21,271 research outputs found
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
Advances in architectural concepts to support distributed systems design
This paper presents and discusses some architectural concepts for distributed systems design. These concepts are derived from an analysis of limitations of some currently available standard design languages. We conclude that language design should be based upon the careful consideration of architectural concepts. This paper aims at supporting designers by presenting a methodological design framework in which they can reason about the design and implementation of distributed systems. The paper is also meant for language developers and formalists by presenting a collection of architectural concepts which deserve consideration for formal support
Qualitative inequalities for squared partial correlations of a Gaussian random vector
We describe various sets of conditional independence relationships,
sufficient for qualitatively comparing non-vanishing squared partial
correlations of a Gaussian random vector. These sufficient conditions are
satisfied by several graphical Markov models. Rules for comparing degree of
association among the vertices of such Gaussian graphical models are also
developed. We apply these rules to compare conditional dependencies on Gaussian
trees. In particular for trees, we show that such dependence can be completely
characterized by the length of the paths joining the dependent vertices to each
other and to the vertices conditioned on. We also apply our results to
postulate rules for model selection for polytree models. Our rules apply to
mutual information of Gaussian random vectors as well.Comment: 21 pages, 13 figure
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