Modeling and Use of Context in Action
Description
The first three chapters lay the foundations, looking at the lessons learned over the past 25 years and arguing for a continued shift toward more pragmatic approaches. The remaining chapters contain contributions to pragmatic context-based research from a wide range of domains, including technological problems - such as subway incident management and autonomous underwater vehicle control - identifying emotions from speech without understanding the words, anonymization in a world where privacy is increasingly threatened, teaching in context and improving management teaching in a business school.
Patrick Brezillon is Professor Emeritus at Sorbonne University, France. His research focuses on context modeling and its use in applications and has culminated in the Contextual-Graph software. This research focus is shared by a large community that has been active for almost 25 years.
Roy M. Turner is Associate Professor of computer science at the University of Maine, USA. His research area is artificial intelligence, focusing in particular on context-sensitive reasoning, deep learning in context, intelligent real-world agent control and computational ecology.
More details
Other editions
Additional editions
Persons
Patrick Brezillon is Professor Emeritus at Sorbonne University, France. His research focuses on context modeling and its use in applications and has culminated in the Contextual-Graph software. This research focus is shared by a large community that has been active for almost 25 years.
Roy M. Turner is Associate Professor of computer science at the University of Maine, USA. His research area is artificial intelligence, focusing in particular on context-sensitive reasoning, deep learning in context, intelligent real-world agent control and computational ecology.
Content
Preface xi
Patrick Brézillon and Roy M. Turner
Introduction xxi
Patrick Brézillon and Roy M. Turner
Chapter 1 Pragmatic Research on Context Modeling and Use 1
Patrick Brézillon and Roy M. Turner
1.1 Introduction 1
1.2 Pragmatic research on context 2
1.3 Role of context in AI systems 3
1.3.1 Data, information and knowledge 3
1.3.2 Contextual knowledge 6
1.4 Three examples of pragmatic research on context 8
1.4.1 Introduction 8
1.4.2 Contextual graphs (CxGs) 9
1.4.3 Context-based reasoning (CxBR) 11
1.4.4 Context-mediated behavior (CMB) 12
1.4.5 Conclusions and lessons learned 14
1.5 Conclusion 18
1.6 References 19
Chapter 2. Modeling and Using Context: 25 Years of Lessons Learned 23
Patrick Brézillon
2.1 Introduction 23
2.2 Knowledge in action 25
2.2.1 Operational knowledge and contextual knowledge 25
2.2.2 Operational knowledge and mental models 26
2.2.3 Modeling operational knowledge 27
2.2.4 Indirect modeling from experience reuse 29
2.2.5 Lessons learned 31
2.3 Context in action 32
2.3.1 Conceptual modeling 32
2.3.2 A typology of contexts 33
2.3.3 About contextual elements 34
2.3.4 Implementation of the contextual graphs formalism 39
2.4 Using context in real-world applications 40
2.4.1 Context and focus processing 40
2.4.2 Context and actors 42
2.4.3 Extension of the CxG formalism 43
2.5 Conclusion 46
2.6 References 49
Chapter 3 Toward Pragmatic Context-Based Intelligent Systems 53
Roy M. Turner and Patrick Brézillon
3.1 Introduction 53
3.2 Evolution of AI systems 55
3.2.1 Formal versus pragmatic acontextual approaches 55
3.2.2 Formal consideration of context 56
3.2.3 Pragmatic consideration of context 57
3.3 Pragmatic context-based intelligent systems 62
3.3.1 Explicit context representation 63
3.3.2 Context assessment mechanism 66
3.3.3 Context transitioning mechanism 68
3.3.4 Context-based intelligent assistant systems 68
3.3.5 Context-based intelligent autonomous agents 73
3.4 Conclusion 80
3.5 References 81
Chapter 4 Activating the Context for Learning and Teaching: Findings from the TEEC Project 87
Claire Anjou, Thomas Forissier, Jacqueline Bourdeau, Valéry Psyché, Lamprini Chartofylaka and Alain Stockless
4.1 Introduction 87
4.2 Theoretical framework 89
4.2.1 Internal and external contexts for education 89
4.2.2 Modeling external context 91
4.3 The research focuses 95
4.4 Methodology 98
4.4.1 DBR methodology 98
4.4.2 Data collection and analysis 99
4.4.3 TEEC organization 99
