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| rdf:type
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| Description
| - 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 for creation of our agents, then we mention several main ideas behind our research are presented. Finally, we describe our latest architectures of autonomous agents. Several experiments which were concluded in order to validate the expected abilities of our agents are also presented. One of main contributions of our research is in proposing a new hybrid domain independent hierarchical planner. This planner combines classical planning system with hierarchical reinforcement learning. The ability to accommodate changing ideas about causality allows the creature to exist in and adapt to a dynamic world.
- 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 for creation of our agents, then we mention several main ideas behind our research are presented. Finally, we describe our latest architectures of autonomous agents. Several experiments which were concluded in order to validate the expected abilities of our agents are also presented. One of main contributions of our research is in proposing a new hybrid domain independent hierarchical planner. This planner combines classical planning system with hierarchical reinforcement learning. The ability to accommodate changing ideas about causality allows the creature to exist in and adapt to a dynamic world. (en)
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| Title
| - Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life
- Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life (en)
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| skos:prefLabel
| - Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life
- Ethology-Inspired Design of Autonomous Agents in Domain of Artificial Life (en)
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| skos:notation
| - RIV/68407700:21230/14:00224948!RIV15-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/14:00224948
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Artificial Intelligence (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Nahodil, Pavel
- Vítků, Jaroslav
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| http://linked.open...n/vavai/riv/zamer
| |
| http://localhost/t...ganizacniJednotka
| |
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