A Computational Model of “Artificial Intuition” in Decision Making
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A COMPUTATIONAL MODEL OF “ARTIFICIAL INTUITION” IN DECISION MAKING BY OLAYINKA PATRICK JOHNNY A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY EDGE HILL UNIVERSITY ORMSKIRK, ENGLAND SEPTEMBER 2020 A Computational Model of ”Artificial Intuition" in Decision Making Olayinka Patrick Johnny Submitted for The Degree of Doctor of Philosophy ABSTRACT The ability to perform a data-driven decision-making approach is at the core of Data Science, AI, and general Machine Learning techniques. To achieve a detailed data-driven approach, all possible scenarios must be con- sidered, and their outcomes must be assessed logically and systematically to obtain accurate and applicable methods for knowledge discovery. These are considered in order to identify the best possible choice. Although, the data-driven approaches have been shown to be effective in theory, a major drawback is that it is typically associated with high computational com- plexity. Moreover, it is non-trivial to develop and train models with deep and complex model structures with potentially large number of parameters. However, there are compelling evidence from the cognitive sciences that in- tuition plays an important role in intelligence extraction and the associated decision-making process. More specifically, intuition can be used to identify, combine and discover knowledge in a `parallel' manner and so more effi- ciently. As a consequence, the embedding of Artificial Intuition within Data Science is likely to provide novel ways to identify and process information. The first contribution of this thesis is the introduction of a rigorous math- ematical formulations and a novel algorithm for artificial intuition. Specif- ically, a mathematical formulation is introduced to describe a model that utilises semantic network to improve decision making. Moreover, the model that is introduced included some lemmas and propositions that provides a way of combining the aggregation of edges in semantic networks. The model implements techniques from computational linguistic via the process- ing pipeline to derive semantic networks. Moreover, the thesis contributed a state-of-the-art review of artificial intuition. The author provided relevant 2 and detailed research about the concepts of artificial intuition as it relates to creativity, gut-feeling, rational thinking. It finally identified the umbrella concept called artificial intuition and identified some key requirements for the development of a model. Keywords: Artificial Intuition, Artificial Intelligence, Modelling, Net- work Theory, Data Science, Decision Algorithms 3 Dedications I will like to dedicate this Ph.D thesis to my wife and children for their love, support and encouragement. I will also like to dedicate this to my parents, even though they are no longer here with us, they will be proud of their son. 4 Acknowledgements I will like to thank all those who have made this thesis possible. I will like to thank my Ph.D supervisory team, Dr. Swagat Kumar and Dr. Hari Pandey. In particular, my special thanks go to my Director of Studies, Prof. Marcello Trovati for his guidance and support and for supervising my thesis. I have learnt a lot from you in this my Ph.D journey. I will not forget to thank Professor Nik Bessis for his support. I will like to thank my wife and angel, Toyin for her patience, support and understanding throughout my studies. My thanks also go to my wonderful children, Oluwadamilare and Oluwademilade for being so lovely. 5 List of Main Publications 1. Johnny, O., Trovati, M., and Ray, J. (2017, August). The \Gut- Feeling" in the Decision-Making Process: A Computationally Efficient Approach to Influence Relation Assessment. In International Confer- ence on Intelligent Networking and Collaborative Systems (pp. 576- 584). Springer, Cham. 2. Johnny, O., Trovati, M., and Ray, J. (2019, August). Towards a Com- putational Model of Artificial Intuition and Decision Making. In In- ternational Conference on Intelligent Networking and Collaborative Systems. Springer, Cham. 3. Johnny, O., and Trovati, M. (2020, August). Knowledge-Based Net- works for Artificial Intuition. In International Conference on Intelli- gent Networking and Collaborative Systems (pp. 319-325). Springer, Cham. 4. Johnny, O., and Trovati, M. (2018, September). Big Data Inconsis- tencies: A Literature Review. In International Conference on Intelli- gent Networking and Collaborative Systems (pp. 486-492). Springer, Cham. 5. Ray, J., Johnny, O., Trovati, M., Sotiriadis, S., and Bessis, N. (2018). The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory. Big Data and Cognitive Computing, 2(3), 22. 