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<Paper uid="W00-1406">
  <Title>Towards the Generations.of Rebul;tals in a Bayesian Argumentation System</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> During argumentation, conversational partners often use expressions of doubt, such as &amp;quot;But the victim was stabbed&amp;quot;, and requests for the consideration of additional facts they consider relevant, such as &amp;quot;What about the fingerprints found on the gun?&amp;quot;.</Paragraph>
    <Paragraph position="1"> In this paper, we describe a mechanism which generates rebuttals to such rejoinders in the context of arguments generated from Bayesian networks (BNs) (Pearl, 1988). This mechanism is implemented in a system called BIAS (Bayesian Interactive Argumentation System). Given an argument produced by BIAS and a follow-up rejoinder posed by a user, our mechanisln generates a rebuttal on tim basis of a line of reasoning identified by BIAS from the user's rejoinder. These capabilities constitute a significant step towards allowing a user to interact freely with an argumentation system and to improve the explanation capability of Bayesian systems.</Paragraph>
    <Paragraph position="2"> Normal arguments are unconstrained in the sense that they can use whatever means are available to * justify a goal proposition:i,:&amp;quot;In'~conterast, rebuttals are constrained, since they must address the point through which the conversational partner attempted to undermine or question a previous argument. To illustrate the operation of BIAS and its rebuttal capability, consider the exchange in Figure 1, which consists of a preamble that contains background information~ followed by an argument generated by BIAS, a user's rejoinder and BIAS' rebuttal. 1 The domain of implementation is a murder investigation where the question under consideration (the goal proposition) is &amp;quot;Did Mr Green murder Mr Body?&amp;quot;, and both the user and the system have access to evidence. After the presentation of the argument where BIAS contends Mr Green's possible innocence, 2 the user presents a rejoinder which requests that BIAS consider a fact that was omitted from the argument: The \]found gun is available only to Mr Green. BIAS infers from this rejoinder that the user is adding support to Mr Green having the means to kill Mr Body, and hence to Mr Green's guilt, through the following line of reasoning, which is determined as described in (Zukerman et al., 2000): The gun being available only to Mr Green ~ The gun was fired by Mr Green Mr Green had the means to kill Mr Body -+ Mr Green killed Mr Body. BIAS finds that it does not share the user's belief in the rejoinder proposition, and that in addition, the effect of this proposition on the goal is rather weak. This prompts the generation of a rebuttal of the form Deny-Dismiss-Follow, whereby the rejoinder proposition is denied, its effect on the goal proposition is dismissed, and its implications are followed hypothetically until they break down due to the marginal effect of the rejoinder on Mr Green's guilt.</Paragraph>
    <Paragraph position="3"> In the next section, we present our knowledge representation formalism, followed by an outline of our procedure for determining a user's line of reasoning.</Paragraph>
    <Paragraph position="4"> In Section 4, we describe our algorithm for rebuttal generation and discuss our results. We then review related work and present concluding remarks.</Paragraph>
  </Section>
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