61,930 research outputs found
Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks
Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap even further by directly associating individual neuron
activity with behavior. However, most computational models that have analyzed
embodied behaviors have employed non-spiking neurons. On the other hand,
computational models that employ spiking neural networks tend to be restricted
to disembodied tasks. We show for the first time the artificial evolution and
TE-analysis of embodied spiking neural networks to perform a
cognitively-interesting behavior. Specifically, we evolved an agent controlled
by an Izhikevich neural network to perform a visual categorization task. The
smallest networks capable of performing the task were found by repeating
evolutionary runs with different network sizes. Informational analysis of the
best solution revealed task-specific TE-network clusters, suggesting that
within-task homogeneity and across-task heterogeneity were key to behavioral
success. Moreover, analysis of the ensemble of solutions revealed that
task-specificity of TE-network clusters correlated with fitness. This provides
an empirically testable hypothesis that links network structure to behavior.Comment: Camera ready version of accepted for GECCO'1
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Metaphor and Materiality in Early Prehistory
In this paper we argue for a relational perspective based on metaphorical rather than semiotic understandings of human and hominin1 material culture. The corporeality of material culture and thus its role as solid metaphors for a shared experience of embodiment precedes language in the archaeological record. While arguments continue as to both the cognitive abilities that underpin symbolism and the necessary and sufficient evidence for the identification of symbolic material culture in the archaeological record, a symbolic approach will inevitably restrict the available data to sapiens or even to literate societies. However, a focus on material culture as material metaphor allows the consideration of the ways in which even the very earliest archaeological record reflects hominins’ embodied, distributed relationships with heterogeneous forms of agent, as will be demonstrated by two case studies
Multifunctionality in embodied agents: Three levels of neural reuse
The brain in conjunction with the body is able to adapt to new environments
and perform multiple behaviors through reuse of neural resources and transfer
of existing behavioral traits. Although mechanisms that underlie this ability
are not well understood, they are largely attributed to neuromodulation. In
this work, we demonstrate that an agent can be multifunctional using the same
sensory and motor systems across behaviors, in the absence of modulatory
mechanisms. Further, we lay out the different levels at which neural reuse can
occur through a dynamical filtering of the brain-body-environment system's
operation: structural network, autonomous dynamics, and transient dynamics.
Notably, transient dynamics reuse could only be explained by studying the
brain-body-environment system as a whole and not just the brain. The
multifunctional agent we present here demonstrates neural reuse at all three
levels.Comment: Accepted at Cognitive Science Conference, 201
- …