During argumentation, people persuade their audience using a variety of strategies, e.g., hypothetical reasoning, reasoning by cases and ordinary premise-to-goal arguments. In this paper, we offer an operational definition of the conditions for pursuing these strategies, and incorporate into a Bayesian argument-generation system a mechanism for proposing applicable argumentation strategies, generating specific arguments based on these strategies, and selecting a final argument.

We describe a mechanism which generates rebuttals to a user's rejoinders in the context of arguments generated from Bayesian networks. This mechanism is implemented in an interactive argumentation system. Given an argument generated by the system and an interpretation of a user's rejoinder, the generation of the rebuttal takes into account the intended effect of the user's rejoinder, determined on a model of the user's beliefs, and its actual effect, determined on a model of the system's beliefs. We consider three main rebuttal strategies: refute the user's rejoinder, strengthen the argument goal, and dismiss the user's line of reasoning.

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The explicit consideration of an addressee's inferences during discourse planning affects the content and coherence of the generated discourse. The content of the discourse is affected because the consideration of an addressee's inferences may require the addition of information that addresses erroneous inferences or suggest the omission of easily inferred information. The coherence of the discourse is affected because the generated discourse should incorporate inferential relations that emerge from the consideration of an addressee's inferences. In this article, we describe a discourse planner that takes into consideration a user's inferences. In particular, we discuss its content planning and discourse structuring mechanisms. The content planning mechanism generates a set of rhetorical devices that achieves a given communicative goal. The discourse structuring mechanism takes into consideration schematic, inferential, and prescriptive relations between these rhetorical devices in order to organize them into a coherent sequence. Our discourse planner has been implemented in a system called WISHFUL that generates explanations about concepts in technical domains. We evaluated WISHFUL's performance to test the effect of considering a user's inferences during content planning and discourse organization and also to determine the suitability of the discourse produced by WISHFUL for different types of users. The results of our evaluation endorse the ideas embodied in the WISHFUL system.

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