elaine McGovern, Bernard Roche, Rem Collier, Eleni Mangina 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
  78. 2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 10.2.1 Multi-agent systems based learning technologies . . . . . . . . . . . . . . 164 10.2.2 The mobile device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 10.2.3 Modular education at UCD . . . . . . . . . . . . . . . . . . . . . . . . . 169 10.2.4 Learning styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 10.2.5 Teaching strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 10.2.6 Evaluation techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 10.2.7 Presenting modules for selection . . . . . . . . . . . . . . . . . . . . . . 171
  79. 3 IUMELA: the agent architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 10.3.1 The assistant agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 10.3.2 The moderator agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 10.3.3 Expert agent technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 174
  80. 4 IUMELA student user interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 10.4.1 Initial registration and login . . . . . . . . . . . . . . . . . . . . . . . . . 174 10.4.2 Personalised welcome screen . . . . . . . . . . . . . . . . . . . . . . . . 175 10.4.3 Learning journal facility . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 10.4.4 Student messaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 10.4.5 The module and assistant facilities . . . . . . . . . . . . . . . . . . . . . 178
  81. 5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 10.5.1 ABITS FIPA messenger in IUMELA . . . . . . . . . . . . . . . . . . . . 180
  82. 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

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