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Abstraction Heuristics for Planning and Combinatorial Search

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 a... ver más

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ALU-FR

241K€

Presupuesto del proyecto: 241K€

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ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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ALU-FR

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Líder del proyecto

ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 241K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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