Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species

Bertin, R.J.V. and van de Grind, W.A. (1998) Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species. [Journal (Paginated)]

Full text available as:

[img] PDF


An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it hunts autoluminescent prey. It uses a biologically inspired phototaxic foraging-strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the body-geometry (e.g. placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body-% geometry adapted to the habitat characteristics. In general performance degrades smoothly for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of 'design space'. The parameters have to strike a compromise between on the one hand the ability to 'fixate' an attractive target, and on the other hand to 'see' as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat 'mould' the body, and the body-geometry strongly influences performance. The resulting platform enables further testing of foraging strategies, or vision and locomotion theories stemming either from biology or from robotics.

Item Type:Journal (Paginated)
Keywords:hypothetical animal, 2 light experiment, phototaxic foraging, visuomotor behaviour, foraging performance, animats.
Subjects:Biology > Theoretical Biology
Neuroscience > Neural Modelling
ID Code:2119
Deposited By: Bertin, Dr R.J.V.
Deposited On:07 Mar 2002
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[1] Beer R.D. (1990) Intelligence as Adaptive Behaviour - An Experiment in Computational Neuroethology. Boston, Academic Press 1990

[2] Beer R.D. and Gallagher J.C. (1992) Evolving Dynamical Neural Networks for Adaptive Behavior.

Adaptive Behavior 1 1992: 91-122

[3] Bertin R.J.V. (1994) Natural smartness in Hypothetical animals-Of paddlers and glowballs, PhD thesis,

Utrecht University.

[4] Bertin R.J.V. and van de Grind W.A. (1996) The inuence of light/ dark adaptation and lateral

inhibition on phototaxic foraging.A hypothetical-animal study. Adaptive Behavior 2 1996: 141-167

[5] Braitenberg V. (1984) Vehicles - Experiments in Synthetic Psychology. Cambridge, MA, The MIT

Press 1984

[6] Cliff D. and Bullock S. (1993) Adding Foveal Vision to Wilson's Animat. Adaptive Behavior 2

1993: 49-72

[7] Cliff D., Husbands P. and Harvey I. (1993) Explorations in Evolutionary Robotics. Adaptive Behavior 2 1993: 73-110

[8] Corbacho F.J. and Arbib M.A. (1995) Learning to Detour. Adaptive Behavior 4 1995: 419-468

[9] Cruse H., Brunn D.E., Bartling Ch., Dean J., Dreifert M., Kindermann J. and Schmitz J. (1995)

Walking: A Complex Behavior Controlled by Simple Networks. Adaptive Behavior 4 1995: 385-418

[10] Ekeberg O., Lansner A. and Grillner S. (1995) The Neural Control of Fish Swimming Studied

Through Numerical Simulations. Adaptive Behavior 4 1995: 363-384

[11] Jacob F. (1977) Evolution and tinkering. Science 196: 1161-1166

[12] Fraenkel G.S. and Gunn D.L. (1961) The orientation of animals. New York, Dover Publications


[13] van de Grind W.A. (1990) Smart mechanisms for the visual evaluation and control of self-motion.

In: Warren R & Wertheim A (eds.): Perception and control of self-motion. Hillsdale NJ, LEA: Ch. 14,


[14] Holland J.H. (1975) Adaptation in natural and articial systems. Univ. Michigan Press

[15] Holland J.H. and Reitman J.S. (1978) Cognitive systems based on adaptive algorithms. In: Waterman & Hayes-Roth (eds): Pattern-directed inference systems. Academic Press

[16] Holland O and Melhuish C (1996) Some adaptive movements of animats with single sensor symmetrical sensors. In: Maes P, Mataric M, Meyer J-A, Pollack J and Wilson S.W. (eds): From Animals to

Animats 4: Proceeding of the Fourth International Conference on Simulation of Adaptive Behavior. The MIT

Press/Bradford Books, Cambridge, MA.

[17] Kuehn A. (1919) Die Orientierung der Tiere im Raum. Gustav Fischer Verlag, Jena, 1919

[18] Locket N.A. (1977) Adaptations to the deep-sea environment. In: Crescitelli F. (ed.): Handbook of

Sensory Physiology Vol. VII/5: The visual system in vertebrates. Berlin/New York, Springer Verlag 1977:

Ch.3, 67-192

[19] Loeb J. (1918) Forced movements, tropisms and animal conduct. Philidelphia, Lippincott 1918;

republished 1973, Dover, New York

[20] Lythgoe J.N. (1979) The ecology of vision. Oxford, Clarendon Press, 1979

[21] Roessler O.E. (1974) Adequate locomotion strategies for an abstract organism in an abstract

environment-A relational approach to brain function. In: Conrad M., Guettinger W. and Dal Cin M. (1974):

Lecture Notes in Biomathematics 4: Physics and Mathematics of the Nervous System. Berlin/New York,

Springer Verlag 1974: 342-370

[22] Schoene H. (1984) Spatial orientation. The spatial control of behavior in animals and man. Princeton

N.Y., Princeton University Press 1984

[23] Terzopoulos D., Tu X., and Grzeszczuk, R. (1994) Articial Fishes with Autonomous Locomotion,

Perception, Behavior, and Learning in a Simulated Physical World. In: Brooks R. and Maes P. (eds.):

Articial Life IV: Proc. of the Fourth International Workshop on the Synthesis and Simulation of Living

Systems. Cambridge, MA, 1994: p.17-27

[24] Walter W.G. (1950) An imitation of life. Scien. Am. 182: 42-45

[25] Walter W.G. (1951) A machine that learns. Scien. Am. 185: 60-63

[26] Wilson D.M. and Waldron I. (1968) Models for the generation of motor output patterns in ying

locusts. Proc. IEEE 56: 1058-1064

[27] Wilson D.M. and Weis-Fogh T. (1962) Patterned Activity of Co-ordinated Motor Units, studied in

ying Locusts. J. Exp. Biol. 39 1962: 643-667.

[28] Young H.D. (1992, 8th edition) University Physics. Addison-Wesley Publishing Company, Reading

MA 1992


Repository Staff Only: item control page