A Bayesian Approach to Automating Argumentation

I l I I | l / A Bayesian Approach to Automating Argumentation Richard McConachy, Kevin B. Korb & Ingrid Zukerman Department of Computer Science, Monash University Clayton, Victoria 3168, AUSTRALIA {ricky, korb, ingrid}~cs .monash. edu. au Abstract Our argumentation system NAG uses Bayesian networks in a user model and in a normative model to assemble and assess nice arguments, that is arguments which balance persuasive- ness with normative correctness. Attentional focus is simulated in both models to select relevant subnetworks for Bayesian propaga- tion. Bayesian propagation in the user model is modified to represent some human cognitive weaknesses. The subnetworks are expanded in an iterative abductive process until argumen- tative goals are achieved in both models, when the argument is presented to the user. 1 Introduction "The St. Kilda Football Club will probably win the grand final in 1997. They are likely to do so be- cause they have won at least as many games as any other side during the regular season, they have scored more points than any other team this year so far, and they have few unavailable players." How strongly should St. Kilda's good form in the later part of the 1997 season be taken as a premiership indicator? Is it better to lose narrowly to a strong side or narrowly defeat a weak side? Most argu- ments, like the St. Kilda example above, must be formed using information that is both incomplete and uncertain. This paper focuses upon the Bayesian underpin- nings of our argument generation-analysis system, NAG (Nice Argument Generator). The goal of our system is to generate nice arguments. A nice ar- gument employs normatively strong inferences from accepted premises while also being persuasive for the target audience. The main modules of NAG are shown in Figure 1. The Generator (Section 3) uses semantic activation to quickly form the initial Argument Graph for an argument, or to quickly extend an already existing t ,Re-nin l LAroment Generator I Agents Analyzer . Argument Goal Analysis .~"~Proposition aalysis~// Goal _ I Argument Proposition r Strategist Argument It User Argument/Inquiry USER Figure 1: System Architecture Argument Graph. An Argument Graph is a net- work structure with nodes that represent proposi- tions, and connecting links that represent the infer- ences that connect these propositions. An Argument Graph is fleshed out by consulting several sources of information called Reasoning Agents and incorporat- ing the relevant inferences and propositions returned by these sources into the Argument Graph. This Ar- gument Graph is passed to the Strategist. The Strategist decides what NAG should do next: call the Generator to continue the argument building process; call the Analyzer (Section 4) to estimate how nice the current Argument Graph is; or present an argument based on the current Argument Graph to the user for inspection and response. The Strategist will pass the current Argument Graph to the Analyzer at least once before the argu- ment is presented to the user, and often more than once. To estimate the persuasive power of …

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