Real-Time Action Model Learning with Online Algorithm 3 SG

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00227813" target="_blank" >RIV/68407700:21230/14:00227813 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/08839514.2014.927692" target="_blank" >http://dx.doi.org/10.1080/08839514.2014.927692</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/08839514.2014.927692" target="_blank" >10.1080/08839514.2014.927692</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Real-Time Action Model Learning with Online Algorithm 3 SG

Popis výsledku v původním jazyce

An action model, as a logic-based representation of action?s effects and preconditions, constitutes an essential requirement for planning and intelligent behavior. Writing these models by hand, especially in complex domains, is often a time-consuming anderror-prone task. An alternative approach is to let the agents learn action models from their own observations. We introduce a novel action learning algorithm called 3SG (Simultaneous Specification, Simplification, and Generalization), analyze and provesome of its properties, and present the first experimental results (using real-world robots of the SyRoTek platform and simulated agents in action computer game Unreal Tournament 2004). Unlike the majority of available alternatives, 3SG produces probabilistic action models with conditional effects and deals with action failures, sensoric noise, and incomplete observations. The main difference, however, is that 3SG is an online algorithm, which means it is rather fast (polynomial in the

Název v anglickém jazyce

Real-Time Action Model Learning with Online Algorithm 3 SG

Popis výsledku anglicky

An action model, as a logic-based representation of action?s effects and preconditions, constitutes an essential requirement for planning and intelligent behavior. Writing these models by hand, especially in complex domains, is often a time-consuming anderror-prone task. An alternative approach is to let the agents learn action models from their own observations. We introduce a novel action learning algorithm called 3SG (Simultaneous Specification, Simplification, and Generalization), analyze and provesome of its properties, and present the first experimental results (using real-world robots of the SyRoTek platform and simulated agents in action computer game Unreal Tournament 2004). Unlike the majority of available alternatives, 3SG produces probabilistic action models with conditional effects and deals with action failures, sensoric noise, and incomplete observations. The main difference, however, is that 3SG is an online algorithm, which means it is rather fast (polynomial in the

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Applied Artificial Intelligence

ISSN

0883-9514

e-ISSN

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Svazek periodika

28

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

690-711

Kód UT WoS článku

000340389400003

EID výsledku v databázi Scopus

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