<?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%">Andrzej Gajda</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author><author><style face="normal" font="default" size="100%">Mariusz Urbański</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">M. Ganzha</style></author><author><style face="normal" font="default" size="100%">L. Maciaszek</style></author><author><style face="normal" font="default" size="100%">M. Paprzycki</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A connectionist approach to abductive problems: employing a learning algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">ACSIS</style></publisher><pages><style face="normal" font="default" size="100%">353–362</style></pages><abstract><style face="normal" font="default" size="100%">This paper presents preliminary results of an application of artificial neural networks and Backpropagation learning algorithm to solve logical abductive problems. To represent logic programs in the form of artificial neural networks CIL2P approach proposed by Garcez et al. is employed. Our abductive procedure makes use of translation of a logic program representing a knowledge base into a neural network, training of the neural network with an example representing an abductive goal and translation of the trained network back to the form of a logic program. An abductive hypothesis is represented as the symmetric difference between the initial logic program and the one obtained after training of the network. The first part of the paper introduces formal description of the tools used to model the abductive process, while the second part illustrates our contribution with results of a few computational experiments and discusses the ways of possible improvements of the proposed procedure.</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%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Jan Polak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolving free-form stick ski jumpers and their neural control systems</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Conference on Evolutionary Computation and Global Optimization</style></secondary-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%">http://www.framsticks.com/files/common/Komosinski_Polak_EvolvedSkiJumping.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Poland</style></pub-location><pages><style face="normal" font="default" size="100%">103--110</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper concerns evolution of stick agents in a simplified ski-jumping task. Both body morphologies and control systems are optimized. Evolutionary processes are performed in a range of conditions: the air drag and the friction of the ramp varies. Qualitative and quantitative analyses are presented that show how jump distance, jump height, and flight trajectory depend on environmental conditions. Jumping and landing strategies are investigated, and the most interesting evolved behaviors are reported.</style></abstract></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%">Pyles, J.A.</style></author><author><style face="normal" font="default" size="100%">Grossman, E.D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural adaptation for novel objects during dynamic articulation</style></title><secondary-title><style face="normal" font="default" size="100%">Neuropsychologia</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1261–1268</style></pages></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%">Spaak, Eelke</style></author><author><style face="normal" font="default" size="100%">Haselager, Pim F. G.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Anton Nijholt</style></author><author><style face="normal" font="default" size="100%">Maja Pantic</style></author><author><style face="normal" font="default" size="100%">Mannes Poel</style></author><author><style face="normal" font="default" size="100%">Hendri Hondorp</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Imitation and mirror neurons: an evolutionary robotics model</style></title><secondary-title><style face="normal" font="default" size="100%">BNAIC 2008: 20th Belgian-Dutch Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pub-location><style face="normal" font="default" size="100%">Enschede</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The involvement of the mirror neuron system (MNS) in both imitation and action understanding has been firmly established. Various authors have claimed that the MNS's function in facilitating imitation builds upon its role in action understanding and is a phylogenetically later development. We argue that this hypothesis lacks sufficient evidence and present support for the reverse: the phylogenetically primary function of the MNS is imitation and the MNS could have evolved in response to a selective pressure for imitative behavior. This hypothesis was tested using evolutionary robotics simulation techniques. The simulation was conducted with embodied and embedded agents with a lifetime-adapting neural network for which the learning parameters were evolutionarily optimized. The agents had to perform an imitation task. Analysis of the resulting controller revealed artificial neurons showing clear mirror characteristics, suggesting that, indeed, mirror neurons evolve due to a selective pressure for imitative behavior.</style></abstract></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%">Pyles, J.A.</style></author><author><style face="normal" font="default" size="100%">Garcia, J.O.</style></author><author><style face="normal" font="default" size="100%">Hoffman, D.D.</style></author><author><style face="normal" font="default" size="100%">Grossman, E.D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visual perception and neural correlates of novel 'biological motion'</style></title><secondary-title><style face="normal" font="default" size="100%">Vision Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.visres.2007.07.017</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">21</style></number><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">2786–2797</style></pages></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%">Pyles, J.A.</style></author><author><style face="normal" font="default" size="100%">Garcia, J.O.</style></author><author><style face="normal" font="default" size="100%">Hoffman, D.D.</style></author><author><style face="normal" font="default" size="100%">Grossman, E.D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Brain activity evoked by perception of novel &quot;biological motion&quot;</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">Association for Research in Vision and Ophthalmology</style></publisher><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">794–794</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Rotaru-Varga</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Mark A. Bedau</style></author><author><style face="normal" font="default" size="100%">John S. McCaskill</style></author><author><style face="normal" font="default" size="100%">Norman H. Packard</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%">From Directed to Open-Ended Evolution in a Complex Simulation Model</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Life VII</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">AL</style></keyword><keyword><style  face="normal" font="default" size="100%">Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Theory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pages><style face="normal" font="default" size="100%">293–299</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>