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pro vyhledávání: '"Peter Kissmann"'
Publikováno v:
Electronic Proceedings in Theoretical Computer Science, Vol 99, Iss Proc. GRAPHITE 2012, Pp 66-82 (2012)
For the exploration of large state spaces, symbolic search using binary decision diagrams (BDDs) can save huge amounts of memory and computation time. State sets are represented and modified by accessing and manipulating their characteristic function
Externí odkaz:
https://doaj.org/article/ba59a66bc672440f871a3c09fbe50dbf
In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs efficient state space exploration in terms of time and memory. This is crucia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df037ed01115480c125278ea27568c5b
Autor:
Peter Kissmann, Jörg Hoffmann
Publikováno v:
Journal of Artificial Intelligence Research. 51:779-804
Symbolic search using binary decision diagrams (BDDs) can often save large amounts of memory due to its concise representation of state sets. A decisive factor for this method's success is the chosen variable ordering. Generally speaking, it is plaus
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling. 24:101-110
This work combines recent advances in AI planning under memory limitation, namely bitvector and symbolic search. Bitvector search assumes a bijective mapping between state and memory addresses, while symbolic search compactly represents state sets. T
Publikováno v:
Electronic Proceedings in Theoretical Computer Science, Vol 99, Iss Proc. GRAPHITE 2012, Pp 66-82 (2012)
GRAPHITE
GRAPHITE
For the exploration of large state spaces, symbolic search using binary decision diagrams (BDDs) can save huge amounts of memory and computation time. State sets are represented and modified by accessing and manipulating their characteristic function
Autor:
Peter Kissmann, Stefan Edelkamp
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 25:992-997
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning by exploiting a succinct representation and exploration of state sets. In this paper we enhance BDD-based planning by applying a combination of domain-
Autor:
Stefan Edelkamp, Peter Kissmann
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 25:18-23
Symbolic search using BDDs usually saves huge amounts of memory, while in some domains its savings are moderate at best. It is an open problem to determine if BDDs work well for a certain domain. Motivated by finding evidences for BDD growths for sta
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling. 21:242-249
In this paper optimal state space planning is parallelized by exploiting the processing power of a graphics card. The two exploration steps, namely selecting the actions to be applied and generating the successors, are performed on a graphics process
Autor:
Stefan Edelkamp, Peter Kissmann
Publikováno v:
KI - Künstliche Intelligenz. 25:49-52
This work is concerned with our general game playing agent Gamer. In contrast to many other players, we do not only use a Prolog-like mechanism to infer knowledge about the current state and the available moves but instantiate the games to reduce the
Autor:
Peter Kissmann, Stefan Edelkamp
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 1:63-70
In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers u