Summary and conclusion

In the previous sections, an overview about the field of Cognitive Technical Systems is given. The sections include definitions of central paradigms and detailed descriptions of important approaches regarding knowledge representation, reasoning, and learning. Furthermore, some examples for integrated cognitive architectures combining several models and mechanisms are described since it is commonly assumed that only such sys- tems are able to model and simulate the variety of human cognition.

In this regard, several different approaches with own concepts of the system’s structure exist. Classically, cognitive architectures (especially those that are related to Cognitive Psychology) propose a few kinds of predominantly explicit representations. However, the trend goes to systems representing knowledge explicit and implicit by several kinds of representations (e.g., production rules, neural networks etc.). Nevertheless, some basic paradigms as the step ladder model or the differentiation among working memory, long- term memory, and short-term memory can be found in a large number of approaches. Another trend is that the source code of components which are typically part of cogni- tive architectures (models, learning methods etc.) as well as the source code of whole

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architectures itself (CLARION etc.) are available for free. Hence, they can be modified, tested, and easily integrated into other applications.

Classical cognitive architectures are typically intended to model human performance. However, they are more and more applied to control technical systems (sometimes as a part of another system). Furthermore, some of the younger cognitive architectures are basically intended for this kind of application. Typical demonstration examples are mobile robots, humanoid robots, or software agents. In simulations or real experi- ments, architectures’ functionalities as the solution of certain problems, the acquisition of knowledge, or the communication with humans are shown. However, the format of used memory is often aligned to a certain application and the system borders are typi- cally well defined.

It can be concluded that all presented approaches might be useful to solve some important problems and reach some research goals. This could be the communication by spoken language with humans, the exact modeling of human behavior, or the learning of how to interact with a certain environment. However, in order to develop a general purpose system, which can enable different technical systems to interact autonomously with their environment, research should be more related to

• fundamental methodical approaches for the unified formalization of real world’s structure and dynamics,

• application-independent implementation to arbitrary kinds of technical systems, and

• realization of real autonomy by flexible representations of knowledge and learning mechanisms.

Although some of today’s systems solve some aspects such as embodiment and learning very well, a lot of approaches neither consider them nor realize them in a flexible man- ner. In summary, no system or concept exists which can fulfill all the mentioned issues sufficiently.

Although architectures or parts of it are partially recycled, each approach proposes different models, functions, and system structures. However, the development of new candidates of such systems require a lot of work and time. In contrast to that, ap- proaches with variable structures and the ability to add and modify new models and functions more easily are a promising alternative. Consequently, to handle the arising complexity and to simplify the communication between system designers, specialized meta-modeling approaches are necessary. Such approaches should be able to formal- ize the interaction between cognitive systems and real world environments as well as the representation of that by corresponding mental structures in a unified manner.

The development of many architectures is neither intended to control technical systems nor driven by experiments in real world environments. In this respect, the

question arises whether a model of human performance can be transferred one by one to a technical system whose interaction is based on totally different requirements re- garding hardware and information processing in general. Furthermore, in the case of architectures that are developed in order to control a certain technical system, the used representations and functions are often specified to the related hardware and applica- tion respectively. Although this can lead to successful results, the learning of unexpected facts and relations is avoided.

Although many systems provide learning mechanisms, it seems to be sometimes not intended from the very beginning. However, learning is a fundamental characteristic of cognitive systems, influences their structure, and can not be simply added. Hence, this might be the reason why some systems only provide simple learning mechanisms as the storage of events or experiences respectively. However, for the realization of real autonomy, it is moreover necessary that all cognitive functions and used models are in- fluenced by learning. Especially, learning regarding a system’s recognition and attention capabilities is often missing.

This thesis describes an approach considering (in contrast to other approaches) all mentioned demands for representation, learning, and embodiment, in order to realize Cognitive Technical Systems. Here, Situation-Operator-Modeling is applied as method- ical background to realize a framework consisting of several models and mechanisms, which can be used to realize an integrated cognitive architecture based on a unified formalization of the real world. The approach is related to arbitrary technical systems enabling the portability of knowledge and functionalities. The development is driven by experiments with a mobile robot, whereas human cognition serves as inspiration for the realization of a flexible and adaptive system behavior. The internal representation is hierarchized in two dimensions and realizes the situational and task-relevant reduction of complexity. Furthermore, knowledge can be illustrated clearly and is therefore acces- sible by humans. All cognitive functions use models that can be extended and refined from interaction.

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