TY - GEN A1 - Römer, Ronald A1 - Huber-Liebl, Markus A1 - Wolff, Matthias T1 - Quantum-inspired representation and querying of semiotic object properties by cognitive agents T2 - 2025 IEEE 16th International Conference on Cognitive Infocommunications (CogInfoCom) N2 - Advanced intelligence often relies on the ability to build and use models. They allow the representation of knowledge, enable predictions, and serve to plan goal-directed actions. However, the successfull use of models requires a certain level of competence in interacting with models. This competence can be acquired by exploiting the separation of the cognitive agent from the objects represented in the model; because it allows the differentiation of individual semiotic levels. This includes the separation of self-sufficient and non self-sufficient object attributes. Non self-sufficient attributes that the agent assigned to potential target objects during the exploration phase form the starting point for the information requirement regarding the self-sufficient attributes of these objects. These attributes are required for target selection and the subsequent alignment of the model-based planning. This process is characterized by a series of queries to the environment model and thus shows a certain similarity to information retrieval (IR). Therefore, in this work we particularly investigate quantum-inspired IR-methods for representation and querying. We demonstrate that they are suitable for modelling cognitive effort and behavior, as they represent a realistic and universally applicable descriptive tool. KW - Information retrieval KW - Semiotics KW - Planning Y1 - 2025 SN - 979-8-3503-5691-5 U6 - https://doi.org/10.1109/CogInfoCom66819.2025.11200567 SP - 000129 EP - 000134 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Huber-Liebl, Markus A1 - Rosenow, Tillmann A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Wolff, Matthias T1 - Knowledge representation and querying with tensors for a class of practical test problems T2 - Refereed Paper Proceedings - KM Conference 2025 – Siena, Italy N2 - We further promote the idea of quantum inspiration and propose to equip cognitive systems not only with tensors for data representation but also for operation representation and querying. We discuss ideas from information retrieval and translate them into our method of behavioral control. We formalize the method with tensor algebra and discuss its extensions to learning. At the example of the problem of 𝑁-dimensional aiming, we visualize the transfer of our stated theoretical foundation to a class of practical test problems. KW - Knowledge representation KW - Cognitive agents KW - Tensor algebra KW - Quantum inspiration Y1 - 2025 UR - https://www.iiakm.org/conference/proceedings/KM2025_RefereedProceedingsPapers.pdf SP - 111 EP - 123 PB - International Institute for Applied Knowledge Management ER - TY - GEN A1 - Schmitt, Ingo A1 - Sowoidnich, Kay A1 - Gosswami, Tapashi A1 - Sumpf, Bernd A1 - Maiwald, Martin A1 - Wolff, Matthias T1 - PCA-based peak feature selection for classification of spectroscopic datasets T2 - Journal of chemometrics N2 - Reducing feature dimensionality in spectroscopic data is crucial for efficient analysis and classification. Using all available features for classification typically results in an unacceptably high runtime and poor accuracy. Popular feature extraction methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and autoencoders, reduce feature dimensionality by extracting latent features that can be challenging to interpret. To enable better human interpretation of the classification model, we avoid extraction methods and instead propose applying feature selection methods. In this work, we develop an innovative PCA-based feature selection method for spectroscopic data, providing an essential subset of the original features. As an important advantage, no prior knowledge about the characteristic signals of the respective target substance is required. In this proof-of-concept study, the proposed method is initially characterized using simulated Raman and infrared absorption datasets. From the top five PCA eigenvectors of spectroscopic data, we identify a set of three top peaks each at specific wavenumbers (features). The compact set of selected features is then used for classification tasks applying a decision tree. Based on two well-defined spectroscopic datasets, our study demonstrates that our new method of PCA-based peak finding outperforms selected other approaches with regard to interpretability and accuracy. For both investigated datasets, accuracies greater than 97% are achieved. Our approach shows large potential for accurate classification combined with interpretability in further scenarios involving spectroscopic datasets. KW - Cclassification KW - Decision tree KW - Peak finding KW - Principal component analysis KW - Spectroscopic data Y1 - 2025 U6 - https://doi.org/10.1002/cem.70074 SN - 0886-9383 SN - 1099-128X VL - 39 IS - 11 SP - 1 EP - 14 PB - John Wiley & Sons Ltd. CY - New York, NY ER -