8,250 research outputs found
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework
Simulation is widely used to make model-based predictions, but few approaches
have attempted this technique in dynamic physical environments of medium to
high complexity or in general contexts. After an introduction to the cognitive
science concepts from which this work is inspired and the current development
in the use of simulation as a decision-making technique, we propose a generic
framework based on theory of mind, which allows an agent to reason and perform
actions using multiple simulations of automatically created or externally
inputted models of the perceived environment. A description of a partial
implementation is given, which aims to solve a popular game within the
IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison
with the competition benchmark. Finally, future developments regarding the
framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird
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
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Practopoiesis: Or how life fosters a mind
The mind is a biological phenomenon. Thus, biological principles of
organization should also be the principles underlying mental operations.
Practopoiesis states that the key for achieving intelligence through adaptation
is an arrangement in which mechanisms laying a lower level of organization, by
their operations and interaction with the environment, enable creation of
mechanisms lying at a higher level of organization. When such an organizational
advance of a system occurs, it is called a traverse. A case of traverse is when
plasticity mechanisms (at a lower level of organization), by their operations,
create a neural network anatomy (at a higher level of organization). Another
case is the actual production of behavior by that network, whereby the
mechanisms of neuronal activity operate to create motor actions. Practopoietic
theory explains why the adaptability of a system increases with each increase
in the number of traverses. With a larger number of traverses, a system can be
relatively small and yet, produce a higher degree of adaptive/intelligent
behavior than a system with a lower number of traverses. The present analyses
indicate that the two well-known traverses-neural plasticity and neural
activity-are not sufficient to explain human mental capabilities. At least one
additional traverse is needed, which is named anapoiesis for its contribution
in reconstructing knowledge e.g., from long-term memory into working memory.
The conclusions bear implications for brain theory, the mind-body explanatory
gap, and developments of artificial intelligence technologies.Comment: Revised version in response to reviewer comment
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