Applications and Innovations in Intelligent Systems XIV (eBook)

APPLICATION PROGRAMME CHAIR'S INTRODUCTION 5
ACKNOWLEDGEMENTS 6
APPLICATION EXECUTIVE PROGRAMME COMMITTEE 6
APPLICATION PROGRAMME COMMITTEE 7
Table of contents 
8 
BEST APPLICATION PAPER 11
Managing Restaurant Tables using Constraints 13
1. Introduction 13
2. Restaurant Table Management 14
3. Constraint Programming 15
4. Modelling the static table management problem 16
5. Flexibility and Optimisation 18
6. Minimising Disruption 20
7. The Integrated Table Management Adviser 20
8. Conclusions and Future Work 25
Acknowledgements 26
References 26
SESSION 1: DATA MINING AND BAYESIAN NETWORKS 
28 
Use of Data Mining Techniques to Model Crime Scene Investigator Performance 
29 
1. Introduction 29
2. Current CSI Activity and Assessment 
30 
3. Methodology 31
3.1 CRISP-OM 32
4. Data Manipulation 34
5. Results 36
5.1 CSI Personal Predictions 39
6. Conclusion 40
References 41
Analyzing Collaborative Problem Solving with Bayesian Networks 
43 
1 Introduction 43
2 Bayesian networks in teaching and learning 44
3 Collaborative problem solving in DomoSim-TPC 45
4 A Bayesian network to model collaborative work 48
5 Data analysis 49
6 Conclusions 52
Acknowledgments 52
References 53
The Integration of Heterogeneous Biological Data using Bayesian Networks 
54 
1 Introduction 54
2 Bayesian Networks Models 56
2.1 Modification of Bayesian Network through Informative Prior Knowledge 
58 
2.2 Related Work on Bayesian Networks 59
3 Integrating Heterogenous Sources of Data 59
3.1 Genomic Data (Microarray Data) 60
3.2 Proteomic data (2DE Gels) 61
3.3 Expert opinion and diabetes domain knowledge 61
4 Experimental Procedure 62
4.1 Data preprocessing 62
4.2 Results 63
5 Conclusions 66
6 Acknowledgements 66
References 66
Automatic Species Identification of Live Moths 68
1. Introduction 68
2. Recent Work on Automatic Species Identification 69
3. The Macrolepidoptera Image Collection 71
4. Extracting Features from the Images 71
5. Results 74
6. Conclusion 79
Acknowledgements 81
References 81
SESSION 2: GENETIC ALGORITHMS ANDOPTIMISATION TECHNIQUES 
82 
Estimating Photometric Redshifts Using Genetic Algorithms 
83 
1. INTRODUCTION 83
1.1 Spectroscopy & Photometry
1.2 Contribution 84
2. Extragalactic Astronomy 84
2.1 What is redshift? 84
2.2 Finding high-redshift objects 84
2.3 Photometric Redshift 85
2.4 Deep Field Surveys 86
3. Preparation of the data to be mined 86
4. A Genetic Algorithm for estimating redshifts 87
4.1 Individual Representation 87
4.2 SequentialCovering 88
4.3 Fitness Function 89
4.4 Genetic Operators (Crossover and Mutation) 90
4.4.1 Crossover 90
4.5 Selection Method 91
5. Computational Results 91
6. Conclusion and Future Research 93
7. References 93
Non-linear Total Energy Optimisation of a Fleet of Power Plants 
96 
1. Introduction 96
2. Approaches to Mixed Integer Optimisation 97
3. Fast Simulation of Power Plants 98
4. Simulated Annealing 99
5. Power Plant Optimisation: A Case Study 100
6. Conclusion 103
References 104
Optimal Transceivers Placement in an Optical Communication Broadband Network Using Genetic Algorithms 
105 
1. Introduction 105
2. Problem Description 106
3. Method Proposed 107
4. Description of the Genetic Algorithm. 108
5. Simulations and Results 110
5. Conclusions 112
Acknowledgment 114
References 114
SASS APPLIED TO OPTIMUM WORK ROLL PROFILE SELECTION IN THE HOT ROLLING OF WIDE STEEL 
115 
1. Introduction 115
2. Hot RoIling of Wide Strip 116
2.1 Mill Train 116
2.2 Strip Quality 117
2.3 Initially Ground Work Roll Profiles 119
2.4 Roll Profile Specification 120
3. Optimisation of profiles 120
3.1 The Fitness Function 121
3.2 Self-Adaptive Stepsize Search 121
4. Experimental Results 123
5. Conclusion 124
References 125
SESSION 3: AGENTS AND SEMANTIC WEB 
126 
Agents in Safety Related Systems Including Ubiquitous Networks 
127 
1. Introduction 127
2. Overview 129
3. Handling Safety 133
4. Intelligence 135
5. Matching lEe 61508 and ADM 136
6. Demonstrator 138
7. Conclusions 139
References 140
Using Semantic Web technologies to bridge the Language Gap between Academia and Industry in the Construction Sector 
141 
1. Introduction 141
2. Motivation and initial requirements 142
3. A Web portal with a keyword-based search engine 143
