Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments
This work addresses the need for flexible argumentation frameworks in AI, particularly for multi-agent systems, though it appears incremental as an extension of existing epistemic approaches.
The paper introduces epistemic graphs as a generalization of probabilistic argumentation to represent arguments with degrees of belief, enabling modeling of attack, support, and other relations for more fine-grained and context-sensitive evaluation.
This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine--grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context--sensitive. It also allows for better modelling of imperfect agents, which can be important in multi--agent applications.