Korb, K. B., R. McConachy, et al. A Cognitive Model of Argumentation. Proc. 19th Cognitive Science Conf, Stanford, CA, August 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....different degrees of domain knowledge. This problem is novel in ITS, as it requires sophisticated natural language generation (NLG) techniques. While the NLG field has extensively studied the process of producing text tailored to a model of the user s inferential capabilities [e.g. Horacek 1997; Korb, McConachy et al. 1997; Young 1999] the application of NLG techniques in ITS are few and mainly focused on managing and structuring the tutorial dialogue [e.g. Moore 1996; Freedman 2000] rather than on tailoring the presentation of instructional material to a detailed student model. The rationale behind varying the ....

....concise text by taking into account the inferential capabilities of the user. Young 1999] generates effective plan descriptions tailored to the hearer s plan reasoning capabilities. Horacek 1997] is an example of models that take into account the hearer s logical inference capabilities. And [Korb, McConachy et al. 1997] proposes a system that relies on a model of user s probabilistic inferences to generate sufficiently persuasive arguments. In contrast, our generation system tailors the content and organisation of an example to a probabilistic model of the user logical inferences, which allows us to explicitly ....

Korb, K. B., R. McConachy, et al. A Cognitive Model of Argumentation. Proc. 19th Cognitive Science Conf, Stanford, CA, August 1997.

....need to be combined into a common representation. We have chosen BNs for this purpose because oftheir ability to represent normatively correct reasoning under uncertainty, and because simple alterations of the normal Bayesian Propagation rules allow us to model various human cognitive phenomena [Korb et al. 1997]. The content planning process produces an Argument Graph which starts from admissible premises and ends in the goal proposition. Admissible premises are nonnatively acceptable propositions that are believed by NAG and are either believed the user (sufficiently for the argument to work) or ....

....to the user model) Of course, an argument s effectiveness may be quite different from its normative strength. When anticipating an argument s effect upon a user, NAG takes into account three cognitive errors that humans frequently succumb to: belief bias, overconfidence and the base rate fallacy [Korb et al. 1997]. If the normafive strength or effectiveness of the Argument Graph is insufficient, another cycle of the Generation Analysis algorithm is executed, gathering further support for propositions which have a path to the goal or have a high activation level (Step 3 of the Generation Analysis ....

Korb, K. B., McConachy, R., and Zukerman, I. (1997).. A cognitive model of argumentation. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pages 400-405, Stanford, California.

....need to be combined into a common representation. We have chosen BNs for this purpose because of their ability to represent normatively correct reasoning under uncertainty, and because simple alterations of the normal Bayesian propagation rules allow us to model various human cognitive phenomena [Korb et al. 1997]. The content planning process produces an Argument Graph which starts from admissible premises and ends in the goal proposition. Admissible premises are normatively acceptable propositions that are believed by NAG and are either believed the user (sufficiently for the argument to work) or ....

....to the user model) Of course, an argument s effectiveness may be quite different from its normative strength. When anticipating an argument s effect upon a user, NAG takes into account three cognitive errors that humans frequently succumb to: belief bias, overconfidence and the base rate fallacy [Korb et al. 1997]. If the normative strength or effectiveness of the Argument Graph is insufficient, another cycle of the Generation Analysis algorithm is executed, gathering further support for propositions which have a path to the goal or have a high activation level (Step 3 of the Generation Analysis ....

Korb, K. B., McConachy, R., and Zukerman, I. (1997). A cognitive model of argumentation. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pages 400--405, Stanford, California.

....in both its normative and its persuasive aspects. We have chosen BNs as the format in which NAG assembles its arguments, since they support reasoning under uncertainty with multiple (possibly interactive) supporting factors, and can be readily modified to model human cognitive weaknesses (see (Korb et al. 1997)) An Argument Graph starts from admissible premises and ends in the goal proposition (the proposition to be argued for) Admissible premises are normatively acceptable propositions (represented in NAG s normative model) which are believed by the user according to NAG s user model or assented to ....

....from the target(s) 8 The propagation rules used to update beliefs when checking the Argument Graph in the user model are modified to model three cognitive weaknesses commonly observed in human subjects. The three weaknesses modeled are belief bias, overconfidence and the base rate fallacy. See (Korb et al. 1997) for details. For an example of semantic suppression, the prior probability of the intermediate conclusion N 4 , Phobos is building nuclear reactors] is set to 0.5, and the modified Bayesian belief update rules are applied. The resulting posterior probability in the intermediate conclusion is ....

Korb, K., McConachy, R., and Zukerman, I. (1997). A cognitive model of argumentation. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pages 400--405, Stanford, California.

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Korb, K. B., R. McConachy, et al. A Cognitive Model of Argumentation. Proc. 19th Cognitive Science Conf, Stanford, CA, August 1997.

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