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 - 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 - 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 - RPRT A1 - Noack, Tino A1 - Schmitt, Ingo T1 - A Cyclic Process Model for Monitoring Mobile Cyber-Physical Systems Y1 - 2012 PB - BTU CY - Cottbus ER - TY - RPRT A1 - Schmitt, Ingo A1 - Balko, S. T1 - Signature Indexing and Self-Refinement in Metric Spaces Y1 - 2012 PB - BTU CY - Cottbus ER -