Difference between revisions of "User:MirzaRamicic"
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|advisor=AndreaBonarini | |advisor=AndreaBonarini | ||
| − | |resarea=Machine Learning;Affective Computing; | + | |resarea=Machine Learning;Affective Computing;Agents, Multiagent Systems, Agencies |
|status=active | |status=active | ||
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| + | PhD student in the area of Computer Engineering. | ||
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| + | My primary focus is Artificial Intelligence where I tend to adapt a interdisciplinary approach in order to address the problems that are commonly present in Machine Learning. | ||
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| + | == Main Research Summary == | ||
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| + | From its inception, the research in artificial intelligence exhibited a rather strange dichotomy. One path led to “utilitarian” agents whose unique purpose was to perform a task or achieve a goal in the most efficient, possible way. The other approach, which is the main concern of this research was mostly facing the question: How far has the AI come close to the most complex analog computational system known: the human brain. That being said, it is not just necessary for the agent to reach its designated goal in a most efficient way, but in the most human-like, possible way. This implies that the process of reaching the goal itself should exhibit common behavioral patterns found in humans. | ||
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| + | The underlying driving force influencing/motivating complex organism’s behavior has always been the evolutionary drive. In humans, contrary to animals, the primal instincts are replaced by complex emotions that involve communication and social interaction. This makes the evolutionary drive subtler and implicit, but it still remains big motivating factor. | ||
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| + | The agents in the proposed artificial life system will be motivated by the primal goal of exploring their environment for the purpose of adapting to it, therefore ensuring the best chances of their survival in the evolutionary context. | ||
Latest revision as of 16:59, 26 December 2015
Mirza Ramicic
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| E-Mail: | mirza.ramicic@polimi.it |
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| Status: | active |
PhD student in the area of Computer Engineering.
My primary focus is Artificial Intelligence where I tend to adapt a interdisciplinary approach in order to address the problems that are commonly present in Machine Learning.
Main Research Summary
From its inception, the research in artificial intelligence exhibited a rather strange dichotomy. One path led to “utilitarian” agents whose unique purpose was to perform a task or achieve a goal in the most efficient, possible way. The other approach, which is the main concern of this research was mostly facing the question: How far has the AI come close to the most complex analog computational system known: the human brain. That being said, it is not just necessary for the agent to reach its designated goal in a most efficient way, but in the most human-like, possible way. This implies that the process of reaching the goal itself should exhibit common behavioral patterns found in humans.
The underlying driving force influencing/motivating complex organism’s behavior has always been the evolutionary drive. In humans, contrary to animals, the primal instincts are replaced by complex emotions that involve communication and social interaction. This makes the evolutionary drive subtler and implicit, but it still remains big motivating factor.
The agents in the proposed artificial life system will be motivated by the primal goal of exploring their environment for the purpose of adapting to it, therefore ensuring the best chances of their survival in the evolutionary context.
