@conference {gajda2016connectionist,
	title = {A connectionist approach to abductive problems: employing a learning algorithm},
	booktitle = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)},
	year = {2016},
	pages = {353{\textendash}362},
	publisher = {ACSIS},
	organization = {ACSIS},
	abstract = {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.},
	doi = {10.15439/2016F484},
	author = {Andrzej Gajda and Adam Kups and Mariusz Urba{\'n}ski},
	editor = {M. Ganzha and L. Maciaszek and M. Paprzycki}
}
@conference {Komosinski-and-Polak-2009,
	title = {Evolving free-form stick ski jumpers and their neural control systems},
	booktitle = {Proceedings of the National Conference on Evolutionary Computation and Global Optimization},
	year = {2009},
	pages = {103--110},
	address = {Poland},
	abstract = {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.},
	url = {http://www.framsticks.com/files/common/Komosinski_Polak_EvolvedSkiJumping.pdf},
	author = {Maciej Komosinski and Jan Polak}
}
@article {pyles2009neural,
	title = {Neural adaptation for novel objects during dynamic articulation},
	journal = {Neuropsychologia},
	volume = {47},
	number = {5},
	year = {2009},
	pages = {1261{\textendash}1268},
	publisher = {Elsevier},
	author = {Pyles, J.A. and Grossman, E.D.}
}
@conference {2339,
	title = {Imitation and mirror neurons: an evolutionary robotics model},
	booktitle = {BNAIC 2008: 20th Belgian-Dutch Conference on Artificial Intelligence},
	year = {2008},
	address = {Enschede},
	abstract = {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{\textquoteright}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.},
	author = {Spaak, Eelke and Haselager, Pim F. G.},
	editor = {Anton Nijholt and Maja Pantic and Mannes Poel and Hendri Hondorp}
}
@article {pyles2007visual,
	title = {Visual perception and neural correlates of novel {\textquoteright}biological motion{\textquoteright}},
	journal = {Vision Research},
	volume = {47},
	number = {21},
	year = {2007},
	pages = {2786{\textendash}2797},
	publisher = {Elsevier},
	doi = {10.1016/j.visres.2007.07.017},
	url = {http://dx.doi.org/10.1016/j.visres.2007.07.017},
	author = {Pyles, J.A. and Garcia, J.O. and Hoffman, D.D. and Grossman, E.D.}
}
@article {pyles2006brain,
	title = {Brain activity evoked by perception of novel "biological motion"},
	journal = {Journal of Vision},
	volume = {6},
	number = {6},
	year = {2006},
	pages = {794{\textendash}794},
	publisher = {Association for Research in Vision and Ophthalmology},
	author = {Pyles, J.A. and Garcia, J.O. and Hoffman, D.D. and Grossman, E.D.}
}
@book {Komosinski-and-Rotaru-Varga-2000,
	title = {From Directed to Open-Ended Evolution in a Complex Simulation Model},
	series = {Artificial Life VII},
	year = {2000},
	pages = {293{\textendash}299},
	publisher = {MIT Press},
	organization = {MIT Press},
	keywords = {Agents, AL, Simulation, Theory},
	author = {Maciej Komosinski and Adam Rotaru-Varga},
	editor = {Mark A. Bedau and John S. McCaskill and Norman H. Packard and Steen Rasmussen}
}
