3,773 research outputs found
Delegated causality of complex systems
A notion of delegated causality is introduced here. This subtle kind of causality is dual to interventional causality. Delegated causality elucidates the causal role of dynamical systems at the “edge of chaos”, explicates evident cases of downward causation, and relates emergent phenomena to Gödel’s incompleteness theorem. Apparently rich implications are noticed in biology and Chinese philosophy. The perspective of delegated causality supports cognitive interpretations of self-organization and evolution
Interactivist approach to representation in epigenetic agents
Interactivism is a vast and rather ambitious philosophical
and theoretical system originally developed by Mark
Bickhard, which covers plethora of aspects related to
mind and person. Within interactivism, an agent is
regarded as an action system: an autonomous, self-organizing,
self-maintaining entity, which can exercise
actions and sense their effects in the environment it
inhabits. In this paper, we will argue that it is especially
suited for treatment of the problem of representation in
epigenetic agents. More precisely, we will elaborate on
process-based ontology for representations, and will
sketch a way of discussing about architectures for
epigenetic agents in a general manner
Embodied Robot Models for Interdisciplinary Emotion Research
Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe
From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)
This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Embodied Cognitive Science of Music. Modeling Experience and Behavior in Musical Contexts
Recently, the role of corporeal interaction has gained wide recognition within cognitive musicology. This thesis reviews evidence from different directions in music research supporting the importance of body-based processes for the understanding of music-related experience and behaviour. Stressing the synthetic focus of cognitive science, cognitive science of music is discussed as a modeling approach that takes these processes into account and may theoretically be embedded within the theory of dynamic systems. In particular, arguments are presented for the use of robotic devices as tools for the investigation of processes underlying human music-related capabilities (musical robotics)
Behavioural robustness and the distributed mechanisms hypothesis
A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent ‘brain’, body, and environment, rather than warranted exclusively by organisms’ internal mechanisms. Distribution is operationally defined here based on perturbation analyses.
Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goalseeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching. The second model evolves situated, dynamically limited agents exhibiting minimalcognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitivebehavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the systemin-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution.
In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies
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