Quantum Robotics: A Primer on Current Science and Future Perspectives

Tandon, Prateek; Lam, Stanley; Shih, Ben; Mehta, Tanay; Mitev, Alex; Ong, Zhiyang

doi:10.2200/s00746ed1v01y201612qmc010

Cited by 8 publications

(5 citation statements)

References 172 publications

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“…Measurement is an important way to transform the illusory world of quantum into the real world, and human consciousness itself is transforming various possibilities into reality. This makes many scientists and philosophers think that quantum probability can not only describe the microscopic particle world, but also describe human consciousness and cognitive behavior 14 .…”

Section: Methodsmentioning

confidence: 99%

“…What is gratifying is that quantum theory originated in the field of microscopic physics has been extended in the past two decades and it made great progress in many non-physical and macro fields such as cognition, decision making, information, communication, computing, etc. It has not only formed an increasingly mature theoretical system, but also been increasingly widely applied 14 . In particular, the initial quantum inkling in the field of mobile robots, being most closely related to unmanned driving technology 14 , allows us to see the potential and possibility of applying quantum theory to solve the cognitive problems of autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

“…It has not only formed an increasingly mature theoretical system, but also been increasingly widely applied 14 . In particular, the initial quantum inkling in the field of mobile robots, being most closely related to unmanned driving technology 14 , allows us to see the potential and possibility of applying quantum theory to solve the cognitive problems of autonomous driving. Quantum theory provides a new way to study the uncertain behavior (including irrational behavior) of human traffic participants and their interaction.…”

Section: Introductionmentioning

confidence: 99%

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Research on quantum cognition in autonomous driving

Song

Wang

et al. 2022

Sci Rep

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Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on quantum cognition in autonomous driving

Song

Wang

et al. 2022

Sci Rep

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“…In each state, after choosing an action a reward is received, which can, through a transition probability function, determine the state at the next decision epoch [1]. According to Reference [16], a Markov Decision Process (MDP) is a model of an agent operating in an uncertain but observable world. The definition of Markov Decision Process presented here and the notations are according to the ones used in Reference [16] and Reference [17].…”

Section: Formal Mdp Definitionmentioning

confidence: 99%

A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

Akbari

Unland

2019

MAKE

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Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decision Makers (DMs), such RL algorithms may be designed for single-agent systems or multi-agent systems that either consist of agents with individual goals and decision making capabilities, which are influenced by other agent’s decisions, or behave as a swarm of agents that collaboratively learn a single objective. Many studies have been conducted in this area; however, when concentrating on available swarm RL algorithms, one obtains a clear view of the areas that still require attention. Most of the studies in this area focus on homogeneous swarms and so far, systems introduced as Heterogeneous Swarms (HetSs) merely include very few, i.e., two or three sub-swarms of homogeneous agents, which either, according to their capabilities, deal with a specific sub-problem of the general problem or exhibit different behaviors in order to reduce the risk of bias. This study introduces a novel approach that allows agents, which are originally designed to solve different problems and hence have higher degrees of heterogeneity, to behave as a swarm when addressing identical sub-problems. In fact, the affinity between two agents, which measures the compatibility of agents to work together towards solving a specific sub-problem, is used in designing a Heterogeneous Swarm RL (HetSRL) algorithm that allows HetSs to solve the intended SDMPs.

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“…The current work is aimed at such a branch of quantum technologies. Namely, we address the concept of a quantum robot as a fundamental basis for the development quantum CPC systems (Tandon et al, 2017;Ivancevic et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

Gonçalves

2019

Neuroquantology

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We are using, within the technological context, the concept of cyber-physicalcognitive system worked in Ivancevic (et al., 2018), which addresses the integration of the ICT's in physical systems (the cyber-physical technological transformation) and of the CogTech (cyber-physical-cognitive transformation). Since the Fourth Industrial Revolution involves the merge of the ICTs with the CogTech, the CogTech pillar is explicitly addressed in the acronym CPC (cyber-physical-cognitive). transformations, CPC systems form the basis for a distributed intelligent artificial cognition and includes smart devices embedded in different physical systems, linked to the Internet (Schwab, 2017), such that the current technological transformation can be described by an acronym SDO (Smart, Digital, Online). However, quantum technologies, in particular quantum computers, threaten the current infrastructure of this technological basis, in the sense that quantum computers can solve cryptographic problems that are central for secure communications, making cryptographic quantum resistance one of the major problems to the SDO-based transformation (

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Quantum Robotics: A Primer on Current Science and Future Perspectives

Cited by 8 publications

References 172 publications

Research on quantum cognition in autonomous driving

Research on quantum cognition in autonomous driving

A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

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