Resumen
Limited memory influence diagrams are graph-based models that describe decision problems with limited information, as in the case of teams and agents with imperfect recall. Solving a (limited memory) influence diagram is an NP-hard problem, often approached through local search. In this paper we investigate algorithms for k-neighborhood local search. We show that finding a k-neighbor that improves on the current solution is W[1]-hard and hence unlikely to be polynomial-time tractable. We then develop fast schema to perform approximate k-local search; experiments show that our methods improve on current local search algorithms both with respect to time and to accuracy.
Idioma original | Inglés |
---|---|
Páginas (desde-hasta) | 334-349 |
Número de páginas | 16 |
Publicación | Lecture Notes in Computer Science |
Volumen | 8754 |
DOI | |
Estado | Publicada - 2014 |
Publicado de forma externa | Sí |