@article {Komosinski-and-Kups-2017r,
	title = {Evolutionary construction of derivations in classical propositional logic using a symbolic-connectionist representation},
	number = {RA{\textendash}3/17},
	year = {2017},
	institution = {Poznan University of Technology, Institute of Computing Science},
	type = {Research report},
	abstract = {This report introduces a way derivations in classical propositional logic can be constructed using evolutionary algorithms. The derivations are represented by connectionist systems. There are three kinds of nodes constituting these systems: formula nodes that generate signal in the form of strings of symbols, "modus ponens" nodes that transform incoming signal according to the "modus ponens" rule, and substitution nodes that transform incoming signal by applying the substitution rule. This work presents initial research on an approach that is a part of our quest for efficient construction of derivations using various logics and constrained in various ways. The final part of this report outlines limitations encountered in our initial experiments and the ways the proposed approach can be improved.},
	url = {http://www.framsticks.com/files/common/EvolutionOfDerivationsInLogic.pdf},
	author = {Maciej Komosinski and Adam Kups}
}
@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-and-Kups-2015,
	title = {Time-order error and scalar variance in a computational model of human timing: simulations and predictions},
	journal = {Computational Cognitive Science},
	volume = {1},
	number = {1},
	year = {2015},
	pages = {1{\textendash}24},
	publisher = {Springer},
	abstract = {This work introduces a computational model of human temporal discrimination mechanism - the Clock-Counter Timing Network. It is an artificial neural network implementation of a timing mechanism based on the informational architecture of the popular Scalar Timing Model. The model has been simulated in a virtual environment enabling computational experiments which imitate a temporal discrimination task - the two-alternative forced choice task. The influence of key parameters of the model (including the internal pacemaker speed and the variability of memory translation) on the network accuracy and the time-order error phenomenon has been evaluated. The results of simulations reveal how activities of different modules contribute to the overall performance of the model. These results can be compared and verified in empirical experiments with human participants, especially when the modes of activity of the internal timing mechanism are changed because of some external conditions, or are impaired due to some kind of a neural degradation process.},
	doi = {10.1186/s40469-015-0002-0},
	url = {http://dx.doi.org/10.1186/s40469-015-0002-0},
	author = {Maciej Komosinski and Adam Kups}
}
@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}
}
@article {Komosinski-and-Kups-2009r,
	title = {Models and implementations of timing processes using Artificial Life techniques},
	number = {RA-05/09},
	year = {2009},
	institution = {Poznan University of Technology, Institute of Computing Science},
	type = {Research report},
	abstract = {This work presents implementation of the Scalar Timing Model (STM) in the neural networks environment. STM is rather popular and commonly used model in the perception of time intervals in humans and animals fields of study. Currently many experiments are conducted in order to verify and research STM parameters and attributes. One of the goal of the implementation was to check whether theoretical model will cope with constraints of artificial neural networks. During implementation process it turned out, that scheme of the model should be revised (by adding extra components) in order to maintain it{\textquoteright}s functional adequacy. Another case was to check how does manipulations of certain parameters will influence collected representation of the real time within model. In this preliminary research we focus on the pacemaker module. Conclusion of this research is that appropriate choice of distribution form of impulses generated by pacemaker make it simulation of the model more congruent with the experimentally collected data then with formal assumptions of STM.},
	url = {http://www.framsticks.com/files/common/HumanTimingModelsSimulations.pdf},
	author = {Maciej Komosinski and Adam Kups}
}
