Real-Time Action Model Learning with Online Algorithm 3 SG

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Real-Time Action Model Learning with Online Algorithm 3 SG

Original language description

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

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JC - Computer hardware and software

OECD FORD branch

—

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2014

Confidentiality

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

Name of the periodical

Applied Artificial Intelligence

ISSN

0883-9514

e-ISSN

—

Volume of the periodical

28

Issue of the periodical within the volume

7

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

690-711

UT code for WoS article

000340389400003

EID of the result in the Scopus database

—