<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christian Balkenius</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Philip Pärnamets</style></author><author><style face="normal" font="default" size="100%">Birger Johansson</style></author><author><style face="normal" font="default" size="100%">Martin V Butz</style></author><author><style face="normal" font="default" size="100%">Andreas Olsson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Outline of a sensory-motor perspective on intrinsically moral agents</style></title><secondary-title><style face="normal" font="default" size="100%">Adaptive Behavior</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.sagepub.com/doi/10.1177/1059712316667203</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">SAGE</style></publisher><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">306–319 </style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose that moral behaviour of artificial agents could (and should) be intrinsically grounded in their own sensory-motor experiences. Such an ability depends critically on seven types of competencies. First, intrinsic morality should be grounded in the internal values of the robot arising from its physiology and embodiment. Second, the moral principles of robots should develop through their interactions with the environment and with other agents. Third, we claim that the dynamics of moral (or social) emotions closely follows that of other non-social emotions used in valuation and decision making. Fourth, we explain how moral emotions can be learned from the observation of others. Fifth, we argue that to assess social interaction, a robot should be able to learn about and understand responsibility and causation. Sixth, we explain how mechanisms that can learn the consequences of actions are necessary for a robot to make moral decisions. Seventh, we describe how the moral evaluation mechanisms outlined can be extended to situations where a robot should understand the goals of others. Finally, we argue that these competencies lay the foundation for robots that can feel guilt, shame and pride, that have compassion and that know how to assign responsibility and blame.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://journals.sagepub.com/doi/10.1177/1059712316667203&quot;&gt;Download&lt;/a&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Coninx, Alexandre</style></author><author><style face="normal" font="default" size="100%">Paul E. Baxter</style></author><author><style face="normal" font="default" size="100%">Oleari, Elettra</style></author><author><style face="normal" font="default" size="100%">Bellini, Sara</style></author><author><style face="normal" font="default" size="100%">Bierman, Bert</style></author><author><style face="normal" font="default" size="100%">Henkemans, Olivier Blanson</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Cosi, Piero</style></author><author><style face="normal" font="default" size="100%">Valentin Enescu</style></author><author><style face="normal" font="default" size="100%">Espinoza, Raquel Ros</style></author><author><style face="normal" font="default" size="100%">Antoine Hiolle</style></author><author><style face="normal" font="default" size="100%">Remi Humbert</style></author><author><style face="normal" font="default" size="100%">Kiefer, Bernd</style></author><author><style face="normal" font="default" size="100%">Kruijff-Korbayová, Ivana</style></author><author><style face="normal" font="default" size="100%">Looije, Rosmarijn</style></author><author><style face="normal" font="default" size="100%">Mosconi, Marco</style></author><author><style face="normal" font="default" size="100%">Mark A. Neerincx</style></author><author><style face="normal" font="default" size="100%">Giulio Paci</style></author><author><style face="normal" font="default" size="100%">Patsis, Georgios</style></author><author><style face="normal" font="default" size="100%">Pozzi, Clara</style></author><author><style face="normal" font="default" size="100%">Sacchitelli, Francesca</style></author><author><style face="normal" font="default" size="100%">Hichem Sahli</style></author><author><style face="normal" font="default" size="100%">Alberto Sanna</style></author><author><style face="normal" font="default" size="100%">Sommavilla, Giacomo</style></author><author><style face="normal" font="default" size="100%">Tesser, Fabio</style></author><author><style face="normal" font="default" size="100%">Yiannis Demiris</style></author><author><style face="normal" font="default" size="100%">Tony Belpaeme</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Long-Term Social Child-Robot Interaction: Using Multi-Activity Switching to Engage Young Users</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Human-Robot Interaction</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.5898/JHRI.5.1.Coninx</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">32–67</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://dl.acm.org/doi/abs/10.5898/JHRI.5.1.Coninx&quot;&gt;Download&lt;/a&gt; (Open Access)</style></notes></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%">Lewis, Matthew</style></author><author><style face="normal" font="default" size="100%">Oleari, Elettra</style></author><author><style face="normal" font="default" size="100%">Pozzi, Clara</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%">Tapus, Adriana</style></author><author><style face="normal" font="default" size="100%">André, Elisabeth</style></author><author><style face="normal" font="default" size="100%">Martin, Jean-Claude</style></author><author><style face="normal" font="default" size="100%">Ferland, François</style></author><author><style face="normal" font="default" size="100%">Ammi, Mehdi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Embodied AI Approach to Individual Differences: Supporting Self-Efficacy in Diabetic Children with an Autonomous Robot</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 7th International Conference on Social Robotics (ICSR-2015)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007%2F978-3-319-25554-5_40</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Paris</style></pub-location><pages><style face="normal" font="default" size="100%">401–410</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-25553-8</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 discuss how a motivationally autonomous robot, designed using the principles of embodied AI, provides a suitable approach to address individual differences of children interacting with a robot, without having to explicitly modify the system. We do this in the context of two pilot studies using Robin, a robot to support self-confidence in diabetic children.