Purpose -Conventional rule-based systems are insufficient for description of complex dynamic systems requiring nontrivial decision procedures. Fuzzy cognitive maps seem to be convenient to overcome these limitations. However, they lack ability of self-learning and therefore some adaptation approaches are needed. The purpose of this paper is both to show the use of fuzzy cognitive maps for such systems and to present migration algorithms as convenient adaptation means. Design/methodology/approach -Some problems of a complex dynamic system description by knowledge-based means are discussed. Fuzzy cognitive maps are presented as a possible way to solve these problems followed by description of migration algorithms as their adaptation means. Their use is clarified on an example of the so-called parking problem based on path planning using a graph search algorithm and a traffic simulation system. Findings -After series of simulations the reality of the proposed system and selected methods with their modifications was proved. It has shown the robustness of the presented solution under circumstances of uncertainty, too.Research limitations/implications -The paper points to stability investigation of the proposed approach introducing uncertainties into the traffic simulation system to take into account, e.g. unexpected events. Further, a possibility of developing a linguistic information retrieval system is mentioned. Practical implications -The proposed approach can find various implementations not only in planning tasks but also in robotic navigation and multi-agent applications in general. In addition, it suggests possibilities of knowledge-based systems, directly using human-like approaches, to areas of decision making under uncertainties and contradictories. Originality/value -An new modification of migration algorithms for adaptation of parameters for fuzzy cognitive maps is introduced and compared to other known self-learning methods. Further, the concept of a traffic simulation system for path planning is presented.

No abstract

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

Abstract-We propose hierarchical multi agent control system based on rule based fuzzy system for pursuit-evasion task and state a new representation of this type of game that is based on fuzzy logic. This approach enables improvement of the rule base under uncertain conditions and can process a priori inserted expert knowledge. Example application domain includes reckon and guard robots, research space probes, coordination of multiple mine sweeping devices or autonomous rescue teams..

No abstract