Automated planning and machine learning
23 June 2011

Nell'ambito del programma Visiting Professor 2010 finanziato dalla Regione Autonoma della Sardegna


 
il Prof. Daniel Borrajo,
 
University of Carlos III, Madrid

 
terrà una serie di seminari sul tema
 
"Automated planning and machine learning".

Seguono informazioni sull'agenda relativa ai seminari e un breve sommario sulle tematiche che saranno affrontate.

Instructor:     Prof. Daniel Borrajo, University of Carlos III, Madrid
Duration:     8 hours
Schedule:     Thu 23/6 (15-18), Mon 27/6 (15-18), Wed 29/6 (15-17)
Venue:     DIEE Building B - room B1
Topics:     Automated planning and machine learning
Organizer:     Giuliano Armano
Dep. of Electrical and Electronic Engineering
University of Cagliari, Italy
Email: armano@diee.unica.it

[ 3hours + 3hours ]
The goal of the subfield of Artificial Intelligence called Planning&Scheduling is the development of software tools that generate plans of actions for physical  (humans or robots) or software agents. Examples of successful deployment of this technology have been shown on tasks such are: robotics, military operations, or civil emergencies. Very efficient general algorithms exist that work indepedently of the task. We will cover the main approaches currently used in automated planning.

[ 2hours ]
In order to apply automated planning to real-world tasks, there are two major configuration tasks that have to be performed manually:  defining the actions model of the domain (as what actions the robot can execute and what are the effects of those actions), and defining the domain specific knowledge to make the software efficient for that domain (when performing an action is better than performing another one). This is what machine learning techniques can help with. We will cover some of these machine learning approaches applied to automated planning during the seminar.

--
 
Giuliano Armano
Associate Professor of Computer Engineering,
DIEE - Univ. of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy
Phone: +39-070-675.5758 FAX: 5782 Email: armano@diee.unica.it

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