</style></abstract><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://link.springer.com/chapter/10.1007%2F978-3-319-25554-5_40&quot;&gt;Download&lt;/a&gt; (or &lt;a href=&quot;http://www.emotion-modeling.info/sites/default/files/2015_Lewis_Canamero_ICSR.pdf&quot;&gt;Download authors' draft&lt;/a&gt;)</style></notes></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%">Kruijff-Korbayová, Ivana</style></author><author><style face="normal" font="default" size="100%">Oleari, Elettra</style></author><author><style face="normal" font="default" size="100%">Pozzi, Clara</style></author><author><style face="normal" font="default" size="100%">Sacchitelli, Francesca</style></author><author><style face="normal" font="default" size="100%">Bagherzadhalimi, Anahita</style></author><author><style face="normal" font="default" size="100%">Bellini, Sara</style></author><author><style face="normal" font="default" size="100%">Kiefer, Bernd</style></author><author><style face="normal" font="default" size="100%">Racioppa, Stefania</style></author><author><style face="normal" font="default" size="100%">Coninx, Alexandre</style></author><author><style face="normal" font="default" size="100%">Paul E. Baxter</style></author><author><style face="normal" font="default" size="100%">Bierman, Bert</style></author><author><style face="normal" font="default" size="100%">Henkemans, Olivier Blanson</style></author><author><style face="normal" font="default" size="100%">Mark A. Neerincx</style></author><author><style face="normal" font="default" size="100%">Rosemarijn Looije</style></author><author><style face="normal" font="default" size="100%">Yiannis Demiris</style></author><author><style face="normal" font="default" size="100%">Espinoza, Raquel Ros</style></author><author><style face="normal" font="default" size="100%">Mosconi, Marco</style></author><author><style face="normal" font="default" size="100%">Cosi, Piero</style></author><author><style face="normal" font="default" size="100%">Remi Humbert</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Hichem Sahli</style></author><author><style face="normal" font="default" size="100%">Joachim de Greeff</style></author><author><style face="normal" font="default" size="100%">James Kennedy</style></author><author><style face="normal" font="default" size="100%">Robin Read</style></author><author><style face="normal" font="default" size="100%">Lewis, Matthew</style></author><author><style face="normal" font="default" size="100%">Antoine Hiolle</style></author><author><style face="normal" font="default" size="100%">Giulio Paci</style></author><author><style face="normal" font="default" size="100%">Sommavilla, Giacomo</style></author><author><style face="normal" font="default" size="100%">Tesser, Fabio</style></author><author><style face="normal" font="default" size="100%">Athanasopoulos, Georgios</style></author><author><style face="normal" font="default" size="100%">Patsis, Georgios</style></author><author><style face="normal" font="default" size="100%">Verhelst, Werner</style></author><author><style face="normal" font="default" size="100%">Alberto Sanna</style></author><author><style face="normal" font="default" size="100%">Tony Belpaeme</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Let’s Be Friends: Perception of a Social Robotic Companion for children with T1DM</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. New Friends 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://mheerink.home.xs4all.nl/pdf/ProceedingsNF2015-3.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Almere, The Netherlands</style></pub-location><pages><style face="normal" font="default" size="100%">32–33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We describe the social characteristics of a robot developed to support children with Type 1 Diabetes Mellitus (T1DM) in the process of education and care. We evaluated the perception of the robot at a summer camp where diabetic children aged 10-14 experienced the robot in group interactions. Children in the intervention condition additionally interacted with it also individually, in one-to-one sessions featuring several game-like activities. These children perceived the robot significantly more as a friend than those in the control group. They also readily engaged with it in dialogues about their habits related to healthy lifestyle as well as personal experiences concerning diabetes. This indicates that the one-on-one interactions added a special quality to the relationship of the children with the robot.</style></abstract><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://mheerink.home.xs4all.nl/pdf/ProceedingsNF2015-3.pdf&quot;&gt;Download full proceedings&lt;/a&gt; (PDF)</style></notes></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%">O'Bryne, Claire</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%">Harold Fellermann</style></author><author><style face="normal" font="default" size="100%">Mark Dörr</style></author><author><style face="normal" font="default" size="100%">Martin M Hanczy</style></author><author><style face="normal" font="default" size="100%">Lone Ladegaard Laursen</style></author><author><style face="normal" font="default" size="100%">Sarah Maurer</style></author><author><style face="normal" font="default" size="100%">Daniel Merkle</style></author><author><style face="normal" font="default" size="100%">Pierre-Alain Monnard</style></author><author><style face="normal" font="default" size="100%">Kasper Støy</style></author><author><style face="normal" font="default" size="100%">Steen Rasmussen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Emotion in Decisions of Life and Death – Its Role in Brain-Body-Environment Interactions for Predator and Prey</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life XII: Proc. of the 12th International Conference on the Synthesis and Simulation of Living Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://mitpress-request.mit.edu/sites/default/files/titles/alife/0262290758chap141.