- •Preface
- •Contents
- •Contributors
- •Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
- •1 Introduction
- •2 The Double-Slit Experiment
- •3 Interrogative Processes
- •4 Modeling the QWeb
- •5 Adding Context
- •6 Conclusion
- •Appendix 1: Interference Plus Context Effects
- •Appendix 2: Meaning Bond
- •References
- •1 Introduction
- •2 Bell Test in the Problem of Cognitive Semantic Information Retrieval
- •2.1 Bell Inequality and Its Interpretation
- •2.2 Bell Test in Semantic Retrieving
- •3 Results
- •References
- •1 Introduction
- •2 Basics of Quantum Probability Theory
- •3 Steps to Build an HSM Model
- •3.1 How to Determine the Compatibility Relations
- •3.2 How to Determine the Dimension
- •3.5 Compute the Choice Probabilities
- •3.6 Estimate Model Parameters, Compare and Test Models
- •4 Computer Programs
- •5 Concluding Comments
- •References
- •Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval
- •1 Introduction
- •3 Quantum Mathematics
- •3.1 Hermitian Operators in Hilbert Space
- •3.2 Pure and Mixed States: Normalized Vectors and Density Operators
- •4 Quantum Mechanics: Postulates
- •5 Compatible and Incompatible Observables
- •5.1 Post-Measurement State From the Projection Postulate
- •6 Interpretations of Quantum Mechanics
- •6.1 Ensemble and Individual Interpretations
- •6.2 Information Interpretations
- •7 Quantum Conditional (Transition) Probability
- •9 Formula of Total Probability with the Interference Term
- •9.1 Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
- •10 Quantum Logic
- •11 Space of Square Integrable Functions as a State Space
- •12 Operation of Tensor Product
- •14 Qubit
- •15 Entanglement
- •References
- •1 Introduction
- •2 Background
- •2.1 Distributional Hypothesis
- •2.2 A Brief History of Word Embedding
- •3 Applications of Word Embedding
- •3.1 Word-Level Applications
- •3.2 Sentence-Level Application
- •3.3 Sentence-Pair Level Application
- •3.4 Seq2seq Application
- •3.5 Evaluation
- •4 Reconsidering Word Embedding
- •4.1 Limitations
- •4.2 Trends
- •4.4 Towards Dynamic Word Embedding
- •5 Conclusion
- •References
- •1 Introduction
- •2 Motivating Example: Car Dealership
- •3 Modelling Elementary Data Types
- •3.1 Orthogonal Data Types
- •3.2 Non-orthogonal Data Types
- •4 Data Type Construction
- •5 Quantum-Based Data Type Constructors
- •5.1 Tuple Data Type Constructor
- •5.2 Set Data Type Constructor
- •6 Conclusion
- •References
- •Incorporating Weights into a Quantum-Logic-Based Query Language
- •1 Introduction
- •2 A Motivating Example
- •5 Logic-Based Weighting
- •6 Related Work
- •7 Conclusion
- •References
- •Searching for Information with Meet and Join Operators
- •1 Introduction
- •2 Background
- •2.1 Vector Spaces
- •2.2 Sets Versus Vector Spaces
- •2.3 The Boolean Model for IR
- •2.5 The Probabilistic Models
- •3 Meet and Join
- •4 Structures of a Query-by-Theme Language
- •4.1 Features and Terms
- •4.2 Themes
- •4.3 Document Ranking
- •4.4 Meet and Join Operators
- •5 Implementation of a Query-by-Theme Language
- •6 Related Work
- •7 Discussion and Future Work
- •References
- •Index
- •Preface
- •Organization
- •Contents
- •Fundamentals
- •Why Should We Use Quantum Theory?