4.5 Results and findings 101
4.5.1 Context effects identification and specification 101
4.5.2 Using the digital technologies 105
4.5.3 Learning as an evolution of mental representations 106
4.5.4 The development of digital tools 107
4.6 Discussion and interpretation 114
4.6.1 Context effect and affective dimension: learning with contexts, contexts effect and cognitive conflict 114
4.6.2 Digital education and context 117
4.6.3 Mazcalc needs to interact with the scripting tool 118
4.7 Conclusion and related work 118
4.8 Acknowledgment and credits 120
4.9 Appendices: description of the TEEC experiment 120
4.9.1 Historical event/social realities 120
4.9.2 Geothermal energy 121
4.9.3 Literature 122
4.9.4 Sustainable development: sugar 122
4.9.5 Sustainable development: fruit 124
4.10 References 125
Chapter 5 Pragmatic Reasoning in Context: Context-Mediated Behavior 131
Roy M. Turner
5.1 Introduction 131
5.2 Context-mediated behavior 133
5.2.1 CMB for autonomous agents: Orca Project 137
5.2.2 Contextual schemas 138
5.2.3 Context assessment 144
5.3 CMB and planning 146
5.4 CMB in multiagent systems 149
5.4.1 Context-appropriate organization and reorganization 149
5.4.2 An ontology for contextual knowledge and contexts 151
5.4.3 Trust in context 154
5.5 (Deep) learning in context 155
5.6 Conclusion 162
5.7 Acknowledgments 162
5.8 References 163
Chapter 6 Using Context to Help Identify the Emotional State of a Human in a Conversation 169
Andreas H. Marpaung and Avelino J. Gonzalez
6.1 Introduction and background 169
6.2 Use case and research hypothesis 170
6.3 Related works 172
6.4 Sentiment analysis as a way to model context 174
6.5 Our approach to the problem 176
6.5.1 Our overall approach to paralinguistic affect recognition 176
6.5.2 A (very) brief description of phase I (context-free classification) 177
6.5.3 Phase II - the context-centered process 178
6.6 Example application: smart phone 189
6.6.1 Phase 1: context-free process 189
6.6.2 Phase 2: context-centered process 190
6.7 Summary and conclusion 191
6.8 References 192
Chapter 7 Context-Driven Behavior: A Proactive Approach to Contextual Reasoning 197
Christian Wilson
7.1 Motivation for a proactive model 197
7.2 Challenges associated with a proactive model 199
7.2.1 Coping with uncertainty 199
7.2.2 A lack of initial knowledge 202
7.3 Context and contextual knowledge 203
7.3.1 Problem-solving contexts 203
7.3.2 Contextual schemas 204
7.4 A framework for context-driven agent 208
7.4.1 Defining a problem-solving scenario 209
7.4.2 Predicting future contexts 210
7.4.3 Identifying context-inappropriate behavior 215
7.4.4 Strategy modification 217
7.5 Conclusion 219
7.6 References 219
Chapter 8. Context-Based Personal Data Discovery for Anonymization 221
Hassane Tahir and Patrick Brézillon
8.1 Introduction 221
8.2 Personal and sensitive data 223
8.3 Procedure of personal data discovery 224
8.3.1 Objective of personal data discovery procedures 224
8.3.2 Role of a DPO in personal data discovery 225
8.3.3 Description of procedure of data discovery 225
8.4 Specifying personal data in the context of an anonymization process 228
8.4.1 Definition of anonymization 228
8.4.2 Motivation for data anonymization 228
8.4.3 Examples of techniques of anonymization 229
8.4.4 Anonymization process 231
8.4.5 Contextual elements in personal data discovery 232
8.5 Related work 235
8.6 Procedure contextualization for data discovery 236
8.6.1 The concept of context 236
8.6.2 Conceptual graph approach 236
8.6.3 A case study 238
8.7 Conclusion 242
8.8 References 243
Chapter 9 Situated Management Learning 245
John Hegarty and Régis Maubrey
9.1 Introduction 245
9.2 Management practices, values and theoretical insights 246
9.2.1 Management practices 247
9.2.2 Management values 251
9.2.3 Management insights 253
9.2.4 Toward a dynamic model of situated management learning 256
9.3 Situated learning - an application in an accounting classroom 257
9.3.1 The rules of the learning game 257
9.3.2 The accounting decision-making situation 258
9.3.3 Learning teams 259
9.3.4 Deliverables by the learning teams 259
9.3.5 Feedback to the learning teams 260
9.4 Results 260
9.5 Discussion, outlook and related research 261
9.6 Acknowledgments 262
9.7 References 263
List of Authors 267
Index 269
