BDE
Financiado
Beyond Distance Estimates A New Theory of Heuristics for State Space Search
"Many problems in computer science can be cast as state-space search, where the
objective is to find a path from an initial state to a goal state in a
directed graph called a ""state space"". State-space search is challenging due...
"Many problems in computer science can be cast as state-space search, where the
objective is to find a path from an initial state to a goal state in a
directed graph called a ""state space"". State-space search is challenging due
to the state explosion problem a.k.a. ""curse of dimensionality"": interesting
state spaces are often astronomically large, defying brute-force exploration.
State-space search has been a core research problem in Artificial Intelligence
since its early days and is alive as ever. Every year, a substantial fraction
of research published at the ICAPS and SoCS conferences is concerned with
state-space search, and the topic is very active at general AI conferences
such as IJCAI and AAAI.
Algorithms in the A* family, dating back to 1968, are still the go-to approach
for state-space search. A* is a graph search algorithm whose only
""intelligence"" stems from a so-called ""heuristic function"", which estimates
the distance from a state to the nearest goal state. The efficiency of A*
depends on the accuracy of this estimate, and decades of research have pushed
the envelope in devising increasingly accurate estimates.
In this project, we question the ""A* + distance estimator"" paradigm and
explore three new directions that go beyond the classical approach:
1. We propose a new paradigm of declarative heuristics, where heuristic
information is not represented as distance estimates, but as properties of
solutions amenable to introspection and general reasoning.
2. We suggest moving the burden of creativity away from the human expert by
casting heuristic design as a meta-optimization problem that can be solved
automatically.
3. We propose abandoning the idea of exploring sequential paths in state
spaces, instead transforming state-space search into combinatorial
optimization problems with no explicit sequencing aspect. We argue that the
""curse of sequentiality"" is as bad as the curse of dimensionality and must
be addressed head-on."
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Duración del proyecto: 61 meses
Fecha Inicio: 2018-12-18
Fecha Fin: 2024-01-31
Fecha Fin: 2024-01-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2024-01-31
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2018-COG:
ERC Consolidator Grant
Cerrada
hace 6 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
UNIVERSITAT BASEL
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
684.66K€ |
Participante