@inproceedings{WolffWirschingHuberetal., author = {Wolff, Matthias and Wirsching, G{\"u}nther and Huber, Markus and Beim Graben, Peter and R{\"o}mer, Ronald and Schmitt, Ingo}, title = {A Fock Space Toolbox and Some Applications in Computational Cognition}, series = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, booktitle = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, editor = {Karpov, Alexey and Jokisch, Oliver and Potapova, Rodmonga}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-99578-6}, doi = {10.1007/978-3-319-99579-3}, abstract = {We present a Matlab toolbox, called "FockBox", handling Fock spaces and objects associated with Fock spaces: scalars, ket and bra vectors, and linear operators. We give brief application examples from computational linguistics, semantics processing, and quantum logic,demonstrating the use of the toolbox.}, language = {en} } @inproceedings{BeimGrabenHuberRoemeretal., author = {Beim Graben, Peter and Huber, Markus and R{\"o}mer, Ronald and Schmitt, Ingo and Wolff, Matthias}, title = {Der Fockraum als Labyrinth: Wissensrepr{\"a}sentation und Probleml{\"o}sungen am Beispiel des Mouse-Maze-Problems}, series = {Elektronische Sprachsignalverarbeitung, Tagungsband der 29. Konferenz, Ulm, 7.-9. M{\"a}rz 2018}, booktitle = {Elektronische Sprachsignalverarbeitung, Tagungsband der 29. Konferenz, Ulm, 7.-9. M{\"a}rz 2018}, editor = {Berton, Andr{\´e} and Haiber, Udo and Minker, Wolfgang}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-128-3}, pages = {167 -- 174}, language = {de} } @inproceedings{SchmittRoemerWirschingetal., author = {Schmitt, Ingo and R{\"o}mer, Ronald and Wirsching, G{\"u}nther and Wolff, Matthias}, title = {Denormalized Quantum Density Operators for Encoding Semantic Uncertainty in Cognitive Agents}, series = {8th IEEE International Conference on Cognitive Infocommunications CogInfoCom 2017 Proceedings, September 11-14, 2017 Debrecen, Hungary}, booktitle = {8th IEEE International Conference on Cognitive Infocommunications CogInfoCom 2017 Proceedings, September 11-14, 2017 Debrecen, Hungary}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5386-1264-4}, pages = {165 -- 170}, language = {en} } @inproceedings{GiurcaBaierSchmitt, author = {Giurca, Adrian and Baier, Daniel and Schmitt, Ingo}, title = {What is in a Like? : Preference Aggregation on the Social Web}, series = {Data Science, Learning by Latent Structures, and Knowledge Discovery}, booktitle = {Data Science, Learning by Latent Structures, and Knowledge Discovery}, editor = {Lausen, Berthold and Krolak-Schwerdt, Sabine and B{\"o}hmer, Matthias}, publisher = {Springer}, address = {Heidelberg}, isbn = {978-3-662-44982-0}, doi = {10.1007/978-3-662-44983-7_38}, pages = {435 -- 444}, language = {en} } @book{Schmitt, author = {Schmitt, Ingo}, title = {{\"A}hnlichkeitssuche in Multimedia-Datenbanken : Retrival, Suchalgorithmen und Anfragebehandlung}, publisher = {Oldenbourg}, address = {M{\"u}nchen}, isbn = {978-3-486-57907-9}, pages = {445}, language = {de} } @misc{SahaSchmitt, author = {Saha, Sanjit Kumar and Schmitt, Ingo}, title = {Quantitative Weighting Approach for Non-TI Clustering}, series = {Procedia Computer Science}, volume = {184}, journal = {Procedia Computer Science}, issn = {1877-0509}, doi = {10.1016/j.procs.2021.03.119}, pages = {966 -- 971}, abstract = {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.}, language = {en} } @inproceedings{SchmittNuernberger, author = {Schmitt, Ingo and N{\"u}rnberger, Andreas}, title = {Image Database Search using Fuzzy and Quantum Logic}, series = {IEEE International Fuzzy Systems Conference, 2007, FUZZ-IEEE 2007, 23 - 26 July 2007, London, United Kingdom}, booktitle = {IEEE International Fuzzy Systems Conference, 2007, FUZZ-IEEE 2007, 23 - 26 July 2007, London, United Kingdom}, publisher = {IEEE Service Center}, address = {Piscataway, NJ}, isbn = {1-4244-1209-9}, pages = {1 -- 6}, language = {en} } @misc{Schmitt, author = {Schmitt, Ingo}, title = {QLDT: A Decision Tree Based on Quantum Logic}, series = {New Trends in Database and Information Systems. ADBIS 2022}, journal = {New Trends in Database and Information Systems. ADBIS 2022}, publisher = {Springer}, address = {Switzerland}, isbn = {978-3-031-15743-1}, doi = {10.1007/978-3-031-15743-1_28}, pages = {299 -- 308}, abstract = {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.