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Motivated learning (ML) is a new biologically inspired machine learning method. It is the combination of a reinforcement learning (RL) algorithm and a system that creates hierarchy of goals. The goal creation system is concerned with creating new internal goals, building a hierarchy of them, and controlling the agent's behavior according to this constituted(More)
A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of(More)
Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to(More)
The paper describes a test-bench model for braincomputer interface research based on EEG signals. The test-bench is going to be used for students training and education. The goal is to prepare modern Brain-Computer Interface development environment in order to create interest about this topic among the students.
The paper presents a structural model of a cognitive agent and its Blender implementation. Built in a virtual world, the agent is able to act autonomously, observe its environment and learn from its actions using principles of motivated learning. We discuss both its organizational structure and tools we used to develop virtual implementation of the agent.(More)
The rapid growth in availability of new biomedical systems and devices capable of acquiring biosignals for disease diagnosis and health monitoring require rigorous processing. Biomedical research by nature depends on integrated problem solving software environment and often involves people located at different geographical positions. The reusability of(More)
In this paper we present a model of single-hop type wireless sensor network with random access and oneway transmission. In the paper, we analyze the WSN single-hop network using one single radio frequency, such that all nodes are divided into several groups depending on the average time between the transmissions. We replaced deterministic number of nodes in(More)
This work presents an application of Wireless Sensor Network (WSN) of random access with one-way transmission to the monitoring of hospital patients. In the paper, we consider WSN single-hop network using one single radio frequency, such that all nodes are divided into several groups depending on the average time between the transmission due to the(More)
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