<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oros, Nicolas</style></author><author><style face="normal" font="default" size="100%">Volker Steuber</style></author><author><style face="normal" font="default" size="100%">Davey, Neil</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Roderick G Adams</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Asada, Minoru</style></author><author><style face="normal" font="default" size="100%">Hallam, John C T</style></author><author><style face="normal" font="default" size="100%">Jean-Arcady Meyer</style></author><author><style face="normal" font="default" size="100%">Tani, Jun</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Olfactory Encoding in Agents Controlled by Spiking Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">From Animals to Animats 10: Proc. 10th International Conference on Simulation of Adaptive Behavior (SAB 2008)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (LNCS)</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-540-69134-1_15</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer, Berlin, Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Osaka, Japan</style></pub-location><volume><style face="normal" font="default" size="100%"> 5040</style></volume><pages><style face="normal" font="default" size="100%">148–158</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-69134-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We created a neural architecture that can use two different types of information encoding strategies depending on the environment. The goal of this research was to create a simulated agent that could react to two different overlapping chemicals having varying concentrations. The neural network controls the agent by encoding its sensory information as temporal coincidences in a low concentration environment, and as firing rates at high concentration. With such an architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avila-García, Orlando</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Stefan Schaal</style></author><author><style face="normal" font="default" size="100%">Auke Jan Ijspeert</style></author><author><style face="normal" font="default" size="100%">Aude Billard</style></author><author><style face="normal" font="default" size="100%">Sethu Vijayakumar</style></author><author><style face="normal" font="default" size="100%">John Hallam</style></author><author><style face="normal" font="default" size="100%">Jean-Arcady Meyer</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Using Hormonal Feedback to Modulate Action Selection in a Competitive Scenario</style></title><secondary-title><style face="normal" font="default" size="100%">From Animals to Animats 8: Proc. 8th Intl. Conf. on Simulation of Adaptive Behavior (SAB'04)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Orlando_Avila-Garcia/publication/228958663_Using_Hormonal_Feedback_to_Modulate_Action_Selection_in_a_Competitive_Scenario/links/0deec533c8411ebe0c000000.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Los Angeles, USA</style></pub-location><pages><style face="normal" font="default" size="100%">243–252</style></pages><isbn><style face="normal" font="default" size="100%">9780262693417</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we investigate the use of hormonal feedback as a mechanism to modulate a &quot;motivation-based,&quot; homeostatic action selection mechanism (ASM) in a robot. We have framed our study in the context of a dynamic, multirobot, competitive &quot;two-resource&quot; action selection problem. The introduction of competitors has important consequences for action selection. We first show how the interaction between robots introduces new forms of environmental complexity that affect their viability. Secondly, we propose a &quot;hormone-like&quot; mechanism that, modulating the input of the ASM, tackles these new sources of complexity.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avila-García, Orlando</style></author><author><style face="normal" font="default" size="100%">Hafner, Elena</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bridget Hallam</style></author><author><style face="normal" font="default" size="100%">Dario Floreano</style></author><author><style face="normal" font="default" size="100%">John Hallam</style></author><author><style face="normal" font="default" size="100%">Gillian M Hayes</style></author><author><style face="normal" font="default" size="100%">Jean-Arcady Meyer</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Relating Behavior Selection Architectures to Environmental Complexity</style></title><secondary-title><style face="normal" font="default" size="100%">From Animals to Animats: Proc. 7th International Conference on Simulation of Adaptive Behavior</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Edinburgh, Scotland</style></pub-location><pages><style face="normal" font="default" size="100%">127–128</style></pages><isbn><style face="normal" font="default" size="100%">9780-262-58217-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D Cañamero</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Alexis Drogoul</style></author><author><style face="normal" font="default" size="100%">Jean-Arcady Meyer</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Emotions pour les agents situés</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligence Artificielle Située</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Hermès science publications</style></publisher><pub-location><style face="normal" font="default" size="100%">Paris</style></pub-location><isbn><style face="normal" font="default" size="100%">978-274620076-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Contrairement à l'intelligence artificielle (IA) symbolique, l'IA située, qui adopte une vision plus large de l'intelligence &quot;complète&quot; qui ne la détache pas de sa réalisation corporelle et qui s'intéresse à son rôle adaptatif, ouvre naturellement la porte à l'étude des rôles des émotions d'un point de vue évolutif et à leur intégration dans les agents autonomes ou animats comme des mécanismes favorisant l'adaptation. Cet article examine les raisons pour lesquelles il semble intéressant de doter d'émotions les agents situés, en établissant un lien avec les émotions naturelles, ainsi que les différentes approches envisageables permettant de modéliser les émotions dans le cadre de l'IA située, et les différents problèmes qui en découlent. 

The notion of intelligence underlying symbolic Artificial Intelligence (AI) is tightly coupled to the idea of rationality. On the contrary, situated AI, with a wider view of intelligence that focuses on its embodiment and its adaptive value, allows to study emotional phenomena in animats from the point of view of evolution, and to investigate their adaptive roles. This paper examines the main reasons why it seems interesting to endow animats with emotions, establishing a parallel with natural emotions. It also considers the main approches that can be used to model emotions within situated AI, and the problems they pose. 
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