6. Johnny, O, and Trovati M. (Jan 2020). Big data inconsistencies and incompleteness: a literature review Int. J. Grid and Utility Comput- ing, 7. Johnny, O., Sotiriadis, S., Asimakopoulou, E., and Bessis, N. (2014, September). Towards a social graph approach for modeling risks in big data and Internet of Things (IoT). In Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference on (pp. 439-444). IEEE. 6 Contents 1 Introduction 12 1.1 Problem Statement ....................... 13 1.2 Research Aim and Objectives ................. 16 1.3 Motivation ............................ 16 1.4 Major Contributions ...................... 19 1.5 Thesis Organization ...................... 20 2 Background and Literature Review 22 2.1 Background ........................... 22 2.2 Human Intuition and Decision Making Approaches .... 23 2.3 Overview of the application of artificial intuition ..... 30 2.3.1 Intuition and Clinical Decision Making (CDM) .. 30 2.3.2 Intuition and Business Decision ........... 31 2.3.3 Intuition and Player in a Game ............ 35 2.4 Emotion and Decision Making Approaches ......... 35 2.5 Rational Decision-Making Approaches ............ 42 2.5.1 Bayesian Network Creation from data ........ 43 2.6 Guts-feeling and Decision Making .............. 50 2.7 Shallow Learning Approaches of Decision Network .... 54 2.8 Neural Network and Decision-Making Approaches .... 55 2.9 Summary and Discussion of the Approaches ........ 59 2.10 Key Requirements for the Model Development ....... 62 3 Network, Graph and Semantic Network 66 3.1 Network and Graph Theory .................. 66 3.1.1 Scale-free Networks ................... 67 3.1.2 Random Graphs ..................... 68 3.1.3 Small-world Networks ................. 68 3.2 Graph Theory .......................... 69 3.3 Semantic Analysis ........................ 71 3.4 Semantic Network ........................ 76 7 3.4.1 Advantages and Disadvantages of Semantic Network 80 3.4.2 Applications of Semantic Networks ......... 83 3.5 Measurements of Semantic Similarities and Relatedness . 84 3.5.1 A Motivating Discussion ................ 86 3.6 Spreading Activation ...................... 89 3.7 Modelling Semantic Network evolution and Decision Mak- ing ................................. 92 3.8 Algorithms For The Evolution of Semantic Networks ... 93 3.9 Summary ............................. 96 4 Gut-feeling in Decision Making: An initial Ap- proach for Influence Relation Assessment 98 4.1 Description of the Approach ................. 100 4.2 Summary ............................. 105 5 The Proposed Solution 106 5.1 Defining Essential Components of the Model ........ 106 5.2 General Definitions and Background of Main model ... 108 5.3 Description of the Main Artificial Intuition Model .... 112 5.3.1 Combination of Edge Attributes ........... 114 5.3.2 Explicit Calculation of the Node Combinations .. 117 5.4 The Assessment of Potential Solutions ........... 118 5.4.1 Intuition Index ..................... 119 5.4.2 Propagation Index ................... 119 5.4.3 Edge Entropy ...................... 120 5.5 Entropy of the Query Space .................. 122 5.5.1 Geometric Interpretation of the Query Space . 124 5.6 Summary ............................. 125 6 Model Evaluation and Results 126 6.1 Description of Datasets: ConceptNet, Wikipedia and Recipe- Ingredient Datasets ....................... 127 8 6.1.1 ConceptNet ....................... 127 6.1.2 Wikipedia ........................ 130 6.1.3 Recipe-Ingredient Dataset ............... 131 6.1.4 Validation Approach .................. 132 6.2 Evaluation and Results ..................... 134 6.2.1 Evaluation using Recipe-ingredient and Concept- Net Dataset ....................... 135 6.3 Summary ............................. 143 7 Conclusion and Future Works 144 7.1 Limitation of the Research .................. 145 7.2 Future Work ........................... 146 List of Figures 1 Making a decision to take the umbrella . 15 2 Overview of entrepreneur decision making process . 34 3 A network graph from ConceptNet that is formed from the concept T hinking and the related edges (Liu and Singh, 2004) 70 4 Graph Relationship between two Nodes extracted from texts in Wikipedia . 72 5 Example of entities and relations extracted from texts in Wikipedia . 74 6 Example 2 of Entities and Relations Extracted . 75 7 Example of \is-a" link that suggest ambiguity . 82 8 Example of \is-a" link after removing the ambiguity . 82 9 Example of \is-a" link that suggest self-loop . 82 10 Evolution of Semantic Network using algorithm 1. This is an example of a small-world network . 95 11 Influence Relation/Link in a Network . 101 12 The Components of Gut-feeling Method . 101 13 Network of Influence link between some some prior knowledge of the dynamics of the rain