The Stanford Encyclopedia of Philosophy
01 Jan 2013-
About: The article was published on 2013-01-01 and is currently open access. It has received 3599 citations till now. The article focuses on the topics: Encyclopedia.
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TL;DR: This paper analyzes the literature from the point of view of swarm engineering and proposes two taxonomies: in the first taxonomy, works that deal with design and analysis methods are classified; in the second, works according to the collective behavior studied are classified.
Abstract: Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
1,405 citations
Book•
26 Jul 2012TL;DR: The foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory, 'contextuality' and 'quantum entanglement', are introduced, which allow cognitive phenomena to be modeled in non-reductionist ways.
Abstract: Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.
745 citations
TL;DR: In this article, the authors describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain, and discuss the fundamental issue of quantum generalizations of learning and AI concepts.
Abstract: Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
684 citations
TL;DR: It is argued that an embodiment view of conceptual representations realized as distributed sensory and motor cell assemblies that are complemented by supramodal integration brain circuits may serve as a theoretical framework to guide future research on concrete and abstract concepts.
653 citations
TL;DR: 3 studies that explore the role of mindsets in the context of stress suggest that stress mindset is a distinct and meaningful variable in determining the stress response.
Abstract: This article describes 3 studies that explore the role of mindsets in the context of stress. In Study 1, we present data supporting the reliability and validity of an 8-item instrument, the Stress Mindset Measure (SMM), designed to assess the extent to which an individual believes that the effects of stress are either enhancing or debilitating. In Study 2, we demonstrate that stress mindsets can be altered by watching short, multimedia film clips presenting factual information biased toward defining the nature of stress in 1 of 2 ways (stress-is-enhancing vs. stress-is-debilitating). In Study 3, we demonstrate the effect of stress mindset on physiological and behavioral outcomes, showing that a stress-is-enhancing mindset is associated with moderate cortisol reactivity and high desire for feedback under stress. Together, these 3 studies suggest that stress mindset is a distinct and meaningful variable in determining the stress response.
570 citations