TY - GEN A1 - Beim Graben, Peter A1 - Huber-Liebl, Markus A1 - Klimczak, Peter A1 - Wirsching, Günther T1 - Machine Semiotics T2 - arXiv N2 - Recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome the shortcomings of current speech assistive devices. For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the conventional meaning of an utterance. Rather, they draw conversational implicatures in the sense of (neo-)Gricean pragmatics. For speech assistive devices, the learning of machine-specific meanings of human utterances, i.e. the fossilization of conversational implicatures into conventionalized ones by trial and error through lexicalization appears to be sufficient. Using the quite trivial example of a cognitive heating | device, we show that — based on dynamic semantics — this process can be formalized as the reinforcement learning of utterance-meaning pairs (UMP). KW - Semiotics KW - machine learning KW - dynamic semantics KW - pragmatic implicatures KW - fossilization Y1 - 2023 UR - https://arxiv.org/abs/2008.10522v2 SP - 1 EP - 48 ER - 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 -