- •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 |
120 R. A. F. Cunha et al.
Table 2. Doubt emotion truth table
|xf |
μL |
μR |
Behavior |
|00 |
0 |
0 |
No movement |
|01 |
1 |
1 |
Goes straight |
|10 |
0 |
1 |
Turns to the left |
|11 |
0 |
0 |
No movement |
5 Quantum Wheel of Emotions
The concept of “wheel of emotions” introduced by Plutchik et al. [7] pictures the idea that a complex emotional state is the composition of elementary emotions. This picture can be interpreted in a quantum-like way using the quantum state vector |ψ . Each qubit (2-dimensional) quantum state can be mapped to a point on the surface of the Bloch unit sphere:
|
θ |
φ |
θ |
|
|
|ψ = cos( |
|
) |0 + e−i 2 sin( |
|
) |1 |
(19) |
2 |
2 |
||||
where φ and θ are the spherical angles. In order to simplify interpretation, the coe cients (associated with the degree of truth) multiplying the base states are taken as real numbers. The points of the vector are thus placed on a circle corresponding to a quantum wheel of emotions.
We summarized our simulation results by associating the di erent observed behaviors to a sector in the wheel as shown in Fig. 4. Other emotions not presented in Fig. 3 have been simulated such as for example: Interest, Curiosity, Distraction, Fear, Worship and Sadness. The two latter ones have been obtained using a circuit that combines the standard quantum gates H and CNOT.
The quantum wheel of emotions thus allows a continuous set of emotional states. A small perturbation in the angle of the input state |ψ due to environmental factors, even if still inside the same emotional sector, will correspond to small changes in the vehicle’s behavior. The measurement of the input state implies the collapse of |ψ to a specific point of the wheel, and thus we can say that, in this aspect, the vehicle behaves as a quantum-like system. Furthermore, the fuzzy aspect of the system arouses naturally since the collapse can involve any state belonging to the continuous surface of the wheel. These observations can be compared to the observed similarities between neural network models and quantum systems. In particular, it has been suggested that it is possible to implement quantum learning algorithms dedicated to fuzzy qubits [8] where the weighted sums of inputs of a neuron correspond to the superposition of quantum states at the input of a quantum circuit and the quantum wave function collapse corresponds to the threshold activation of a neuron.
suai.ru/our-contacts |
quantum machine learning |
Fuzzy Logic Behavior of Quantum-Controlled Braitenberg Vehicle Agents |
121 |
Fig. 4. Quantum Wheel of Emotions. For a given emotion, we associate quantum operators controlling the speed of the left and the right wheel respectively. (Anger)
FB , FA ; (Passion) FB = A , FA = B ; (Love) F ¯, F ¯ ; (Interest) F ¯ , F ¯; (Curios-
A B B A
ity) FA = B , FB = A ; (Distraction)FA = B , FA XOR B; (Apprehension) FB = A , FA XOR B; (Worship) H H , FB ; (Sadness) CNOT, CNOT; (Fear) FA , FB .
6 Discussion and Conclusion
The purpose of this research is to show the multiplicity of behaviors obtained by using fuzzy logic along with quantum logical gates in the control of simple Braitenberg Vehicle agents. The number of cases becomes intractable in simple theoretical approaches with increasing complexity. A computer simulation is mandatory and allows us to abstract the complexity by observing the motion of the vehicles and use it for illustrative purposes. At the same time, we see that by changing and combining di erent quantum control gates we can tune small changes in the vehicle’s behavior, and hence get specific features around the main basic BV emotions of Fear, Aggression, Love and Explore. By tweaking these quantum gates, one can also obtain a vehicle that has a mixture of multiple emotions.
Further extensions to this project can be imagined. Currently, when the vehicles collide, their respective control operators could change in order to reflect a quantum-like entanglement behavior due to interaction. It would be interesting to entangle the vehicles so that the behavior of one vehicle depends upon the current state of the environment from the perspective of other vehicles even after they separate after the collision. It could also be interesting to explore the Braitenberg vehicles using di erent types of stimuli (instead of only light) and sensors. Also a formalization of the quantum BV components as quantum neural networks could lead to new investigation strategies and could benefit researches in machine learning algorithms related to emotion analysis.
suai.ru/our-contacts |
quantum machine learning |
122 R. A. F. Cunha et al.
7 Credits and Acknowledgements
The first coauthor is under the Brafitec scholarship, CAPES Foundation, Ministry of Education of Brazil, Brasilia 70.040-020, Brazil.
