TY - GEN A1 - Schmitt, Ingo T1 - QLDT+: Efficient Construction of a Quantum Logic Decision Tree T2 - ICMLT '23: Proceedings of the 2023 8th International Conference on Machine Learning Technologies, SESSION: Session 2 - Data Model Design and Algorithm Analysis N2 - The quantum-logic inspired decision tree (QLDT) is based on quantum logic concepts and input values from the unit interval whereas the traditional decision tree is based on Boolean values. The logic behind the QLDT obeys the rules of a Boolean algebra. The QLDT is appropriate for classification problems where for a class decision several input values interact gradually with each other. The QLDT construction for a classification problem with n input attributes requires the computation of 2n minterms. The QLDT+ method, however, uses a heuristic for obtaining a QLDT with much smaller computational complexity. As result, the QLDT+ method can be applied to classification problems with a higher number of input attributes. Y1 - 2023 UR - https://dl.acm.org/doi/proceedings/10.1145/3589883 SN - 978-1-4503-9832-9 U6 - https://doi.org/10.1145/3589883.3589895 SP - 82 EP - 88 PB - Association for Computing Machinery CY - New York, NY, United States ER - TY - GEN ED - Wirsching, Günther ED - Wolff, Matthias ED - Schmitt, Ingo T1 - Quantenlogik : eine Einführung für Ingenieure und Informatiker N2 - Das wesentliche Ziel dieses Lehrbuchs ist es, die mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse auf der Grundlage der klassischen Logik und der Quantenlogik zu entwickeln. Die dafür erforderlichen mathematischen Sachverhalte werden so dargestellt und anhand von Beispielen motiviert, dass sie für angehende Ingenieure und Informatiker verständlich sind. - Kompakte logische Darstellung der mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse - Beispiele erläutern die Anwendung im Engineering Zielgruppen sind insbesondere Studierende der Ingenieurwissenschaften und der Informatik, aber auch Studierende der Mathematik oder der Physik können durch den anwendungsbezogenen Blick ihren Horizont erweitern. KW - Projektive Geometrie KW - Lineare Algebra KW - Verbandstheorie KW - Information Retrieval KW - Kognitive Systeme KW - Künstliche Intelligenz KW - vektor-symbolische Architekturen KW - Quantenmessung KW - Wahrscheinlichkeiten KW - Anfragesysteme KW - Informationskodierung Y1 - 2023 UR - https://link.springer.com/book/10.1007/978-3-662-66780-4 SN - 978-3-662-66779-8 SN - 978-3-662-66780-4 U6 - https://doi.org/10.1007/978-3-662-66780-4 PB - Springer Vieweg CY - Berlin ; Heidelberg ET - 1. Auflage ER - TY - GEN A1 - Schmitt, Ingo T1 - QLDT: A Decision Tree Based on Quantum Logic T2 - New Trends in Database and Information Systems. ADBIS 2022 N2 - Besides a good prediction a classifier is to give an explanation how input data is related to the classification result. Decision trees are very popular classifiers and provide a good trade-off between accuracy and explainability for many scenarios. Its split decisions correspond to Boolean conditions on single attributes. In cases when for a class decision several attribute values interact gradually with each other, Boolean-logic-based decision trees are not appropriate. For such cases we propose a quantum-logic inspired decision tree (QLDT) which is based on sums and products on normalized attribute values. In contrast to decision trees based on fuzzy logic a QLDT obeys the rules of the Boolean algebra. KW - Quantum Logic KW - Decission tree KW - Interpretable AI Y1 - 2022 SN - 978-3-031-15743-1 SN - 978-3-031-15742-4 U6 - https://doi.org/10.1007/978-3-031-15743-1_28 SP - 299 EP - 308 PB - Springer CY - Switzerland ER - TY - GEN A1 - Schmitt, Ingo T1 - QLC: A Quantum-Logic-inspired Classifier T2 - IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium N2 - Besides a good prediction a classifier is to give an explanation how the input data is related to the classification result. There is a general agreement that logic expressions provide a better explanation than other methods like SVM, logistic regression, and neural networks. However, a classifier based on Boolean logic needs to map continuous data to Boolean values which can cause a loss of information. In contrast, we design a quantum-logic-inspired classifier where continuous data are directly processed and the laws of the Boolean algebra are maintained. As a result from our approach we obtain a CQQL condition which provides good insights into the relation of input features to the class decision. Furthermore, our experiment shows a good prediction accuracy. Y1 - 2022 SN - 9781450397094 U6 - https://doi.org/10.1145/3548785.3548790 SP - 120 EP - 127 PB - Association of Computing Machinery CY - New York ER - TY - CHAP A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo ED - Yu, Han ED - Fang, Fei ED - Guodong, Long ED - Xue, Li ED - Weitong, Chen ED - Jiang, Jing ED - Yue, Lin ED - Bohan, Li T1 - A Relevance Feedback-Based Approach for Non-TI Clustering T2 - Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II N2 - Homogeneity of persons in a social network is based on the similarity of their attributes. Traditional clustering algorithms like hierarchical (agglomerative) clustering or DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, in addition, not all attributes do have the same influence on the network and thus may affect the network and compromise the quality of resulting clusters. We present an adaptive clustering-based quantitative weighting approach that is completely embedded in logic. To facilitate the user interaction with the system, we exploit the concept of relevance feedback. The approach takes user feedback as input to improve the quality of clusters and finds meaningful clusters where TI does not hold. In addition, it has the capability of providing the user alternative possible feedbacks that can be fulfilled. To test the approach, we evaluate a clustering distance regarding an ideal solution. Experiments demonstrate the benefits of our approach. Y1 - 2022 UR - https://www.springerprofessional.de/en/advanced-data-mining-and-applications/20081846?tocPage=2 SN - 978-3-030-95408-6 SN - 978-3-030-95407-9 SP - 381 EP - 393 PB - Springer International Publishing CY - Cham ER - TY - GEN A1 - Huber-Liebl, Markus A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Schmitt, Ingo A1 - beim Graben, Peter A1 - Wolff, Matthias T1 - Quantum-inspired Cognitive Agents T2 - Frontiers in Applied Mathematics and Statistics N2 - The concept of intelligent agents is—roughly speaking—based on an architecture and a set of behavioral programs that primarily serve to solve problems autonomously. Increasing the degree of autonomy and improving cognitive performance, which can be assessed using cognitive and behavioral tests, are two important research trends. The degree of autonomy can be increased using higher-level psychological modules with which needs and motives are taken into account. In our approach we integrate these modules in architecture for an embodied, enactive multi-agent system, such that distributed problem solutions can be achieved. Furthermore, after uncovering some weaknesses in the cognitive performance of traditionally designed agents, we focus on two major aspects. On the one hand, the knowledge processing of cognitive agents is based on logical formalisms, which have deficiencies in the representation and processing of incomplete or uncertain knowledge. On the other hand, in order to fully understand the performance of cognitive agents, explanations at the symbolic and subsymbolic levels are required. Both aspects can be addressed by quantum-inspired cognitive agents. To investigate this approach, we consider two tasks in the sphere of Shannon's famous mouse-maze problem: namely classifying target objects and ontology inference. First, the classification of an unknown target object in the mouse-maze, such as cheese, water, and bacon, is based on sensory data that measure characteristics such as odor, color, shape, or nature. For an intelligent agent, we need a classifier with good prediction accuracy and explanatory power on a symbolic level. Boolean logic classifiers do work on a symbolic level but are not adequate for dealing with continuous data. Therefore, we demonstrate and evaluate a quantum-logic-inspired classifier in comparison to Boolean-logic-based classifiers. Second, ontology inference is iteratively achieved by a quantum-inspired agent through maze exploration. This requires the agent to be able to manipulate its own state by performing actions and by collecting sensory data during perception. We suggest an algebraic approach where both kinds of behaviors are uniquely described by quantum operators. The agent's state space is then iteratively constructed by carrying out unitary action operators, while Hermitian perception operators act as observables on quantum eigenstates. As a result, an ontology emerges as the simultaneous solution of the respective eigenvalue equations. Tags: btuktqiai; btuktcogsys; btukttheseus; btuktqtheseus Y1 - 2022 UR - https://www.frontiersin.org/articles/10.3389/fams.2022.909873 U6 - https://doi.org/10.3389/fams.2022.909873 SN - 2297-4687 IS - 8 SP - 1 EP - 31 ER - TY - GEN A1 - Schmitt, Ingo A1 - Wang, Dongsheng A1 - Tiwari, Prayaq A1 - Shorfuzzaman, Mohammad T1 - Deep neural learning on weighted datasets utilizing label disagreement from crowdsourcing T2 - Computer Networks N2 - Experts and crowds can work together to generate high-quality datasets, but such collaboration is limited to a large-scale pool of data. In other words, training on a