ACTION MODELS AND THEIR INDUCTION

• Authors: Michal Čertický
• Languages of publication: EN

Abstracts

EN

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.

Keywords

EN

ACTION MODEL; ARTIFICIAL INTELLIGENCE; LEARNING; PLANNING

• Publisher: Institute of Philosophy of the Slovak Academy of Sciences and Institute of Philosophy of Academy of Sciences of Czech Republic
• Journal: Organon F. medzinárodný časopis pre analytickú filozofiu
• Year: 2013
• Volume: 20
• Issue: suppl. 2
• Pages: 206 – 215

Contributors

author

Michal Čertický

• Katedra aplikovanej informatiky, UK, Mlynská dolina, 842 48 Bratislava, Slovak Republic