We want to thank the AFSCET (Association Fran¸caise de Science des Syst`emes) for permitting us to present the idea of this work at the WOSC (World Organisation of Systems and Cybernetics) 2017 Congress in Rome Italy.
We are grateful to Francesco Galofaro of LUB for fruitful discussions on logic and semantics and their link to quantum theory and for pointing out that the late Professor Valentin von Braitenberg was one of the academic founding members of LUB (Libera Universit`a di Bolzano/Freie Universit¨at Bozen, Italy).
References
1.Braitenberg, V.: Vehicles - Experiments in Synthetic Psychology. MIT Press, Cambridge (1986)
2.Benio , P.: Quantum robots and environments as a first step towards a quantum mechanical description of systems that are aware of their environment and make decisions. Phys. Rev. A 58(2), 893–904 (1998)
3.Raghuvanshi, A., Fan, Y., Woyke, M., Perkowski, M.: Quantum robots for teenagers. In: 37th International Symposium on Multiple-Valued Logic ISMVL (2007)
4.Dubois, F., To ano, Z.: Eigenlogic: a quantum view for multiple-valued and fuzzy systems. In: de Barros, J.A., Coecke, B., Pothos, E. (eds.) QI 2016. LNCS, vol. 10106, pp. 239–251. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52289-0 19
5.Kagan, E., Rybalov, A., Sela, A., Siegelmann, H.: Probabilistic control and swarm dynamics in mobile robots and ants. In: Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, January, pp. 11–13 (2014)
6.To ano, Z., Dubois, F.: Interpolating binary and multivalued logical quantum gates. In: Proceedings of the 4th International Electronic Conference on Entropy and Its Applications. MDPI Proceedings, vol. 2, no. 4, p. 152 (2018)
7.Plutchik, R.: The nature of emotions. Am. Sci. 89(4), 334–350 (2001)
8.Hannachi, M.S., Dong, F., Hirota, K.: Emulating quantum interference and quantum associative memory using fuzzy qubits. In: Proceedings of the IEEE International Conference on Computational Cybernetics ICCC 2007, pp. 39–45 (2007)
suai.ru/our-contacts |
quantum machine learning |
Moral Dilemmas for Artificial Intelligence: A Position Paper on an Application
of Compositional Quantum Cognition
Camilo M. Signorelli1,2,5(&) 
and Xerxes D. Arsiwalla3,4,5
1 Department of Computer Science, University of Oxford, Oxford, UK
camiguel@uc.cl
2 Cognitive Neuroimaging Lab, INSERM U992, NeuroSpin,
Gif-sur-Yvette, France
3Barcelona Institute of Science and Technology, Barcelona, Spain 4 Institute for Bioengineering of Catalonia, Barcelona, Spain
5 Pompeu Fabra University, Barcelona, Spain
Abstract. Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and take complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and “apparently” biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.
Keywords: Moral dilemmas Moral test Turing test Artificial Intelligence Compositional semantics Natural language Quantum cognition
1 Introduction
Moral dilemmas and a general intelligence definition have been recently suggested as an alternative to current AI tests [1]. Usually, Intelligence is interpreted regarding particular and efficient behaviours which can be measured in terms of performing or not these behaviours. One example is the Turing test [2], in fact, the first approach grounded on human behaviour and the most well-known and controversial test for AI. Other examples are challenging machines in games like chess or Go [3], and testing AI programs with dilemmas [4], theoretically, demanding a more complex level of information processing. Nevertheless, all these approaches lack a general definition of intelligence as a minimal requirement to measure intelligence [5–7], and restrict AI only to humankind “intelligence” without including key features of a true human intelligence. Therefore, this position paper is going to shortly introduce a new strategy
© Springer Nature Switzerland AG 2019
B. Coecke and A. Lambert-Mogiliansky (Eds.): QI 2018, LNCS 11690, pp. 123–138, 2019. https://doi.org/10.1007/978-3-030-35895-2_9