large-scale dataset depends more on crowdsourced datasets with aggregated labels than expert intensively checked labels. However, the limited amount of high-quality dataset can be used as an objective test dataset to build a connection between disagreement and aggregated labels. In this paper, we claim that the disagreement behind an aggregated label indicates more semantics (e.g. ambiguity or difficulty) of an instance than just spam or error assessment. We attempt to take advantage of the informativeness of disagreement to assist learning neural networks by computing a series of disagreement measurements and incorporating disagreement with distinct mechanisms. Experiments on two datasets demonstrate that the consideration of disagreement, treating training instances differently, can promisingly result in improved performance. KW - Neural networks KW - Assessed dataset KW - Instance weight Y1 - 2021 U6 - https://doi.org/10.1016/j.comnet.2021.108227 SN - 1389-1286 VL - 196 ER - TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Quantitative Weighting Approach for Non-TI Clustering T2 - Procedia Computer Science N2 - To enable users to influence clusters of a social network by their external feedback, we present an adaptive clustering-based quantitative weighting approach. Intrinsically, Persons in a social network are connected and their homogeneity is reflected based on the similarity of their attributes. But all attributes do not have the same influence on the network and thus may affect to form the network and compromise the quality of resulting clusters. The introduced weighting approach is completely embedded in logic and has the capability of assigning query weights to atomic conditions in user interaction. Hence, the presented system supports users by offering an intuitive feedback formulation without deeper knowledge of the underlying attributes of objects. Experiments demonstrate the benefits of our approach. KW - Clustering KW - Social Network KW - Condition Weighting KW - Triangle Inequality KW - Clique Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S1877050921007675 U6 - https://doi.org/10.1016/j.procs.2021.03.119 SN - 1877-0509 VL - 184 SP - 966 EP - 971 ER - TY - RPRT A1 - Schmitt, Ingo T1 - Generating CQQL Conditions from Classifying CNNs N2 - Convolutional neural networks are often successfully used for clas- si cation problems. Usually, a huge number of weights need to be learnt by use of training data. However, the learnt weights give no insight how the cnn really works. Thus, a cnn can be seen as a black box solution. In our approach we develop a method to generate a commuting quantum query language (cqql) condition from a sample derived from a given cnn or from training input. The query language cqql is inspired by quantum logic and its conditions obey the rules of a Boolean algebra. The evaluation of a cqql condition provides values from the unit interval [0; 1] and establishes therefore an elegant bridge between logic and a cnn. The underlying assumption is that a condition (a logic expression) gives much more understanding than pure cnn weights. Furthermore, the rich theory of Boolean algebra can be used for manipulating logic expressions. After extracting a cqql condition from a cnn or its training data we can use logic as a way to predict classes alternatively to a cnn. KW - CNN KW - Erklärbarkeit KW - CQQL KW - Quantenlogik KW - Quantum Logic KW - CNN KW - Abfragesprache KW - Zellulares neuronales Netzwerk Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-55503 SN - 1437-7969 CY - Cottbus ER - TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Non-TI Clustering in the Context of Social Networks T2 - Procedia Computer Science : The 11th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 3rd International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops N2 - Traditional clustering algorithms like K-medoids and DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, thus, may compromise the quality of resulting clusters. However, there are scenarios, for example in the context of social networks, where TI does not hold but a meaningful clustering is still possible. This paper investigates the consequences of TI violation with respect to different traditional clustering techniques and presents instead a clique guided approach to find meaningful clusters. In this paper, we use the quantum logic-based query language (CQQL) to measure the similarity value between objects instead of a distance function. The contribution of this paper is to propose an approach of non-TI clustering in the context of social network scenario. KW - Clustering KW - Social Network KW - Clique KW - Triangle Inequality Y1 - 2020 U6 - https://doi.org/10.1016/j.procs.2020.03.031 SN - 1877-0509 VL - 170 SP - 1186 EP - 1191 ER -