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%">Odense, Denmark</style></pub-location><pages><style face="normal" font="default" size="100%">812–822</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Taking inspiration from the biological world, in our work we are attempting to create and examine artificial predator-prey relationships using two LEGO robots. We do so to explore the possible adaptive value of emotion-like states for action selection in this context. However, we also aim to study and consider these concepts together at different levels of abstraction. For example, in terms of individual agents’ brain-body-environment interactions, as well as the (emergent) predator-prey relationships resulting from these. Here, we discuss some of the background concepts and motivations driving the design of our implementation and experiments. First, we explain why we think the predator-prey relationship is so interesting. Narrowing our focus to emotion-based architectures, this is followed by a review of existing literature, comparing different types and highlighting the novel aspects of our own. We conclude with our proposed contributions to the literature and thus, ultimately, the design and creation of artificial life.</style></abstract><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://mitpress-request.mit.edu/sites/default/files/titles/alife/0262290758chap141.pdf&quot;&gt;Download&lt;/a&gt; (Open Access)</style></notes></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%">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></contributors><titles><title><style face="normal" font="default" size="100%">Evolution of Bistable Dynamics in Spiking Neural Controllers for Agents Performing Olfactory Attraction and Aversion</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 19th Annual Computational Neuroscience Meeting (CNS*2010)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-11-S1-P92</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">BioMed Central Ltd.</style></publisher><pub-location><style face="normal" font="default" size="100%">San Antonio, TX</style></pub-location><volume><style face="normal" font="default" size="100%">11(Suppl 1)</style></volume><pages><style face="normal" font="default" size="100%">92</style></pages><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>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></contributors><titles><title><style face="normal" font="default" size="100%">Evolution of Bilateral Symmetry in Agents Controlled by Spiking Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2009 IEEE Symposium on Artificial Life (ALIFE 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/4937702/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Nashville, TN</style></pub-location><pages><style face="normal" font="default" size="100%">116–123</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-2763-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present in this paper three novel developmental models allowing information to be encoded in space and time, using spiking neurons placed on a 2D substrate. In two of these models, we introduce neural development that can use bilateral symmetry. We show that these models can create neural controllers for agents evolved to perform chemotaxis. Neural bilateral symmetry can be evolved and be beneficial for an agent. This work is the first, as far as we know, to present developmental models where spiking neurons are generated in space and where bilateral symmetry can be evolved and proved to be beneficial in this context.</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%">O'Bryne, Claire</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">John C Murray</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Importance of the Body in Affect-Modulated Action Selection: A Case Study Comparing Proximal Versus Distal Perception in a Prey-Predator Scenario</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 3rd Intl. Conference on Affective Computing and Intelligent Interaction (ACII 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2009</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam, The Netherlands</style></pub-location><pages><style face="normal" font="default" size="100%">1–6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In the context of the animat approach, we investigate the effect of an emotion-like hormonal mechanism, as a modulator of perception - and second order controller to an underlying motivation-based action selection architecture - on brain-body-environment interactions within a prey-predator scenario. We are particularly interested in the effects that affective modulation of different perceptual capabilities has on the dynamics of interactions between predator and prey, as part of a broader study of the adaptive value of emotional states such as &quot;fear&quot; and &quot;aggression&quot; in the context of action selection. In this paper we present experiments where we modulated the architecture of a prey robot using two different types of sensory capabilities, proximal and distal, effectively creating combinations of different prey &quot;brains&quot; and &quot;bodies&quot;.</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%">Antoine Hiolle</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%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Pierre-Yves Oudeyer</style></author><author><style face="normal" font="default" size="100%">Christian Balkenius</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Affective Landmarks</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 9th International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (EpiRob 2009)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lund University Cognitive Studies</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.lucs.lu.se/LUCS/146/epirob09.