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Hierarchical Strategy Learning for FLUX Agents: An applied technique - Couverture souple

Skubch, Hendrik

 
9783639431483: Hierarchical Strategy Learning for FLUX Agents: An applied technique
Couverture souple
ISBN 10 : 3639431480 ISBN 13 : 9783639431483
Editeur : AV Akademikerverlag, 2012

Synopsis

Revision with unchanged content. FLUX is a programming system for reasoning agents based on the fluent calculus. FLUX agents maintain an internal model of their environment, enabling them to represent partial knowledge. This ability gives rise to powerful reasoning techniques such as planning. Conventionally, the strategy such an agents executes in order to achieve a certain goal is static and needs to be defined in advance. Agents which are able to derive a strategy on their own, using experience and domain knowledge, can provide a way to tackle real world problems that change unexpectedly or for which no solution is known in advance. In this work, we focus on learning a strategy able to use the reasoning capabilities offered by systems such as FLUX. We present a technique to decompose a problem into smaller subproblems using heuristics derived from a given domain description. On this basis, subprograms are learnt in different hypothesis spaces, which correspond to different reasoning techniques. With the help of a grammar inducer, an inductive inference machine composes the found subprograms into the final strategy. The application of this work is still restricted to scenarios where a simulator is available.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Revision with unchanged content. FLUX is a programming system for reasoning agents based on the fluent calculus. FLUX agents maintain an internal model of their environment, enabling them to represent partial knowledge. This ability gives rise to powerful reasoning techniques such as planning. Conventionally, the strategy such an agents executes in order to achieve a certain goal is static and needs to be defined in advance. Agents which are able to derive a strategy on their own, using experience and domain knowledge, can provide a way to tackle real world problems that change unexpectedly or for which no solution is known in advance. In this work, we focus on learning a strategy able to use the reasoning capabilities offered by systems such as FLUX. We present a technique to decompose a problem into smaller subproblems using heuristics derived from a given domain description. On this basis, subprograms are learnt in different hypothesis spaces, which correspond to different reasoning techniques. With the help of a grammar inducer, an inductive inference machine composes the found subprograms into the final strategy. The application of this work is still restricted to scenarios where a simulator is available.

Biographie de l'auteur

Dipl. Inf., Studies of Computer Science at the TU Dresden. Currently PhD Student at the University of Kassel in the Distributed Systems Group.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
AV Akademikerverlag
Date d'édition
2012
Langue
anglais
ISBN 10 
3639431480
ISBN 13 
9783639431483
Reliure
Broché
Nombre de pages
100
Coordonnées du fabricant
non disponible
Personne responsable
non disponible