4. A SW portal with an ontology-based search engine 147
5. Empirical results: comparing both search engines 152
6. Conclusions 153
References 154
Acknowledgements 154
Ontology based CBR with jCOLIBRI 155
1 Introduction 155
2 The jCOLIBRI Architecture 156
3 Ontology based CBR 157
3.1 Ontologies as the CBR system vocabulary 158
3.2 Case Retrieval using Ontologies 159
3.3 Case Adaptation based on Ontologies 161
4 KI Case Representation and Retrieval in jCOLIBRI 
162 
5 Ontology-based Adaptation in jCOLIBRI 165
6 Conclusions 167
References 168
Domain Dependent Distributed Models for Railway Scheduling 169
1 Introduction. 169
2 Distributed CSPs. 171
2.1 Definitions. 171
2.2 The Distributed Model. 172
3 Railway Scheduling Problem. 173
3.1 Constraints in the Railway Scheduling Problem 174
4 Partition Proposals 177
4.1 Domain Independent: Partition Proposall. 177
4.2 Domain Dependent: Partition Proposal 2. 177
4.3 Domain Dependent: Partition Proposal 3. 178
5 Evaluation. 179
Random problems. 179
Benchmark problems. 179
6 Conclusions. 180
References 182
SESSION 5: NATURAL LANGUAGE 
184 
Bringing Chatbots into Education: Towards Natural Language Negotiation of Open Learner Models 
185 
1 Background 185
1.1 Chatbots 185
1.2 Intelligent Tutoring Systems 187
1.3 Learner Modelling and Open Learner Modelling 187
1.4 Intelligent Tutoring Systems that use Natural Language 188
2 Using a Chathot for Negotiated Learner Modelling 
189 
2.1 The Choice of Chatbot 189
3 Wizard-of-Oz study 190
3.1 The Wizard-of-Oz Paradigm 190
3.2 The Learner Modelling System 190
3.3 Experimental setup 191
3.4 Outcomes 193
4 Lessons for implementation 194
4.1 System Requirements 194
4.2 Areas for Further Research 195
5 Conclusion 196
Acknowledgements 196
References 196
Adding question answering to an e-tutor for programming languages 
199 
1. Introduction 199
2. Related Work 200
3. IVC Architecture 
203 
3.1 Challenges in teaching Verilog 203
3.2 Constructingthe ontology 203
3.3 Cross-linkingthe different ontologies 203
3.4 Shallow question answering 204
3.5 User Concept Model 204
3.6 User interface 205
4. Results 206
4.1 Research Questions 206
4.2 Methodology 207
4.3 Question Answering Opportunities 207
4.4 Average Activity Index Results 209
4.5 Tracking User Activity 209
4.6 Results from student revision 210
5. Conclusions 211
References 212
Speech-Enabled Interfaces for Travel Information Systems with Large Grammars 
213 
1. Introduction 213
2. Large grammar issues in a London bus travel application 
214 
3. First letter based grammar reduction system 215
3.1 Dividing a large grammar file into many small files 216
3.2 Confusion matrix for first letter recognition 216
3.3 Large & first letter grammar system comparison
3.4 Automatic first phoneme recognition system 220
4. Last word based recognition system 221
4.1 The system design 222
4.2 The experimental results 223
5. Conclusion and discussion 225
References 226
SHORT PAPERS 228
Adoption of New Technologies in a Highly Uncertain Environment: The Case of Egyptian Public Banks 229
1. Introduction 229
2. Current Dominant Technology Adoption Models 230
3. Case: Technology Adoption in Egyptian Banks 230
4. Discussion and Conclusion 232
5. References 232
RoboCup 3D Soccer Simulation Server: A Progressing Testbed for AI Researchers 
234 
1. Introduction 234
2. Characteristics of RoboCup 3D Soccer Server 235
3. Inside of RoboCup 3D Soccer Server 235
3.1 World and Object Representation 235
3.2 Scene Graph 236
4. SPADES 236
4.1 Component Organization 237
4.2 Sense-Think-Act 237
5. Putting All Together: A Complete Simulation 237
6. Conclusion 238
References 238
Review of Current Crime Prediction Techniques 239
1. Introduction 239
2. Crime Facts 240
2.1 Crime Recording Process 240
2.2 Environmental Criminology 240
3. Current Crime and Offending Prediction Techniques 240
3.1 Statistical Methods 240
3.2 Miscellaneous Methods 241
3.3 Geographical Information System Methods 241
4. Discussion 242
References 242
Data Assimilation of a Biological Model Using Genetic Algorithms 
244 
1. Introduction 244
2. Mathematical Model of the Problem 245
3. Experimental Setup 246
4. Numerical Results and Discussions 247
5. Conclusions and Future Scope 247
6. Acknowledgements 248
References 248
AUTHOR INDEX 249