AHPACS

Abstraction Heuristics for Planning and Combinatorial Search

Coordinatore	ALBERT-LUDWIGS-UNIVERSITAET FREIBURG    more  Organization address address: FAHNENBERGPLATZ city: FREIBURG postcode: 79085 contact info Titolo: Prof. Nome: Bernhard Cognome: Nebel Email: send email Telefono: +49 761 2038221 Fax: +49 761 2038222
Nazionalità Coordinatore	Germany [DE]
Totale costo	240˙803 €
EC contributo	240˙803 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2009-IOF
Funding Scheme	MC-IOF
Anno di inizio	0
Periodo (anno-mese-giorno)	0000-00-00   -   0000-00-00

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ALBERT-LUDWIGS-UNIVERSITAET FREIBURG  Organization address address: FAHNENBERGPLATZ city: FREIBURG postcode: 79085 contact info Titolo: Prof. Nome: Bernhard Cognome: Nebel Email: send email Telefono: +49 761 2038221 Fax: +49 761 2038222	DE (FREIBURG)	coordinator	240˙803.30

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 Obiettivo del progetto (Objective)

'Automated planning is concerned with the automated generation of courses of action for autonomous agents, by reasoning over their capabilities and goals. It is one of the central areas of Artificial Intelligence and has numerous applications wherever virtual or embodied agents need to make intelligent decisions autonomously, including domains such as network security, manufacturing, space exploration, robot control, and the semantic web. Heuristic search is the core algorithmic idea underlying most state-of-the-art domain-independent planning systems, and systems based on abstraction heuristics are among the most popular and successful representatives of this approach, especially when there exists a requirement to find optimal plans, i.e., plans of minimal cost. We aim to advance the state of the art of optimal heuristic search with abstraction heuristics in three respects: Firstly, we want to advance the theory of abstraction heuristics in order to improve our theoretical understanding of their capabilities, their limitations, and their relationship to other commonly used approaches for deriving admissible heuristics. Secondly, we want to develop improved abstraction heuristics for the domain-independent setting, thus improving the state of the art for optimal planning systems. Thirdly, we want to transfer the new insights on abstraction heuristics to applications of optimal search outside of domain-independent planning, in order to advance the state of the art in these areas. Research areas of particular promise to search with abstraction heuristics include model checking, diagnosis of discrete-event systems, parsing for probabilistic context-free grammars, as well as multiple sequence alignment and genome rearrangement problems in bioinformatics.'