| Project/Area Number |
12680384
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kobe University |
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Principal Investigator |
INOUE Katsumi Kobe University, Engineering, Associate Professor, 工学部, 助教授 (10252321)
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| Co-Investigator(Kenkyū-buntansha) |
NABESHIMA Hidetomo Yamanashi University, Engineering, Assistant Professor, 工学部, 助手 (10334848)
HANEDA Hiromasa Kobe University, Engineering, Professor, 工学部, 教授 (10031113)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2001: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2000: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | planning / satisfiability (SAT) / reasoning about change / SAT planning / action language / planning graph / アクション言語 / SATソルバ / SATプランニング / プランニンググラフ / 充足可能性判定 |
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| Research Abstract |
SAT planning is a fast planning method which converts a planning problem into a satisfiability (SAT) problem and extracts a plan by solving the SAT problem using a fast SAT solver. In this research, we have developed an integrated action language processing system AMP to perform fast SAT planning on domain descriptions described in action languages : The contributions of the research can be summerized as follows.
1. We have developed several algorithms to convert domain descriptions described in action languages into SAT problems. In particular, we have considered an algorithm which transforms planning problems involving nondeterministic actions into SAT problems.
2. We have implemented several planning engines in AMP. Using planning graphs as data structures, we have developed forward/backward/mixed graph expansion algorithms to perform planning as fast as possible. We have also designed our system to have multiple SAT solvers for SAT planning. Depending on the feature of a problem, some SAT solvers perform better than others.
3. We have considered learning algorithms for speed-up planning. Given observations that describe fluent values after performing action sequences, learning algorithms induce causal relationships between actions and their effects.
4. AMP has been implemented in Java, and has an ability to draw planning graphs for interactive uses. Experimental results with big benchmark problems show that the performance of AMP is comparable to one of the fastest planner in the world. Unlike previous systems, however, AMP can interpret a description in an action language directly, and can answer queries for not only planning but model generation efficiently.
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