<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Čertický</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Action Models and Their Induction</style></title><secondary-title><style face="normal" font="default" size="100%">Organon F</style></secondary-title><translated-title><style face="normal" font="default" size="100%">Action Models and Their Induction</style></translated-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Action model</style></keyword><keyword><style  face="normal" font="default" size="100%">artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">learning</style></keyword><keyword><style  face="normal" font="default" size="100%">planning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.klemens.sav.sk/fiusav/doc/organon/prilohy/2013/2/206-215.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">206-215</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">Abstract: By action model, we understand any logic-based representation of effects and executability preconditions of individual actions within a certain domain. In the context of artificial intelligence, such models are necessary for planning and goal-oriented automated behaviour. Currently, action models are commonly hand-written by domain experts in advance. However, since this process is often difficult, time-consuming, and error-prone, it makes sense to let agents learn the effects and conditions of actions from their own observations. Even though the research in the area of action learning, as a certain kind of inductive reasoning, is relatively young, there already exist several distinctive action learning methods. We will try to identify the collection of the most important properties of these methods, or challenges that they are trying to overcome, and briefly outline their impact on practical applications.</style></abstract><custom3><style face="normal" font="default" size="100%">206215</style></custom3><custom5><style face="normal" font="default" size="100%">1</style></custom5></record></records></xml>