}, language = {en} } @misc{Schmitt, author = {Schmitt, Ingo}, title = {QLC: A Quantum-Logic-inspired Classifier}, series = {IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium}, journal = {IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium}, publisher = {Association of Computing Machinery}, address = {New York}, isbn = {9781450397094}, doi = {10.1145/3548785.3548790}, pages = {120 -- 127}, abstract = {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.}, language = {en} } @incollection{Schmitt, author = {Schmitt, Ingo}, title = {Incorporating Weights into a Quantum-Logic-Based Query Language}, series = {Quantum-Like Models for Information Retrieval and Decision-Making}, booktitle = {Quantum-Like Models for Information Retrieval and Decision-Making}, editor = {Aerts, Diederik and Khrennikov, Andrei and Melucci, Massimo and Bourama, Toni}, edition = {1. Auflage}, publisher = {Springer Nature Switzerland AG, Springer International Publishing}, address = {Cham}, isbn = {978-3-030-25912-9}, issn = {2520-1948}, doi = {10.1007/978-3-030-25913-6_7}, pages = {129 -- 143}, abstract = {Traditional database query languages are based on set theory and crisp first order logic. However, many applications require imprecise conditions which return result objects associated with a degree of fulfillment. For example, a research paper should be reviewed by giving a degree of fulfillment for originality and relevance. Very often, imprecise conditions of a query are of different importance. Thus, a query language should allow the user to give conditions different weights. This paper proposes a weighting approach which is realized by means of conjunction, disjunction, and negation. Thus, our weighting is completely embedded within a logic. As a result, logical rules are preserved and can be used for query reformulation and optimization. As underlying logic-based query language, we use the CQQL query language. Furthermore we demonstrate that our weighting approach is applicable to further logic-based query languages.}, language = {en} } @incollection{SahaSchmitt, author = {Saha, Sanjit Kumar and Schmitt, Ingo}, title = {A Relevance Feedback-Based Approach for Non-TI Clustering}, series = {Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings, Part II}, booktitle = {Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings, Part II}, editor = {Yu, Han and Fang, Fei and Guodong, Long and Xue, Li and Weitong, Chen and Jiang, Jing and Yue, Lin and Bohan, Li}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-95408-6}, pages = {381 -- 393}, abstract = {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.}, language = {en} } @techreport{Schmitt, author = {Schmitt, Ingo}, title = {Generating CQQL Conditions from Classifying CNNs}, address = {Cottbus}, issn = {1437-7969}, doi = {10.26127/BTUOpen-5550}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-55503}, pages = {15}, abstract = {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.}, language = {en} } @misc{HuberLieblRoemerWirschingetal., author = {Huber-Liebl, Markus and R{\"o}mer, Ronald and Wirsching, G{\"u}nther and Schmitt, Ingo and beim Graben, Peter and Wolff, Matthias}, title = {Quantum-inspired Cognitive Agents}, series = {Frontiers in Applied Mathematics and Statistics}, journal = {Frontiers in Applied Mathematics and Statistics}, number = {8}, issn = {2297-4687}, doi = {10.3389/fams.2022.909873}, pages = {1 -- 31}, abstract = {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}, language = {en} } @techreport{NoackSchmitt, author = {Noack, Tino and Schmitt, Ingo}, title = {A Cyclic Process Model for Monitoring Mobile Cyber-Physical Systems}, publisher = {BTU}, address = {Cottbus}, pages = {12 Bl.}, language = {en} } @techreport{SchmittBalko, author = {Schmitt, Ingo and Balko, S.}, title = {Signature Indexing and Self-Refinement in Metric Spaces}, publisher = {BTU}, address = {Cottbus}, language = {en} }