5,035 research outputs found
Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile-robot navigation problems is presented and tested in both the
real and virtual domains. The LTL phase consists of rapid simulations that use
a Genetic Algorithm to derive diverse sets of behaviours, encoded as variable
sets of attributes, and the STL phase is an idiotypic Artificial Immune System.
Results from the LTL phase show that sets of behaviours develop very rapidly,
and significantly greater diversity is obtained when multiple autonomous
populations are used, rather than a single one. The architecture is assessed
under various scenarios, including removal of the LTL phase and switching off
the idiotypic mechanism in the STL phase. The comparisons provide substantial
evidence that the best option is the inclusion of both the LTL phase and the
idiotypic system. In addition, this paper shows that structurally different
environments can be used for the two phases without compromising
transferability.Comment: 40 pages, 12 tables, Journal of Applied Soft Computin
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on
Artificial Immune Systems (ICARIS2008), Phuket, Thailan
Dynamic Behavior Arbitration of Autonomous Mobile Robots Using Immune Networks
PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATIO
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
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