J.L. Deneubourg, S. Goss, J. Pasteels, D. Fresneau, and J.P. Lachaud. Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor. In J.M. Pasteels and eds. J.L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, Experientia Supplementum, volume 54, pages 177--196. Birkhauser Verlag, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....how we bootstrap a spatial representation, particularly a vision based one, also appears to be relevant to other research areas such as computer vision and even ethology. Several authors have considered the use of self organization in robot navigation [Takahashi et al. 2001; Beni and Wang, 1991; Deneubourg et al. 1989; Selfridge, 1962] often with impressive results. We believe this paper is among the first to demonstrate how to build a complete map of a real (non simulated) unknown environment using monocular vision. We present quantitative data to substantiate this. We approach the problem in the context ....

J.L. Deneubourg, S. Goss, J. Pasteels, D. Fresneau, and J.P. Lachaud. Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor. In J.M. Pasteels and eds. J.L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, Experientia Supplementum, volume 54, pages 177--196. Birkhauser Verlag, 1989.

.... which the (m 1) th ant chooses the upper branch is P U (m) Lm k) 1) while the probability P L (m) that it chooses the lower branch is P L (m) 1 P U (m) This functional form of the probability of choosing a branch over the other was obtained from experiments on trail following [80]; the parameters h and k allow to t the model to exper2 area Nest area Nest area Nest (c) Figure 2. Double bridge experiment. a) Ants start exploring the double bridge. b) Eventually most of the ants choose the shortest path. c) Distribution of the percentage of ants that selected the ....

.... where is a random variable uniformly distributed over the interval [0,1] Monte Carlo simulations were run to test the correspondence between this model and the real data: results of simulations were in agreement with the experiments with real ants when parameters were set to k 20 and h 2 [80]. It is easy to modify the experiment above to the case in which the bridge s branches are of di erent length [58] and to extend the model of Equation 1 so that it can describe this new situation. In this case, because of the same pheromone laying mechanism as in the previous situation, the ....

J. M. Pasteels, J.-L. Deneubourg, and S. Goss. Self-organization mechanisms in ant societies (i): Trail recruitment to newly discovered food sources. Experientia Supplementum, 54:155-175, 1987.

....spatial pattern (like for example in [10] we decided to use multi agent simulation. Multi agent simulation is not a new technique for simulating insect societies. More than 10 years ago Deneubourg et al. modeled foraging and path recruiting behavior of ants based on very simple rules [11] [12]. They also reproduced building behaviors. Simple agents react only to the environmental shapes they perceive and thus are able to construct complex nest structures without a apriori map [13] 14] Besides this work that focuses on single, rather restricted phenomena there is only little effort ....

J. M. Pasteels, J.-L. Deneubourg und S. Goss. Self-organization mechanisms in ant societies (1) Trail recruitment to newly discovered food sources. In J. M. Pasteels and J.-L. Deneubourg (eds.): From Individual to Collective Behavior in Social Insects, 155-176, 1987.

....double bridge. b) Eventually most of the ants choose the shortest path. c) Distribution of the percentage of ants that selected the shorter path. After Goss et al. 1989 [58] functional form of the probability of choosing a branch over the other was obtained from experiments on trail following [80]; the parameters h and k allow to fit the model to experimental data. The ant choice dynamics follows from the above equation: Um 1 = Um 1, if # # P U , Um 1 = Um otherwise, where # is a random variable uniformly distributed over the interval [0,1] Monte Carlo simulations were run to test ....

.... # is a random variable uniformly distributed over the interval [0,1] Monte Carlo simulations were run to test the correspondence between this model and the real data: results of simulations were in agreement with the experiments with real ants when parameters were set to k # 20 and h # 2 [80]. It is easy to modify the experiment above to the case in which the bridge s branches are of di#erent length [58] and to extend the model of Equation 1 so that it can describe this new situation. In this case, because of the same pheromone laying mechanism as in the previous situation, the ....

J. M. Pasteels, J.-L. Deneubourg, and S. Goss. Self-organization mechanisms in ant societies (i): Trail recruitment to newly discovered food sources. Experientia Supplementum, 54:155--175, 1987.

....is still too simple to model realistic foraging tasks. Future work will extend the model so that further learning questions can be addressed. When presented with a choice between two trails of differing lengths (Goss et al. 1989; Deneubourg Goss, 1989) or a choice between two food sources (Pasteels et al. 1987), the reinforcement of the selection can be seen as a form of Hebbian learning (Millonas, 1992) If an ant at a fork F with a choice between branches A and B chooses A and leaves pheromone, then future ants will be more likely to choose A. Future work with this system should examine the effect of ....

Pasteels, J. M., Deneubourg, J.-L., & Goss, S. (1987). Self-organization mechanisms in ant societies (i): Trail recruitment to newly discovered food sources. In J. M. Pasteels & J. Deneubourg (Eds.), From Individual to Collective Behavior in Social Insects (pp. 155--175). Basel: Birkh¨auser Verlag.

