- •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.ru/our-contacts |
quantum machine learning |
Preface
Quantum Interaction (QI) is an emerging interdisciplinary field of science. It proposes applications of quantum theory to a large variety of domains from psychology, economics, semantic and memory, natural language processing, cognition, information retrieval, biology, and political science. The applications addressed typically operate at a macroscopic scale and could not be considered quantum in a quantum mechanical sense. However, they share key properties with quantum systems. These include non-commutativity of measurement, indeterminacy, non-separability, contextuality, and harmonic oscillations. QI thus refers to the use of the quantum mathematical, conceptual, or probabilistic structures outside of physics. Since its inception in 2007, QI has evolved from nearly exclusively theoretical and conceptual contributions to more applied works including lab experiments.
QI 2018, the 11th International Conference on Quantum Interactions was part of a series of international conferences. This now traditional conference started in 2007 as part of the Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposia at Stanford University. For the second time the conference was held in France (QI 2012 was in Paris). The 11th conference took place during September 3–5, 2018, in Nice. It was hosted by Nice Graduate School of Management at the Sophia Antipolis University.
In this year’s conference we had many distinguished speakers, and we are happy to have contributions to this volume from two of our keynote speakers. Prof. Michel Bitbol, Directeur de recherche CNRS at Ecole Normale Supérieure, Paris, France, offers a philosophical and historical perspective that unveils deep reasons for a quantum approach in human sciences. Prof. B. P. F. Jacobs, from the Institute for Computing and Information Sciences (ICIS), Correctness Digital Security Group at Radboud University Nijmegen, The Netherlands, provides a review of the questions related to updating in the classical and quantum context and introduces challenging research issues related to probabilistic logic.
The conference had an affiliated workshop entitled Workshop on Compositional Approaches in Physics, NLP, and Social Sciences (CAPNS 2018). Its proceedings appeared in ENTCS.
We would like to take the opportunity to thank everybody who made this symposium possible: the Steering Committee, the Program Committee members for their reviewing job, the proceedings and the publicity chairs, those responsible for the website design and management, and all the conference participants and presenters. We are grateful for the support given by the University of Nice Sophia Antipolis.
June 2019 |
Bob Coecke |
|
Ariane Lambert-Mogiliansky |
suai.ru/our-contacts |
quantum machine learning |
Organization
Program Chairs
Bob Coecke |
University of Oxford, UK |
Ariane |
Paris School of Economics, France |
Lambert-Mogiliansky |
|
Steering Committee
Peter Bruza |
Queensland University of Technology, Australia |
Trevor Cohen |
University of Texas at Houston, USA |
Bob Coecke |
University of Oxford, UK |
Ariane |
Paris School of Economics, France |
Lambert-Mogiliansky |
|
Dominic Widdows |
Grab Technologies Inc., USA |
Program Committee
Irina Basieva |
LNU, Sweden |
Bob Coecke |
University of Oxford, UK |
Trevor Cohen |
University of Texas, Houston, USA |
Ehtibar Dzhafarov |
Purdue University, USA |
Bart Jacobs |
Radboud University, The Netherlands |
Chris Heunen |
The University of Edinburgh, UK |
Dominic Horsman |
University of Durham, UK |
Dimitri Kartsaklis |
Apple Inc., UK |
Kirsty Kitto |
University of Technology, Sydney |
Andrei Khrennikov |
Linnaeus University, Sweden |
Ariane |
Paris School of Economics, France |
Lambert-Mogiliansky |
|
Bill Lawless |
Paine College, USA |
Martha Lewis |
University of Amsterdam, The Netherlands |
Dan Marsden |
University of Oxford, UK |
Simon Perdrix |
CNRS, University of Grenoble, France |
Emmanuel Pothos |
City University London, UK |
Quanlong Wang |
University of Oxford, UK |
Local Committee |
|
Sébastien Duchêne |
University of Nice Sophia Antipolis, France |
Ismaël Rafaï |
University of Nice Sophia Antipolis, France |
Laurence Gervasoni |
University of Nice Sophia Antipolis, France |
suai.ru/our-contacts |
quantum machine learning |
Contents
Fundamentals |
|
Why Should We Use Quantum Theory? The Case of Human Sciences . . . . . |
3 |
Michel Bitbol |
|
Quantum Cognition |
|
The Power of Distraction: An Experimental Test of Quantum Persuasion . . . . |
25 |
Ariane Lambert-Mogiliansky, Adrian Calmettes, and Hervé Gonay |
|
Are Decisions of Image Trustworthiness Contextual? A Pilot Study . . . . . . . |
39 |
Peter D. Bruza and Lauren Fell |
|
Probabilistic Programs for Investigating Contextuality in Human |
|
Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
51 |
Peter D. Bruza and Peter Wittek |
|
Episodic Source Memory over Distribution by Quantum-Like |
|
Dynamics – A Model Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
63 |
J. B. Broekaert and J. R. Busemeyer |
|
Decision-Making |
|
Balanced Quantum-Like Model for Decision Making. . . . . . . . . . . . . . . . . . |
79 |
Andreas Wichert and Catarina Moreira |
|
Introducing Quantum-Like Influence Diagrams for Violations |
|
of the Sure Thing Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
91 |
Catarina Moreira and Andreas Wichert |
|
Cybernetics and AI |
|
Fuzzy Logic Behavior of Quantum-Controlled Braitenberg |
|
Vehicle Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
111 |
Rebeca Araripe Furtado Cunha, Naman Sharma, Zeno Toffano, |
|
and François Dubois |
|
Moral Dilemmas for Artificial Intelligence: A Position Paper |
|
on an Application of Compositional Quantum Cognition . . . . . . . . . . . . . . . |
123 |
Camilo M. Signorelli and Xerxes D. Arsiwalla |
|
suai.ru/our-contacts |
quantum machine learning |
viii Contents
Probability and Beyond
Density Hypercubes, Higher Order Interference and Hyper-decoherence:
A Categorical Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Stefano Gogioso and Carlo Maria Scandolo
Information Retrieval
Investigating Non-classical Correlations Between Decision
Fused Multi-modal Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Dimitris Gkoumas, Sagar Uprety, and Dawei Song
Investigating Bell Inequalities for Multidimensional Relevance
Judgments in Information Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Sagar Uprety, Dimitris Gkoumas, and Dawei Song
Short Paper
An Update on Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Bart Jacobs
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . |
193 |