## 8 Results and Discussion
#### Directions of Future Research  
It should be obvious to the reader that any artificial computing system is usually an imperfect and often deliberately simplified approximation of its natural prototype. As this is also the case with the architecture proposed above, we would like to outline the following general directions for further development of the presented solution (both in terms of potential additional elements of the very model itself and in terms of the handled types of statements). Firstly, this article discusses the agent's cognitive competences regarding acquiring and processing beliefs about the states of objects in only one base mental space. This limitation is only of an editorial nature. It should be remembered that designing agents for real practical contexts is almost always associated with the need to equip them with cognitive competences (including linguistic competences) relating to more than one basic mental space. Each of such spaces then corresponds to a different type of atomic object in the real world. Moreover, more complete and externally grounded models of embedded agent ontology should also provide for the possibility of conceptualizing the space of complex objects, i.e., those that can be decomposed into a collection of two or more atomic objects. It is quite obvious that expanding the microstructure of objects in the mental spaces operated by the agent always involves the need to introduce more complex measures of distance or similarity. As a result, the methods of learning categories with a prototype need to be tailored to the particular implementation. Secondly, this article introduces a category model with a prototype equipped with a spherical core of the category. Adopting such a solution is sufficient to maintain the logical and commonsense coherence of the argumentation presented in this article. It seems interesting to investigate more flexible definitions of the cores and to allow for solutions in which the core takes a form of, for example, a convex figure with a clearly indicatedcenter acting as a prototype. An analogous postulate can also be formulated regarding the definition of the epistemic neighborhood. Thirdly, this article focuses on a specific class of models and computational methodologies suitable for realizing specific and effective implementations of the proposed agent. It should be noted that the suggested models and implementation methodologies belong to the group of classical computational tools that can be easily applied to the case of the agent's sub-systems with symbolic and relational representation of knowledge. This is particularly visible in the examples proposed in the text of this article. However, the increasing availability of powerful computers expands the spectrum of implementation tools that can be used, e.g., with deep learning (connectionist) techniques requiring significant, but currently already available, computational power. Developing a way to use these techniques to implement the process of learning for a model of a category with a prototype, and in the longer term also to induce the basic mental spaces themselves, seems to be an interesting and potentially very valuable direction for future research. Regarding effective implementations, it is also important to further investigate a way in which the whole agent uses the category learning procedure. Categories need to be periodically evaluated against new learning samples and in terms of their internal consistency. This results in a potential need for an agent to selectively re-initiate a process of category formation for a subset of previously learned categories. Some insights on the maintenance of the categorization can be drawn from such works as [PERSON] et al. [67]. Fourthly, the model presented in this article assumes complete observational information regarding the state of the observed object, which from the point of view of many applications is an unacceptable idealization.