Towards the generation of rebuttals in a Bayesian Argumentation System

Jitnah, Nathalie; Zukerman, Ingrid; McConachy, Richard; George, Sarah

doi:10.3115/1118253.1118260

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Despite the pervasiveness of BNs, the explanation of the reasoning performed by a BN has been considered only by a few researchers (Druzdzel 1996;McConachy et al 1998;Jitnah et al 2000). Druzdzel and McConachy et al studied different aspects of the presentation of BNs.…”

Section: Related Researchmentioning

confidence: 97%

Inferences, suppositions and explanatory extensions in argument interpretation

George

Zukerman

Niemann

2007

User Model User-Adap Inter

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We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user's argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation-a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.

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Section: Related Researchmentioning

confidence: 97%

Inferences, suppositions and explanatory extensions in argument interpretation

George

Zukerman

Niemann

2007

User Model User-Adap Inter

Self Cite

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“…-Address jumps in belief during the presentation of interpretations. This may be done by including in a presented interpretation factors that explain jumps in belief, e.g., propositions that have a significant effect on the consequent of an implication (Jitnah et al, 2000;Zukerman et al, 2004). Adaptive hypertext links could be used to provide this extra information (Bontcheva and Wilks, this issue).…”

Section: Evaluation With Usersmentioning

confidence: 98%

A Probabilistic Approach for Argument Interpretation

Zukerman

George

2005

User Model User-Adap Inter

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We describe a probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user's beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect's guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -a Bayesian network. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable.

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“…The automatic generation of explanations from BNs has been investigated since the early 1980s (Boerlage, 1983;Sember & Zukerman, 1989;Suermondt, 1992), followed by work conducted by authors Korb and Zukerman (Korb et al, 1997;Zukerman et al, 1998Zukerman et al, , 1999Jitnah et al, 2000), and more recently by other researchers (Vreeswijk, 2005;Keppens, 2011). However, automated explanation from BNs has thus far been selective and special purpose, with language tailored to specific variables and subnetworks.…”

Section: Explaining Bayesian Networkmentioning

confidence: 99%

BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning

Nicholson,

Korb,

Nyberg

et al. 2020

Preprint

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In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require significant upfront training, do not provide much guidance on the model building process, and do not support collaboratively building BNs. BARD (Bayesian ARgumentation via Delphi) is both a methodology and an expert system that utilises (1) BNs as the underlying structured representations for better argument analysis, (2) a multi-user web-based software platform and Delphi-style social processes to assist with collaboration, and (3) short, high-quality e-courses on demand, a highly structured process to guide BN construction, and a variety of helpful tools to assist in building and reasoning with BNs, including an automated explanation tool to assist effective report writing. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyse a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and use it to produce a written analytic report. Initial experimental results demonstrate that BARD aids in problem solving, reasoning and collaboration.

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Towards the generation of rebuttals in a Bayesian Argumentation System

Cited by 10 publications

References 2 publications

Inferences, suppositions and explanatory extensions in argument interpretation

Inferences, suppositions and explanatory extensions in argument interpretation

A Probabilistic Approach for Argument Interpretation

BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning

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