Lecture: Memory-Limited Model-Based Diagnosis (Extended Abstract)
Master data
Title: | Memory-Limited Model-Based Diagnosis (Extended Abstract) |
Description: | Model-based diagnosis is a principled and broadly applicable AI-based approach to tackle debugging problems in a wide range of areas including software, knowledge bases, circuits, cars, and robots. Whenever the sound and complete computation of fault explanations in a given preference order (e.g., cardinality or probability) is required, all existing diagnosis algorithms suffer from an exponential space complexity. This can prevent their application on memory-restricted devices and for memory-intensive problem cases. As a remedy, we propose RBF-HS, a diagnostic search based on Korf’s seminal RBFS algorithm which can enumerate an arbitrary fixed number of fault explanations in best-first order within linear space bounds, without sacrificing other desirable properties. Evaluations on real-world diagnosis cases show that RBF-HS, when used to compute minimum-cardinality fault explanations, in most cases saves substantial space while requiring only reasonably more or even less time than Reiter’s HS-Tree, one of the most influential diagnostic algorithms with the same properties. |
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Type: | Registered lecture |
Homepage: | - |
Event: | Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023) (Macao) |
Date: | 22.08.2023 |
lecture status: | stattgefunden (Präsenz) |
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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