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Henrik Hautop Lund
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Journal Articles
Publisher: Journals Gateway
Artificial Life (1998) 4 (4): 303–307.
Published: 01 October 1998
Journal Articles
Publisher: Journals Gateway
Artificial Life (1998) 4 (1): 95–107.
Published: 01 January 1998
Abstract
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Behavioral experiments with crickets show that female crickets respond to male calling songs with syllable rates within a certain bandwidth only. We have made a robot model in which we implement a simple neural controller that is less complex than the controllers traditionally hypothesized for cricket phonotaxis and syllable rate preference. The simple controller, which had been successfully used with a slowed and simplified signal, is here demonstrated to function, using songs with identical parameters to those found in real male cricket song, using an analog electronic model of the peripheral auditory morphology of the female cricket as the sensor. We put the robot under the same experimental conditions as the female crickets, and it responds with phonotaxis to calling songs of real male Gryllus bimaculatus . Further, the robot only responds to songs with syllable rates within a bandwidth similar to the bandwidth found for crickets. By making polar plots of the heading direction of the robot, we obtain behavioral data that can be used in statistical analyses. These analyses show that there are statistically significant differences between the behavioral responses to calling songs with syllable rates within the bandwidth and calling songs with syllable rates outside the bandwidth. This gives the verification that the simple neural control mechanism (together with morphological auditory matched filtering) can account for the syllable rate preference found in female crickets. With our robot system, we can now systematically explore the mechanisms controlling recognition and choice behavior in the female cricket by experimental replication.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1995) 2 (4): 417–434.
Published: 01 July 1995
Abstract
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The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a “conservative” form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1995) 2 (2): 179–197.
Published: 01 January 1995
Abstract
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Populations of simple artificial organisms modeled as neural networks evolve a preference for one particular food type in an environment that contains more than one food type if the quantity of energy extracted from each food type is allowed to coevolve with the behavioral preference (evolvable fitness formula). If, after the emergence of the food preference, the preferred food gradually disappears from the environment at the evolutionary time scale, the evolved specialist strategy is maintained until the preferred food type has completely disappeared. Then a new specialist strategy suddenly emerges with a preference for another food type present in the environment. The appearance of the new strategy takes very few generations, in fact much fewer than in a population starting from zero (random initial population) in the same environment. This, together with the fact that the population with an evolutionary past is more efficient than the population starting from zero, suggests that the former population is preadapted to the changed environment. An analysis of the activation values of the hidden units indicates that the new food preference can be an “exaptation,” that is, a new adaptation based on a structure that has previously emerged for adaptively neutral reasons.