Date of Award

Summer 7-2016

Document Type

Thesis

Degree Name

Bachelor Degree

Department

Computer Science

First Advisor

Krenare Pireva

Second Advisor

Vincenzo Piuri

Language

English

Abstract

The rise of Intelligent Systems has happened gradually, then suddenly. They are gradual because we are aware that this field of computing has come a long way along with the history of computers. Yet, the sudden astonishing changes that affect mankind seem to take everyone in surprise. Their occurrence is reshaping the real world and our interaction with our digital life is changing in profound ways. Can computers think? We don’t have evidence on that, whatever the answer to that question is. But what we know is that computers do learn. Indeed, the whole process of computer evolution revolves around machines that are able to follow instructions and practice and eventually get better at what they are initially produced to accomplish. Consequently, the questions that we try to answer are related to the types of learning that intelligent programs use with special regards to one of the most researched methods of Machine Learning – Reinforcement Learning. On the other hand, it is crucial to apply the intelligent self-learning machines in industry, environment, enterprise, medicine and all the other sectors where we need to see the substantial changes that correspond with the era of machines that can learn. The intersection point in this research is the application of intelligent programs in industry using a very specific learning technique – Reinforcement Learning.

DOI

10.33107/ubt-etd.2016.1518

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