Evolving internal memory strategies for the woods problems
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- @InProceedings{Yim:2012:ICCAS,
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author = "Hyungu Yim and DaeEun Kim",
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booktitle = "12th International Conference on Control, Automation
and Systems (ICCAS 2012)",
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title = "Evolving internal memory strategies for the woods
problems",
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year = "2012",
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pages = "366--369",
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keywords = "genetic algorithms, genetic programming, finite state
machines, genetic algorithms, mobile robots,
GP-automata controllers, behaviour performance, finite
state automata, finite state machine, hidden state
problems, internal memory strategies, memory states,
mobile robots, perceptual aliasing problems, purely
reactive systems, robotics researches, sensor states,
woods problems, Automata, Biological cells, Educational
institutions, Evolutionary computation, Position
measurement, Robot sensing systems, Evolutionary
computation, Finite State Machine, GP-automata,
Perceptual aliasing, Woods Problem",
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URL = "
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6393463",
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size = "4 pages",
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abstract = "Purely reactive systems have been used in many
robotics researches. However, they have difficulty in
solving the hidden state problems. Internal memory has
been used to solve the hidden state problems, which is
also called the perceptual aliasing problems. Woods
problem is one of the perceptual aliasing problems. In
this paper, we apply two methods, Finite State Machine
and GP-automata controllers, to solve the Woods
problem. These two methods are compared in terms of the
behaviour performance of the agents with internal
memory and sensor states. The performance of each
method in the Woods problem is measured by the average
number of time steps needed to reach a goal position
from all possible initial positions. The analysis of
the memory shows that both memory states and sensor
states affect the behaviour performance of the agent.",
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notes = "Also known as \cite{6393463}",
- }
Genetic Programming entries for
Hyungu Yim
DaeEun Kim
Citations