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Summary
In future, machine
agents will be able to communicate among themselves and with humans
with the flexibility of human language. Recent research in
autonomous cognitive systems has focused on the close integration
(grounding) of communication with perception, categorization and
action. Such systems are designed to assist humans in a variety of
situations including everyday tasks (e.g. service/household
robotics), and highly-specialised situations such as with autonomous
systems for defence (e.g. collaborative and multi-agent distributed
tasks for exploration and navigation in unknown terrains).
The
project has the aim of combining research on grounded autonomous
cognitive systems with Modelling Field Theory (MFT; Perlovsky 2001).
This will permit the integration of bottom-up information (e.g.
phonetic signals, tactile and perceptual input) and internal
concept-models that the agent creates during its interaction with
the world. The combination of MFT with grounded agents will deal
with the combinatorial complexity barrier currently faced by
linguistic cognitive systems.
The main objective consists in the
investigation of the benefits of MFT for the development and scaling
up of communication abilities in autonomous systems. This will be
achieved through a series of simulation robotic experiments on the
integration of language and cognitive abilities in collaborative
tasks.
The potential
impact of this research for the development of intelligent
systems is great, also in the field of defence interests.
Cognitive systems are essential for integrated multi-platform
systems capable of sensing and communicating. In future systems,
robots and autonomous agents will be able to learn language and
world understanding from humans. In the area of internet/text
search engines the capability of truly "understanding" the
language query and corpora being used will permit the design of
more efficient search and data-mining systems. In the area of
intelligent agents for defence, the design of cognitive systems
able to develop autonomously their own grounded lexicons will be
beneficial in collaborative and distributed tasks. (e.g.
multi-agent exploration and navigation in unknown terrains,
etc.)
People
Dr. Leonid
Perlovsky
Prof.
Angelo Cangelosi
Mr.
Vadim Tikhanoff
Collaborator
Prof.
Jose Fernando Fontanari
Related
links
Centre for
Interactive Intelligent Systems
Adaptive
Behaviour and Cognition Research Group
IJCNN2006 – Special Session on “Modeling the Evolution and
Acquisition of Language”
The
6th International Conference on the Evolution of Language
(Evolang6)
The
16th International Conference on Artificial Neural Networks
(ICANN 06)
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