Evolving internal memory strategies for the woods problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Yim:2012:ICCAS,
  author =       "Hyungu Yim and DaeEun Kim",
  booktitle =    "12th International Conference on Control, Automation
                 and Systems (ICCAS 2012)",
  title =        "Evolving internal memory strategies for the woods
                 problems",
  year =         "2012",
  pages =        "366--369",
  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",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6393463",
  size =         "4 pages",
  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.",
  notes =        "Also known as \cite{6393463}",
}

Genetic Programming entries for Hyungu Yim DaeEun Kim

Citations