- •1 Introduction
- •2 On the Human Science/Natural Science Issue
- •3 The Human Roots of Quantum Science
- •4 Qualitative Parallels Between Quantum Theory and the Human Sciences
- •5 Early Quantitative Applications of Quantum Theory to the Human Sciences
- •6 Epilogue
- •References
- •Quantum Cognition
- •1 Introduction
- •2 The Quantum Persuasion Approach
- •3 Experimental Design
- •3.1 Testing for Perspective Incompatibility
- •3.2 Quantum Persuasion
- •3.3 Predictions
- •4 Results
- •4.1 Descriptive Statistics
- •4.2 Data Analysis
- •4.3 Interpretation
- •5 Discussion and Concluding Remarks
- •References
- •1 Introduction
- •2 A Probabilistic Fusion Model of Trust
- •3 Contextuality
- •4 Experiment
- •4.1 Subjects
- •4.2 Design and Materials
- •4.3 Procedure
- •4.4 Results
- •4.5 Discussion
- •5 Summary and Conclusions
- •References
- •Probabilistic Programs for Investigating Contextuality in Human Information Processing
- •1 Introduction
- •2 A Framework for Determining Contextuality in Human Information Processing
- •3 Using Probabilistic Programs to Simulate Bell Scenario Experiments
- •References
- •1 Familiarity and Recollection, Verbatim and Gist
- •2 True Memory, False Memory, over Distributed Memory
- •3 The Hamiltonian Based QEM Model
- •4 Data and Prediction
- •5 Discussion
- •References
- •Decision-Making
- •1 Introduction
- •1.2 Two Stage Gambling Game
- •2 Quantum Probabilities and Waves
- •2.1 Intensity Waves
- •2.2 The Law of Balance and Probability Waves
- •2.3 Probability Waves
- •3 Law of Maximal Uncertainty
- •3.1 Principle of Entropy
- •3.2 Mirror Principle
- •4 Conclusion
- •References
- •1 Introduction
- •4 Quantum-Like Bayesian Networks
- •7.1 Results and Discussion
- •8 Conclusion
- •References
- •Cybernetics and AI
- •1 Introduction
- •2 Modeling of the Vehicle
- •2.1 Introduction to Braitenberg Vehicles
- •2.2 Quantum Approach for BV Decision Making
- •3 Topics in Eigenlogic
- •3.1 The Eigenlogic Operators
- •3.2 Incorporation of Fuzzy Logic
- •4 BV Quantum Robot Simulation Results
- •4.1 Simulation Environment
- •5 Quantum Wheel of Emotions
- •6 Discussion and Conclusion
- •7 Credits and Acknowledgements
- •References
- •1 Introduction
- •2.1 What Is Intelligence?
- •2.2 Human Intelligence and Quantum Cognition
- •2.3 In Search of the General Principles of Intelligence
- •3 Towards a Moral Test
- •4 Compositional Quantum Cognition
- •4.1 Categorical Compositional Model of Meaning
- •4.2 Proof of Concept: Compositional Quantum Cognition
- •5 Implementation of a Moral Test
- •5.2 Step II: A Toy Example, Moral Dilemmas and Context Effects
- •5.4 Step IV. Application for AI
- •6 Discussion and Conclusion
- •Appendix A: Example of a Moral Dilemma
- •References
- •Probability and Beyond
- •1 Introduction
- •2 The Theory of Density Hypercubes
- •2.1 Construction of the Theory
- •2.2 Component Symmetries
- •2.3 Normalisation and Causality
- •3 Decoherence and Hyper-decoherence
- •3.1 Decoherence to Classical Theory
- •4 Higher Order Interference
- •5 Conclusions
- •A Proofs
- •References
- •Information Retrieval
- •1 Introduction
- •2 Related Work
- •3 Quantum Entanglement and Bell Inequality
- •5 Experiment Settings
- •5.1 Dataset
- •5.3 Experimental Procedure
- •6 Results and Discussion
- •7 Conclusion
- •A Appendix
- •References
- •Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
- •1 Introduction
- •2 Quantifying Relevance Dimensions
- •3 Deriving a Bell Inequality for Documents
- •3.1 CHSH Inequality
- •3.2 CHSH Inequality for Documents Using the Trace Method
- •4 Experiment and Results
- •5 Conclusion and Future Work
- •A Appendix
- •References
- •Short Paper
- •An Update on Updating
- •References
- •Author Index
- •The Sure Thing principle, the Disjunction Effect and the Law of Total Probability
- •Material and methods
- •Experimental results.
- •Experiment 1
- •Experiment 2
- •More versus less risk averse participants
- •Theoretical analysis
- •Shared features of the theoretical models
- •The Markov model
- •The quantum-like model
- •Logistic model
- •Theoretical model performance
- •Model comparison for risk attitude partitioning.
