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IEEE Congress on Evolutionary Computation (CEC)

Fuzzy quantum circuits to model emotional behaviors of humanoid robots

Arushi Raghuvanshi, Marek A. Perkowski

Published Jul 1, 2010• 24 citations
Publisher

Abstract

In this paper, we propose the concepts that apply quantum circuits to model Fuzzy Sets, thereby creating a new method to model behaviors for a humanoid robot. It is similar to the standard fuzzy sets from two points of view -1) of measured probabilities of state |1〉, and 2) that of the normalized measurement in a quantum ensemble computer. However, one can also look at the internal quantum states created by the operators of the new model. This extends fuzzy sets from [0, 1] interval to some other domain, similar to complex fuzzy logic, but also brings superposed, entangled and parallel quantum behaviors. The standard fuzzy logic aspect (external) of the new model maps the numbers in interval [0,1] to points on one meridian of the Bloch Sphere and after measurement, the results map back to numbers from interval [0,1]. However, the other, more important, aspect of this model (internal) is the operation on arbitrary quantum states, and the realization of additional states via phase measurements. We discuss and illustrate how this model can be used to represent a hidden state of reasoning agents or an emotional state of a humanoid robot.

DOI
10.1109/cec.2010.5586038

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In this paper, we propose the concepts that apply quantum circuits to model Fuzzy Sets, thereby creating a new method to model behaviors for a humanoid robot. It is similar to the standard fuzzy sets from two points of view -1) of measured probabilities of state |1〉, and 2) that of the normalized measurement in a quantum ensemble computer. However, one can also look at the internal quantum states created by the operators of the new model. This extends fuzzy sets from [0, 1] interval to some other domain, similar to complex fuzzy logic, but also brings superposed, entangled and parallel quantum behaviors. The standard fuzzy logic aspect (external) of the new model maps the numbers in interval [0,1] to points on one meridian of the Bloch Sphere and after measurement, the results map back to numbers from interval [0,1]. However, the other, more important, aspect of this model (internal) is the operation on arbitrary quantum states, and the realization of additional states via phase measurements. We discuss and illustrate how this model can be used to represent a hidden state of reasoning agents or an emotional state of a humanoid robot.
In this article we investigate a way in which quantum computing can be used to extend the class of fuzzy sets. The core idea is to see states of a quantum register as characteristic functions of quantum fuzzy subsets of a given set. As the real unit interval is embedded in the Bloch sphere, every fuzzy set is automatically a quantum fuzzy set. However, a generic quantum fuzzy set can be seen as a (possibly entangled) superposition of many fuzzy sets at once, offering new opportunities for modeling uncertainty. After introducing the main framework of quantum fuzzy set theory, we analyze the standard operations of fuzzification and defuzzification from our viewpoint. We conclude this preliminary paper with a list of possible applications of quantum fuzzy sets to pattern recognition, as well as future directions of pure research in quantum fuzzy set theory.
Utilising the properties of quantum mechanics, i.e., entanglement, parallelism, etc., a quantum structure is proposed for representing and manipulating emotion space of robots. This quantum emotion space (QES) provides a mechanism to extend emotion interpretation to the quantum computing domain whereby fewer resources are required and, by using unitary transformations, it facilitates easier tracking of emotion transitions over different intervals in the emotion space. The QES is designed as an intuitive and graphical visualisation of the emotion state as a curve in a cuboid, so that an “emotion sensor” could be used to track the emotion transition as well as its manipulation. This ability to use transition matrices to convey manipulation of emotions suggests the feasibility and effectiveness of the proposed approach. Our study is primarily influenced by two developments. First, the massive amounts of data, complexity of control, planning and reasoning required for today’s sophisticated automation processes necessitates the need to equip robots with powerful sensors to enable them adapt and operate in all kinds of environments. Second, the renewed impetus and inevitable transition to the quantum computing paradigm suggests that quantum robots will have a role to play in future data processing and human-robot interaction either as standalone units or as part of larger hybrid systems. The QES proposed in this study provides a quantum mechanical formulation for quantum emotion as well as a platform to process, track, and manipulate instantaneous transitions in a robot’s emotion. The new perspective will open broad areas, such as applications in emotion recognition and emotional intelligence for quantum robots.
Today's robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture.However, the amount of data increases continuously -a fact which pushes classical computers that control these robots to their limits.In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently.In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings.Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes.This paper promotes a hybrid-controlled robot using the combination of a classical computer-and quantum computer.With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.
Understanding human decision-making in uncertain and dynamic scenarios is essential for advancing technologies in AI, predictive analytics, and behavioral simulations. Fuzzy logic provides an approach for representing gradual transitions and capturing the nuances of human behaviors, making it a foundation for tackling such challenges. This study explores a hybrid approach integrating fuzzy logic, quantum computing, and machine learning to model complex decision-making scenarios. Using a case study of the Prisoner’s Dilemma, the methodology captures nuanced human behaviors and uncertainties through fuzzy membership degrees while leveraging quantum properties like superposition and entanglement to simulate dynamic agent interactions. The resulting simulations generated a comprehensive dataset, which was subsequently applied in a quantum-based artificial intelligence model, demonstrating the capability of this integrated approach to uncover complex patterns in uncertain systems. This work offers a foundation for advancing computational models in decision support systems and predictive analytics, highlighting the promise of emerging technologies in addressing challenges in multidimensional and interdependent environments.
In this discussion paper, I give an account for non-experts of, arguably, quantum dynamics in the brain, underlying the modeling of affective behavior of humanoid robots in the making. Outreach to the larger audience inevitably leads to abbreviations and simplifications; nonetheless, I try to offer the backgrounds of why it is important to study the virtual aspects of ‘people’ we meet online, what dimensions play a role in assessing such creatures, what humanities, psychology, communication, and computer science provide to help us understand how we become attached to non-existent others. As its capstone for the time being, an approach derived from physics is discussed for a robot to handle emotional ambiguity and vagueness of its user. Two computational models, Silicon and Quantum Coppélia, are discussed for their potential and limitations in explaining human affective behavior while dealing with mediated characters.