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lund University</style></publisher><pub-location><style face="normal" font="default" size="100%">Venice, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">146</style></volume><pages><style face="normal" font="default" size="100%">211–212</style></pages><isbn><style face="normal" font="default" size="100%">978-91-977-380-7-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This poster presents early work on the effects of arousal and its regulation on learning about the environment, particularly affective memories associated with places that can be used to safely guide exploration.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Pierre-Yves Oudeyer</style></author><author><style face="normal" font="default" size="100%">Christian Balkenius</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Ninth International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems</style></title><tertiary-title><style face="normal" font="default" size="100%">Lund University Cognitive Studies</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.lucs.lu.se/LUCS/146/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lund University</style></publisher><pub-location><style face="normal" font="default" size="100%">Venice, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">146</style></volume><isbn><style face="normal" font="default" size="100%">978-91-977-380-7-1</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Bowes</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><author><style face="normal" font="default" size="100%">Volker Steuber</style></author><author><style face="normal" font="default" size="100%">Davey, Neil</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Pierre-Yves Oudeyer</style></author><author><style face="normal" font="default" size="100%">Christian Balkenius</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Should I worry about my stressed pregnant robot?</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 9th International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (EpiRob 2009)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lund University Cognitive Studies</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.lucs.lu.se/LUCS/146/epirob09.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Lund University</style></publisher><pub-location><style face="normal" font="default" size="100%">Venice, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">146</style></volume><pages><style face="normal" font="default" size="100%">203–204</style></pages><isbn><style face="normal" font="default" size="100%">978-91-977-380-7-1</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>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%">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%">Seth Bullock</style></author><author><style face="normal" font="default" size="100%">Jason Noble</style></author><author><style face="normal" font="default" size="100%">Richard A. Watson</style></author><author><style face="normal" font="default" size="100%">Mark A Bedau</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Noise in Spiking Neural Networks for the Detection of Chemicals by Simulated Agents</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://mitpress-request.mit.edu/sites/default/files/titles/alife/0262287196chap58.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%">Winchester, UK</style></pub-location><pages><style face="normal" font="default" size="100%">443–449</style></pages><isbn><style face="normal" font="default" size="100%">978-0-262-75017-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We created a spiking neural controller for an agent that could use two different types of information encoding strategies depending on the level of chemical concentration present in the environment. The first goal of this research was to create a simulated agent that could react and stay within a region where there were two different overlapping chemicals having uniform concentrations. The agent was controlled by a spiking neural network that encoded sensory information using temporal coincidence of incoming spikes when the level of chemical concentration was low, and as firing rates at high level of concentration. With this architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour. The next experiment we did was to use a more realistic model by having an environment composed of concentration gradients and by adding input current noise to all neurons. We used a realistic model of diffusive noise and showed that it could improve the agent’s behaviour if used within a certain range. Therefore, an agent with neuronal noise was better able to stay within the chemical concentration than an agent without.</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%">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%">Trappl, R</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Receptor Response Functions for the Detection of Pheromones by Agents Driven by Spiking Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 9th European Meeting on Cybernetics and Systems Research, Vol. II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cogsci.uci.edu/~noros/mypapers/OROS_2008_EMCSR.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Austrian Society for Cybernetic Studies</style></publisher><pub-location><style face="normal" font="default" size="100%">Vienna, Austria</style></pub-location><pages><style face="normal" font="default" size="100%">427–432</style></pages><isbn><style face="normal" font="default" size="100%">978-3-85206-175-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The goal of the work presented here is to find a model of a spiking sensory neuron that could cope with small variations in the concentration of simulated chemicals and also the whole range of concentrations. By using a biologically plausible sigmoid function in our model to map chemical concentration to current, we could produce agents able to detect the whole range of concentration of chemicals (pheromones) present in the environment as well as small variations of them. The sensory neurons used in our model are able to encode the stimulus intensity into appropriate firing rates.</style></abstract></record></records></xml>