....for which agents such as swarm robots or softbots may offer a good solution. Backtracking the development of the abstract notion of swarm systems gives us the first definition in [14] in the field of the simulation of adaptive behaviour, but earlier relevant ideas are found in mathematical biology [15], biological cybernetics [16] and robotics [17] Close relationships with some multi agent systems are obvious as well [18 20] In the last few years, studies of the collective action of simple entities have received a fresh stimulus because of the possibility of making straightforward analogies ....

Deneubourg J.-L., Goss S., Pasteels J.M., Fresneau D. and Lachaud J.-P. Self-organization mechanisms in ant societies (II): learning in foraging and division of labor. In: From Individual to Collective. Behavior in Social Insects. J.M. Pasteels and J.-L. Deneubourg (Eds.), Basel: Birkhauser, 1987, 177-196.

.... models have been developed of a variety of natural behaviors, ranging from reflexive behavior selection strategies [21] cricket phonotaxis for flight and mating behaviors [79] lobster odor location [38] fly [33] and hover fly [20] vision, to insect navigation, trail formation, and path finding [25, 26], the application of the schema theory to modeling navigation [5] and frog behavior [6] the use of evolutionary computation methods, modeled after natural selection, to develop individual robotic behaviors [68] as well as group behaviors [1] and many others. A bi annual international conference, ....

Jean L. Deneubourg, Simon Goss, J. M. Pasteels, D. Fresneau, and J. P. Lachaud. Selforganization mechanisms in ant societies, ii: Learning in foraging and division of labor. In From Individual to Collective Behavior in Social Insects, volume 54, pages 177--196. 1987.

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J.-L. Deneubourg, S. Goss, J.M. Pasteels, D. Fresneau, and J.-P. Lachaud. Self-organization mechanisms in ant societies (II): Learning in foraging and division of labor. In J.M. Pasteels and J.-L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, volume 54 of Experientia Supplementum, pages 177--196. Birkhauser Verlag, Basel, Switzerland, 1987.

No context found.

J.-L. Deneubourg, S. Goss, J.M. Pasteels, D. Fresneau, and J.-P. Lachaud. Selforganization mechanisms in ant societies (II): Learning in foraging and division of labor. In J.M. Pasteels and J.-L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, volume 54 of Experientia Supplementum, pages 177--196. Birkhauser Verlag, Basel, Switzerland, 1987.

....present paper, we extend the fixed threshold model (and, therefore, the FFW model as well) by allowing thresholds to vary in time, following a simple reinforcement process: a threshold decreases when the corresponding task is performed, and increases when the corresponding task is not performed. Deneubourg et al. 1987), Plowright and Plowright (1988) and Theraulaz et al. 1991) introduced this idea (see also (Oster 1976) but did not attempt to explore its consequences in detail, especially when several tasks need to be performed. Moreover, our formulation of the reinforcement is based on a threshold model ....

....hypothesis is fully consistent with experiments, and can overcome limitations (1) 4) it is therefore worth undertaking a detailed study of a threshold model based on this hypothesis. Several experiments suggest the existence of a reinforcement process or support the reinforcement hypothesis. Deneubourg et al. 1987) proposed that such an hypothesis would be consistent with experimental observations of foraging in ants. Sendova Franks and Franks (1994) suggested that reinforcement learning plays a role in the ability of Leptothorax ant colonies to quickly re assemble after dissociation. Withers et al. 1993) ....

Deneubourg, J-L., Goss S., Pasteels, J. M., Fresneau, D., & Lachaud, J-P. 1987 Selforganization mechanisms in ant societies (II): learning in foraging and division of labour. Experientia Suppl. 54, 177-196.

No context found.

J.L. Deneubourg, S. Goss, J. Pasteels, D. Fresneau, and J.P. Lachaud. Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor. In J.M. Pasteels and eds. J.L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, Experientia Supplementum, volume 54, pages 177--196. Birkhauser Verlag, 1989.

No context found.

J.L. Deneubourg, S. Goss, J. Pasteels, D. Fresneau, and J.P. Lachaud. Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor. In J.M. Pasteels and eds. J.L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, Experientia Supplementum, volume 54, pages 177--196. Birkhauser Verlag, 1989.

No context found.

J.-L. Deneubourg, S. Goss, J.M. Pasteels, D. Fresneau, and J.-P. Lachaud. Self-organization mechanisms in ant societies (II): learning in foraging and division of labor. In J.M. Pasteels and J.-L. Deneubourg, editors, From Individual to Collective Behavior in Social Insects, volume 54 of Experientia Supplementum, pages 177--196. Birkhauser Verlag, Basel, Switzerland, 1987.

No context found.

J. M. Pasteels, J.-L. Deneubourg, and S. Goss. Self-organization mechanisms in ant societies (I): Trail recruitment to newly discovered food sources. Experientia Supplementum, 54:155-175, 1987.

No context found.

J.L. Deneubourg, S. Goss, J. Pasteels, D. Fresneau, and J.P. Lachaud, Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor, From Individual to Collective Behavior in Social Insects, Experientia Supplementum (J.M. Pasteels and eds. J.L. Deneubourg, eds.), vol. 54, Birkhauser Verlag, 1989, pp. 177--196.

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