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Models of argument for deliberative dialogue in complex domains

In dynamic multiagent systems, self-motivated agents pursuing individual goals may interfere with each other’s plans. Agents must, therefore, coordinate their plans to resolve dependencies among them. This drives the need for agents to engage in dialogue to decide what to do in collaboration. Agreeing what to do is a complex activity, however, when agents come to an encounter with different objectives and norm expectations (i.e. societal norms that constrain acceptable behaviour). Argumentation-based models of dialogue support agents in deciding what to do analysing pros/cons for decisions, and enable conflict resolution by revealing structured background information that facilitates the identification of acceptable solutions. Existing models of deliberative dialogue, however, commonly assume that agents have a shared goal, and to date their effectiveness has been shown only through the use of extended examples. In this research, we propose a novel model of argumentation schemes to be integrated in a dialogue for the identification of plan, goal and norm conflicts when agents have individual but interdependent objectives. We empirically evaluate our model within a dynamic system to establish how the information shared with argumentation schemes influence dialogue outcomes. We show that by employing our model of arguments in dialogue, agents achieve more successful agreements. The resolution of conflicts and identification of more feasible interdependent plans is achieved through the sharing of focussed information driven by argumentation schemes. Agents may also consider more important conflicts, or conflicts that cause higher loss of utility if unresolved. We explore the use of strategies for agents to select arguments that are more likely to solve important conflicts. We show through an empirical evaluation that the most effective strategy combines the drive for agents to search for additional conflicts and the need to solve the most important ones already identified.