- •Discussion
- •Authors contributions
- •Ethical clearance
- •Funding
- •Acknowledgements
- •References
- •Markov versus quantum dynamic models of belief change during evidence monitoring
- •Results
- •Model comparisons.
- •Discussion
- •Methods
- •Participants.
- •Task.
- •Procedure.
- •Mathematical Models.
- •Acknowledgements
- •New Developments for Value-based Decisions
- •Context Effects in Preferential Choice
- •Comparison of Model Mechanisms
- •Qualitative Empirical Comparisons
- •Quantitative Empirical Comparisons
- •Neural Mechanisms of Value Accumulation
- •Neuroimaging Studies of Context Effects and Attribute-Wise Decision Processes
- •Concluding Remarks
- •Acknowledgments
- •References
- •Comparison of Markov versus quantum dynamical models of human decision making
- •CONFLICT OF INTEREST
- •Endnotes
- •FURTHER READING
- •REFERENCES
suai.STEAMru/our--contactsH: Science, Technology, Engineering,quantumAgriculture,machine learning
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Quantum-Like Models for Information Retrieval and Decision-Making
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STEAM-H: Science, Technology, Engineering,
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Editors
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Center Leo Apostel for Interdisciplinary Studies
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Department of Mathematics
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Massimo Melucci
Department of Information Engineering
University of Padova
Padova, Italy
Andrei Khrennikov
Linnaeus University, International Center for Mathematical Modeling in Physics and Cognitive Sciences
VŠxjš, Sweden
Bourama Toni
Department of Mathematics
Howard University
Washington, DC, USA
ISSN 2520-193X |
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Preface
All of us are users of information access and retrieval (IAR) systems. Search engines for the World Wide Web (WWW), local or personal media repositories and mailbox search functions are some of the many examples that are becoming a preferred way of acquiring, aggregating, Þltering and interacting with multimodal information as well as of achieving certain information goals especially when interacting with people and companies.
The peculiar difÞculty of IAR is the fact that relevance cannot be precisely and exhaustively described by the data itself; for example, a relevant document may not be precisely and exhaustively described using text even if this text includes or considers all elements or aspects of a topic; a userÕs information need may not be precisely and exhaustively described using a query even if this query is a comprehensive description of the need.
Humans are adept at making reasonably robust and quick decisions about what information is relevant to them, despite the ever-increasing complexity and volume of their surrounding information environment. The literature on document relevance has identiÞed various dimensions of relevance (e.g. topicality, novelty, etc.) that evolve and interact with each other.
However, little is understood about how the dimensions of relevance may interact and how this interaction is contextual and uncertain in nature. The problem becomes more complex and challenging when processing and interacting with multimodal information (e.g. linking an image with a news article, identifying regions or objects of interest within images, tagging video and music clips, etc.), due to the semantic gap between low-level multimedia content features (e.g. pixels, colour histograms, texture, etc.) and high-level meanings as well as the interference on relevance judgements for a document caused by multimodal interactions. Therefore, the current state-of-the-art of IAR is insufÞcient to address the challenges of the dynamic, adaptive and multimodal nature of the information and user interaction context. A genuine theoretical breakthrough is on the contrary necessary.
The quantum mechanical framework may help give up the notions of unimodal features and classical ranking models disconnected from context, thus making the emergence of quantum-like modelling of IAR possible and potentially effective at
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vi |
Preface |
the same level of efÞciency of the traditional modelling. It is believed by the authors of this volume that the quantum theoretical framework can provide the breakthrough in IAR because it can integrate abstract vector spaces, probability spaces and query languages and extend and generalise the classical vector, probability and query languages utilised in IAR.
The chapters of this volume share the aim of Þnding novel ways to address foundational problems that a community of researchers is actively working to deÞne, investigate and evaluate new methods for information processing inspired by quantum theory. For many years, there exists an active research area where psychology, economy, mathematics, information science, computer science and others meet to provide and share methodological and experimental results obtained by the means of quantum theory when applied to disciplines other than physics.
