Szczegóły publikacji
Opis bibliograficzny
Creative expert system: comparison of proof searching strategies / Bartłomiej ŚNIEŻYŃSKI, Grzegorz LEGIEŃ, Dorota WILK-KOŁODZIEJCZYK, Stanisława Kluska-Nawarecka, Edward NAWARECKI, Krzysztof Jaśkowiec // W: Intelligent information and database systems : 9th Asian Conference, ACIIDS 2017 : Kanazawa, Japan, April 3–5, 2017 : proceedings, Pt. 1 / eds. Ngoc Thanh Nguyen, [et al.]. — Cham : Springer International Publishing, cop. 2017. — (Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; vol. 10191). — ISBN: 978-3-319-54471-7; e-ISBN: 978-3-319-54472-4. — S. 400–409. — Bibliogr. s. 408–409, Abstr. — Publikacja dostępna online od: 2017-02-26. — D. Wilk-Kołodziejczyk - dodatkowa afiliacja: Foundry Research Institute
Autorzy (6)
- AGHŚnieżyński Bartłomiej
- AGHLegień Grzegorz
- AGHWilk-Kołodziejczyk Dorota
- Kluska-Nawarecka Stanisława
- AGHNawarecki Edward
- Jaśkowiec Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 106716 |
|---|---|
| Data dodania do BaDAP | 2017-08-29 |
| DOI | 10.1007/978-3-319-54472-4_38 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 9th Asian Conference on Intelligent Information and Database Systems |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This paper presents comparison of time cost of three proof searching strategies in a creative expert system. Initially, model of the creative expert system and inference algorithm are proposed. The algorithm searches for a proof up to a given maximal depth, using one of the following strategies: finding all possible proofs, finding the first proof by depth-first and finding the first proof by breadth-first. Calculation time is measured in inference scenarios from a casting domain. Creativity of the expert system is achieved thanks to integration of inference and machine learning. The learning algorithm can be automatically executed during inference process, because its execution is formalized as a complex inference rule. Such a rule can be fired during inference process. During execution, training data is prepared from facts already stored in the knowledge base and new implications are learned from it. These implications can be used in the inference process. Therefore, it is possible to infer decisions in cases not covered by the knowledge base explicitly.