@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}
}
@article {Komosinski-et-al-2014,
	title = {Identifying efficient abductive hypotheses using multi-criteria dominance relation},
	journal = {ACM Transactions on Computational Logic},
	volume = {15},
	number = {4},
	year = {2014},
	pages = {28:1{\textendash}28:20},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {In this article, results of the automation of an abductive procedure are reported. This work is a continuation of our earlier research, where a general scheme of the procedure has been proposed. Here, a more advanced system developed to generate and evaluate abductive hypotheses is introduced. Abductive hypotheses have been generated by the implementation of the Synthetic Tableau Method. Before the evaluation, the set of hypotheses has undergone several reduction phases. To assess usefulness of abductive hypotheses in the reduced set, several criteria have been employed. The evaluation of efficiency of the hypotheses has been provided by the multi-criteria dominance relation. To comprehend the abductive procedure and the evaluation process more extensively, analyses have been conducted on a number of artificially generated abductive problems.},
	doi = {10.1145/2629669},
	url = {http://www.framsticks.com/files/common/IdentifyingEfficientAbductiveHypotheses.pdf},
	author = {Maciej Komosinski and Adam Kups and Dorota Leszczy{\'n}ska-Jasion and Mariusz Urba{\'n}ski}
}
