Constructing Bayesian Network Graphs from Labeled Arguments
| Autor: | Wieten, Remi, Bex, Floris, Prakken, Henry, Renooij, Silja, Kern-Isberner, Gabriele, Ognjanović, Zoran, Intelligent Systems, Decision Support Systems, Sub Intelligent Systems, Sub Decision Support Systems |
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| Přispěvatelé: | Intelligent Systems, Decision Support Systems, Sub Intelligent Systems, Sub Decision Support Systems, TILT |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Symbolic and quantitative approaches to reasoning with uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, proceedings, 99-110 STARTPAGE=99;ENDPAGE=110;TITLE=Symbolic and quantitative approaches to reasoning with uncertainty Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 11726(1), 99. Springer Cham Lecture Notes in Computer Science ISBN: 9783030297640 ECSQARU |
| ISSN: | 0302-9743 |
| Popis: | Bayesian networks (BNs) are powerful tools that are well-suited for reasoning about the uncertain consequences that can be inferred from evidence. Domain experts, however, typically do not have the expertise to construct BNs and instead resort to using other tools such as argument diagrams and mind maps. Recently, a structured approach was proposed to construct a BN graph from arguments annotated with causality information. As argumentative inferences may not be causal, we generalize this approach to include other types of inferences in this paper. Moreover, we prove a number of formal properties of the generalized approach and identify assumptions under which the construction of an initial BN graph can be fully automated. |
| Databáze: | OpenAIRE |
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