<?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%">Chlebowski, Szymon</style></author><author><style face="normal" font="default" size="100%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Generation of Erotetic Search Scenarios: Classification, Optimization, and Knowledge Extraction</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Computational Logic</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">knowledge extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic of questions</style></keyword><keyword><style  face="normal" font="default" size="100%">multicriteria analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">optimal erotetic scenario</style></keyword><keyword><style  face="normal" font="default" size="100%">rule mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.put.poznan.pl/mkomosinski/research/automated-erotetic-search-scenarios.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">8:1–8:27</style></pages><abstract><style face="normal" font="default" size="100%">This paper concerns automated generation and processing of erotetic search scenarios (ESSs). ESSs are formal constructs characterized in Inferential Erotetic Logic that enable finding possible answers to a posed question by decomposing it into auxiliary questions. The first part of this work describes a formal account on ESSs. The formal approach is then applied to automatically generate ESSs, and the resulting scenarios are evaluated according to a number of criteria. These criteria are subjected to discordance analysis that reveals their mutual relationships. Finally, knowledge concerning relationships between different values of evaluation criteria is extracted by applying Apriori - an association rules mining algorithm. The proposed approach of integration of formal erotetic logic with computational tools provides an extensive insight into the former and helps with the development of efficient ESSs.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</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 Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary construction of derivations in classical propositional logic using a symbolic-connectionist representation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/EvolutionOfDerivationsInLogic.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA–3/17</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">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, &quot;modus ponens&quot; nodes that transform incoming signal according to the &quot;modus ponens&quot; 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.</style></abstract><work-type><style face="normal" font="default" size="100%">Research report</style></work-type></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%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time-order error and scalar variance in a computational model of human timing: simulations and predictions</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Cognitive Science</style></secondary-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%">http://dx.doi.org/10.1186/s40469-015-0002-0</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">1–24</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></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%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author><author><style face="normal" font="default" size="100%">Dorota Leszczyńska-Jasion</style></author><author><style face="normal" font="default" size="100%">Mariusz Urbański</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying efficient abductive hypotheses using multi-criteria dominance relation</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Computational Logic</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.framsticks.com/files/common/IdentifyingEfficientAbductiveHypotheses.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">28:1–28:20</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></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%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Postrzeganie czasu przez człowieka: specyfika, modele i symulacje</style></title><secondary-title><style face="normal" font="default" size="100%">Anestezjologia i Ratownictwo</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">błąd porządku czasowego</style></keyword><keyword><style  face="normal" font="default" size="100%">postrzeganie upływu czasu</style></keyword><keyword><style  face="normal" font="default" size="100%">sieć neuronowa</style></keyword><keyword><style  face="normal" font="default" size="100%">symulacja</style></keyword><keyword><style  face="normal" font="default" size="100%">TOE</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.akademiamedycyny.pl/wp-content/uploads/2016/05/201301_AiR_013.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Akademia Medycyny</style></publisher><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">113–124</style></pages><abstract><style face="normal" font="default" size="100%">Niniejszy artykuł poświęcony jest zagadnieniu postrzegania czasu trwania zdarzeń (ang. timing) przez człowieka. Zarysowano w nim bieżący stan wiedzy zwracając uwagę na niedostatek modeli obejmujących szeroki zakres dziedzin, w których prowadzi się badania postrzegania czasu. Przedstawiono przykładową implementację popularnego modelu typu zegar-licznik w środowisku sztucznych sieci neuronowych. Opisano zjawisko &quot;błędu porządku czasowego&quot; oraz pokazano, jak zaproponowana sztuczna sieć neuronowa może naśladować zachowanie człowieka popełniając podobne błędy w zależności od parametrów opisujących działanie jej elementów składowych. Artykuł omawia też rolę technik symulacyjnych oraz metod sztucznej inteligencji i sztucznego życia w budowaniu działających i weryfikowalnych modeli niepoznanych jeszcze procesów i zjawisk biologicznych, których przykładem jest postrzeganie czasu.</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%">Adam Kups</style></author><author><style face="normal" font="default" size="100%">Mariusz Urbanski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-criteria evaluation of abductive hypotheses: towards efficient optimization in proof theory</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 18th International Conference on Soft Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Brno, Czech Republic</style></pub-location><pages><style face="normal" font="default" size="100%">320–325</style></pages><abstract><style face="normal" font="default" size="100%">Research described in this paper aims at implementation of an abductive procedure based on the Synthetic Tableaux Method. This problem concerns a two-stage process of generation and evaluation of abductive hypotheses. While generation of the abductive hypotheses is achieved here by the implementation of the rules provided with a logical apparatus, their evaluation is performed using traditional measures and optimization techniques such as Pareto optimality and fitness–distance analysis. By combining tools known in proof theory and in Artificial Intelligence, we explore the intriguing problem of interpretative reasoning.</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%">Maciej Komosinski</style></author><author><style face="normal" font="default" size="100%">Adam Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementation and Simulation of the Scalar Timing Model</style></title><secondary-title><style face="normal" font="default" size="100%">Bio-Algorithms and Med-Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://bit.wl.cm.uj.edu.pl/cm/uploads/2021/02/bams7_4.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">41–52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</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 Kups</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Models and implementations of timing processes using Artificial Life techniques</style></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/HumanTimingModelsSimulations.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">RA-05/09</style></number><publisher><style face="normal" font="default" size="100%">Poznan University of Technology, Institute of Computing Science</style></publisher><abstract><style face="normal" font="default" size="100%">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'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.</style></abstract><work-type><style face="normal" font="default" size="100%">Research report</style></work-type></record></records></xml>