Constructing Bayesian Network Graphs from Labeled Arguments

Remi Wieten; Floris Bex; Henry Prakken; Silja Renooij

doi:10.1007/978-3-030-29765-7_9

Constructing Bayesian Network Graphs from Labeled Arguments

Remi Wieten^*, Floris Bex, Henry Prakken, Silja Renooij

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Subtitle of host publication	15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings
Editors	Gabriele Kern-Isberner, Zoran Ognjanović
Publisher	Springer
Pages	99-110
Number of pages	12
Edition	1
ISBN (Electronic)	978-3-030-29765-7
ISBN (Print)	978-3-030-29764-0
DOIs	https://doi.org/10.1007/978-3-030-29765-7_9
Publication status	Published - 28 Aug 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11726
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Bayesian networks
Argumentation
Inference
Reasoning

Access to Document

10.1007/978-3-030-29765-7_9

978-3-030-29765-7_9Final published version, 355 KBLicence: Taverne

Cite this

Wieten, R., Bex, F., Prakken, H., & Renooij, S. (2019). Constructing Bayesian Network Graphs from Labeled Arguments. In G. Kern-Isberner, & Z. Ognjanović (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings (1 ed., pp. 99-110). (Lecture Notes in Computer Science; Vol. 11726). Springer. https://doi.org/10.1007/978-3-030-29765-7_9

Wieten, Remi ; Bex, Floris ; Prakken, Henry et al. / Constructing Bayesian Network Graphs from Labeled Arguments. Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings. editor / Gabriele Kern-Isberner ; Zoran Ognjanović. 1. ed. Springer, 2019. pp. 99-110 (Lecture Notes in Computer Science).

@inproceedings{e448b70adb754e0fbfca3c4209fd4cb6,

title = "Constructing Bayesian Network Graphs from Labeled Arguments",

abstract = "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 thispaper. 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.",

keywords = "Bayesian networks, Argumentation, Inference, Reasoning",

author = "Remi Wieten and Floris Bex and Henry Prakken and Silja Renooij",

year = "2019",

month = aug,

day = "28",

doi = "10.1007/978-3-030-29765-7_9",

language = "English",

isbn = "978-3-030-29764-0",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "99--110",

editor = "Gabriele Kern-Isberner and Zoran Ognjanovi{\'c}",

booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty",

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}

Wieten, R, Bex, F , Prakken, H & Renooij, S 2019, Constructing Bayesian Network Graphs from Labeled Arguments. in G Kern-Isberner & Z Ognjanović (eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings. 1 edn, Lecture Notes in Computer Science, vol. 11726, Springer, pp. 99-110. https://doi.org/10.1007/978-3-030-29765-7_9

Constructing Bayesian Network Graphs from Labeled Arguments. / Wieten, Remi; Bex, Floris ; Prakken, Henry et al.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings. ed. / Gabriele Kern-Isberner; Zoran Ognjanović. 1. ed. Springer, 2019. p. 99-110 (Lecture Notes in Computer Science; Vol. 11726).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Constructing Bayesian Network Graphs from Labeled Arguments

AU - Wieten, Remi

AU - Bex, Floris

AU - Prakken, Henry

AU - Renooij, Silja

PY - 2019/8/28

Y1 - 2019/8/28

N2 - 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 thispaper. 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.

AB - 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 thispaper. 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.

KW - Bayesian networks

KW - Argumentation

KW - Inference

KW - Reasoning

U2 - 10.1007/978-3-030-29765-7_9

DO - 10.1007/978-3-030-29765-7_9

M3 - Conference contribution

SN - 978-3-030-29764-0

T3 - Lecture Notes in Computer Science

SP - 99

EP - 110

BT - Symbolic and Quantitative Approaches to Reasoning with Uncertainty

A2 - Kern-Isberner, Gabriele

A2 - Ognjanović, Zoran

PB - Springer

ER -

Wieten R, Bex F , Prakken H , Renooij S. Constructing Bayesian Network Graphs from Labeled Arguments. In Kern-Isberner G, Ognjanović Z, editors, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, Proceedings. 1 ed. Springer. 2019. p. 99-110. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-29765-7_9

Constructing Bayesian Network Graphs from Labeled Arguments

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