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 probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user's beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect's guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -a Bayesian network. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable.

Abstract.We describe an argument-interpretation mechanism based on the Minimum Message Length Principle [1], and investigate the incorporation of a model of the user's beliefs into this mechanism. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -a Bayesian network. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. The results of our evaluation are encouraging, with the system generally producing plausible interpretations of users' arguments.

We describe a mechanism for the interpretation of arguments, which can cope with noisy conditions in terms of wording, beliefs and argument structure. This is achieved through the application of the Minimum Message Length Principle to evaluate candidate interpretations. Our system receives as input a quasi-Natural Language argument, where propositions are presented in English, and generates an interpretation of the argument in the form of a Bayesian network (BN). Performance was evaluated by distorting the system's arguments (generated from a BN) and feeding them to the system for interpretation. In 75% of the cases, the interpretations produced by the system matched precisely or almost-precisely the representation of the original arguments.

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