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<Paper uid="W00-1408">
  <Title>Using Argumentation Strategies in Automated Argument Generation</Title>
  <Section position="4" start_page="55" end_page="56" type="intro">
    <SectionTitle>
2 Related Research formalism (BNs were chosen because of their abil-
</SectionTitle>
    <Paragraph position="0"> ity to represent normatively correct reasoning un-A general introduction to hypothetical reasoning, in- der uncertainty). An argument is represented as an cluding a discussion of counterfactual reasoning and Argument Graph, which is a network of nodes that modality, may be found in (Rescher, 1964). The represent propositions, and links that represent the use of suppositions in hypothetical reasoning to cre- inferences connecting these propositions. This Arate reductio ad absurdum arguments is described in gument Graph is obtained from the structural in(Freeman, 1991), and their use in the analysis of tersection of relevant portions of the normative and such arguments is discussed in (Fisher, 1988). Fis- user BNs. By considering the Argument Graph relcher also illustrates how suppositions can lead to ar- ative to both models we are able to assess both its guments that explain observed outcomes, a weaker normative correctness and its persuasiveness.</Paragraph>
    <Paragraph position="1"> version of inference to the best explanation. Condi- NAG receives as input a goal proposition to be tional argumentation, a weaker form of reasoning by argued for, an initial argument context, and a tarcases, where not all the cases must be examined and get range for the belief to be achieved in the goal the beginning of each case does not have to be proven (as a result of the argument) in the user model BN within the argument itself, is described in Freeman's and the normative model BN. Initially, the context is work. These works provide theoretical insights into composed of the goal proposition and salient propothe field of dialectics. However, they do not present sitions and concepts mentioned in the preceding disimplementable computational mechanisms, cussion. During argument generation, the context is In the area of discourse planning, few systems expanded to include the current Argument Graph.</Paragraph>
    <Paragraph position="2"> deal with the selection of argumentation strategies. Figure 2 shows the main modules of NAG (the Cerbah (1992) considers three discourse strategies: modules in double boxes contain the new argulnen-CausaIChain, which is a special case of our premise tation strategy mechanisms). After receiving a goal to goal strategy'; Parallel, which assigns a paral- proposition, the Strategist activates a sequence of lel structure to part of the text.; and Concessive. focusing-generation-analysis cycles as follows. First: These strategies reflect specific patterns of argumen- the Attentional Mechanism is invoked to focus on ration which may be incorporated in our higher level parts of the normative and user BNs that are likely strategies. Elhadad (1995) considers the use of at- to be useful in the argument. This is performed by gumentative features at several stages of the dis- spreading activation from the initial context. This course planning process, but none of his stages deals process generates an initial Argument Graph, and in with high-level argumentation.st.mtegies...Reed and . . tater~ cycles extends-the existing ArgumentGraph.</Paragraph>
    <Paragraph position="3"> Long (1997) use ordering heuristics to model the el- The Strategist then calls the Generator to continue fect of presentation order on argument persnasive- the argument building process by finding additional ness, and Mareu (1996) considers the effect of vari- information to incorporate in the Argument Graph ous stylistic factors, including ordering and lexical (Zukerman et al., 1998). The extended Argument 2The generalization of this strategy to N propositions re- Graph is returned to the Strategist, which invokes quires the presentation of 2 \&amp;quot; cases: in the current implemen- the Analyzer to deternfine the beliefs in the uodes tation, only individual proposit.ions are considered, in the Argnnlent Graph under a variety of condi- null Bayesian propagation scheme on the normative and user BNs, limiting the updates to the subnetworks represented in the Argument Graph. For the purposes of Bayesian updating, propositions which are provided in the preamble are treated as &amp;quot;observations&amp;quot;; that is, their degrees of belief are used as sources during Bayesian propagation. Based c,n the beliefs resulting from the Bayesian propagation, the Strategist determines which argumentation strategies are worth pursuing (Sections 4.2 and 4.3). If no strategy yields a nice enough argument, i.e., the belief in the goal is outside the target range in one or both models, the context is expanded, and another generation-analysis cycle is performed: the Strategist re-activates the focusing mechanism, followed by the re-activation of the Generator and then the Analyzer. This process iterates until a successful Argument Graph is built, or NAG is unable to continue, e.g., because it failed to find further evidence.</Paragraph>
    <Paragraph position="4"> If one or more strategies yield a nice enough argument, the Strategist selects one of the more concise arguments (Section 4.4). The corresponding Argument Graph and an ordering of the nodes to be presented are then passed to the Presenter, which removes easily inferred propositions from the argument. After each removal, the Presenter activates the Analyzer to check whether the argument remains nice enough, and the Attentional Mechanism to determine whether the argument can still be followed by the user. After the Presenter determines that no more propositions can be removed from the argument, it extracts Bayesian reasoning patterns from the final Argument Graph and passes them to the interface, which renders the argument in English (Zukerman et al., 1999).</Paragraph>
    <Paragraph position="5"> This procedure is implemented by the following algorithm, which is executed by the Strategist) aA previous version of this procedure which generates only premise-to-goal arguments is described in (Zukerman et al., 1998). In this paper, we focus on Steps 4 and 5, which have been modified to support the consideration of different argumentation strategies during the content planning process.</Paragraph>
    <Section position="1" start_page="56" end_page="56" type="sub_section">
      <SectionTitle>
Generation-Analysis Algorithm
</SectionTitle>
      <Paragraph position="0"> 1. Perform spreading activation starting from the items in the current context.</Paragraph>
      <Paragraph position="1"> 2. Identify new subgoals in the current Argument Graph.</Paragraph>
      <Paragraph position="2"> 3. Pass the subgoals identified in Step 2 to the Generator, which adds to the current Argument Graph new information related to these subgoals. null 4. Pass the Argument Graph generated in Step 3 to the Analyzer for evaluation under different conditions.</Paragraph>
      <Paragraph position="3"> 5. If (based on the Analyzer's report) some of the argumentation strategies seem promising then (a) Inspect specific arguments based on these strategies, and (b) Pass to the Presenter the portion of the Argument Graph corresponding to a concise argument which achieves the intended belief in the goal.</Paragraph>
      <Paragraph position="4"> 6. Otherwise, add to the current context new nodes that were connected to the goal or became salient during this cycle, and go to Step 1.</Paragraph>
    </Section>
  </Section>
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