In this context, the European Union funded the project ÒQuantum Information Access TheoryÓ (QUARTZ) which is an Innovative Training Network (ITN) within the Horizon 2020 Marie Sklodowska-Curie Action programme. QUARTZ started from the idea of transferring the scientiÞc research results and expertise from senior researchers in the utilisation of quantum theory in IAR to the junior researchers, thus stimulating the birth and growth of a networked European community of scholars with a larger, stronger and deeper expertise in IAR. We believe that this volume is providing an updated view of the current research in quantum-like modelling of IAR and in particular describes some of the research issues and the solutions thereof investigated within QUARTZ.
Brussels, Belgium |
Diederik Aerts |
VŠxjš, Sweden |
Andrei Khrennikov |
Padua, Italy |
Massimo Melucci |
Washington, DC, USA |
Bourama Toni |
April 2019 |
|
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Contents
Modeling Meaning Associated with Documental Entities: |
|
Introducing the Brussels Quantum Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
1 |
Diederik Aerts, Massimiliano Sassoli de Bianchi, Sandro Sozzo, |
|
and Tomas Veloz |
|
Non-separability Effects in Cognitive Semantic Retrieving . . . . . . . . . . . . . . . . . |
35 |
Aleksey V. Platonov, Igor A. Bessmertny, Evgeny K. Semenenko, |
|
and Alexander P. Alodjants |
|
Introduction to Hilbert Space Multi-Dimensional Modeling. . . . . . . . . . . . . . . . |
41 |
Jerome Busemeyer and Zheng Joyce Wang |
|
Basics of Quantum Theory for Quantum-Like Modeling |
|
Information Retrieval. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
51 |
Andrei Khrennikov |
|
Representing Words in Vector Space and Beyond. . . . . . . . . . . . . . . . . . . . . . . . . . . . |
83 |
Benyou Wang, Emanuele Di Buccio, and Massimo Melucci |
|
Quantum-Based Modelling of Database States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
115 |
Ingo Schmitt, GŸnther Wirsching, and Matthias Wolff |
|
Incorporating Weights into a Quantum-Logic-Based Query Language . . . |
129 |
Ingo Schmitt |
|
Searching for Information with Meet and Join Operators . . . . . . . . . . . . . . . . . . |
145 |
Emanuele Di Buccio and Massimo Melucci |
|
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
169 |
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Contributors
Diederik Aerts Center Leo Apostel for Interdisciplinary Studies, Brussels, Belgium
Department of Mathematics, Brussels Free University, Brussels, Belgium
Alexander P. Alodjants ITMO University, Saint Petersburg, Russia
Igor A. Bessmertny ITMO University, Saint Petersburg, Russia
Jerome Busemeyer Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
Emanuele Di Buccio Department of Information Engineering, University of Padova, Padova, Italy
Andrei Khrennikov Linnaeus University, International Center for Mathematical Modeling in Physics and Cognitive Sciences, VŠxjš, Sweden
Massimo Melucci Department of Information Engineering, University of Padova, Padova, Italy
Aleksey V. Platonov ITMO University, Saint Petersburg, Russia
Massimiliano Sassoli de Bianchi Center Leo Apostel for Interdisciplinary Studies, Brussels, Belgium
Laboratorio di Autoricerca di Base, Barbengo, Switzerland
Ingo Schmitt Brandenburg University of Technology Cottbus-Senftenberg,
Cottbus, Germany
Evgeny K. Semenenko ITMO University, Saint Petersburg, Russia
Sandro Sozzo School of Business and Centre IQSCS, University of Leicester, Leicester, UK
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x |
Contributors |
Tomas Veloz Center Leo Apostel for Interdisciplinary Studies, Brussels, Belgium
Universidad Andres Bello, Departamento Ciencias Biol—gicas, Facultad Ciencias de la Vida, Santiago, Chile
Fundaci—n para el Desarrollo Interdisciplinario de la Ciencia la Tecnolog’a y las Artes, Santiago, Chile
Zheng Joyce Wang School of Communication, Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, OH, USA
Benyou Wang Department of Information Engineering, University of Padova, Padova, Italy
Günther Wirsching Catholic University of EichstŠtt-Ingolstadt, EichstŠtt,
Germany
Matthias Wolff Brandenburg University of Technology Cottbus-Senftenberg,
Cottbus, Germany