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Many of software engineering tools and systems are focused to monitoring source code quality and optimizing software development. Many of them use similar source code metrics to solve different kinds of problems. This inspired us to propose an environment for platform independent code monitoring, which supports employment of multiple software development(More)
Several authors have reported interesting results obtained by using untrained randomly initialized recurrent part of an recurrent neural network (RNN). Instead of long, difficult and often unnecessary adaptation process, dynamics based on fixed point attractors can be rich enough for further exploitation for some tasks. The principle explaining untrained(More)
Simulation of human behavior in various situations is nowadays heavily used in miscellaneous research fields (e.g. emergency exits design, psychology, riot or humanitarian help simulation). The Holy Grail is to identify key elements of human beings that drive our behavior and be able to sufficiently simulate them and replicate in artificial computer(More)
In this paper, we study an emergence of game strategy in multiagent systems. Symbolic and subsymbolic approaches are compared. Symbolic approach is represented by a backtrack algorithm with specified search depth, whereas the subsymbolic approach is represented by feedforward neural networks that are adapted by reinforcement temporal difference TD(lambda)(More)
Human behavior simulation is a challenging task because its nature and the variables affecting it are still unknown. However such simulations play important role in various fields, including army, police training, architecture, emergency exits design etc. In this paper we present a complex simulation of crowd based on the PECS psychological model. We(More)
In this thesis we focused on subsymbolic approach to machine game play problem. We worked on two different methods of learning. Our first goal was to test the ability of common feed-forward neural networks and the mixture of expert topology. We have derived reinforcement learning algorithm for mixture of expert network topology. This topology is capable to(More)