Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00224948" target="_blank" >RIV/68407700:21230/14:00224948 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life

Popis výsledku v původním jazyce

This chapter describes new methods of designing of autonomous agents. We inspire ourselves in fields as Artificial Intelligence, Ethology and Biology, while designing our agents. Typical course of agent?s life is similar to newly born animal, which continuously learns itself: consequently from basic information about its environment towards the ability to solve complex problems. Our latest architecture integrates several learning and action-selection mechanisms into one more complex system. The main advantages of such an agent are in its total autonomy, the ability to gain all information from a surrounding environment. Also, the ability to efficiently decompose potentially huge decision space into a hierarchy of smaller spaces enables the agent to successfully learn and ?live? also in very complex domains. Unsupervised learning is triggered mainly by agent?s predefined physiology and intentions which are autonomously created during his life. We present here theoretical background used

Název v anglickém jazyce

Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life

Popis výsledku anglicky

This chapter describes new methods of designing of autonomous agents. We inspire ourselves in fields as Artificial Intelligence, Ethology and Biology, while designing our agents. Typical course of agent?s life is similar to newly born animal, which continuously learns itself: consequently from basic information about its environment towards the ability to solve complex problems. Our latest architecture integrates several learning and action-selection mechanisms into one more complex system. The main advantages of such an agent are in its total autonomy, the ability to gain all information from a surrounding environment. Also, the ability to efficiently decompose potentially huge decision space into a hierarchy of smaller spaces enables the agent to successfully learn and ?live? also in very complex domains. Unsupervised learning is triggered mainly by agent?s predefined physiology and intentions which are autonomously created during his life. We present here theoretical background used

Druh

O - Ostatní výsledky

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů