Informing dialogue strategy through argumentation-derived evidence

In many settings, agents (whether human or artificial) engage in problem solving activities, which require them to share resources, act on each others’ behalf, communicate and coordinate individual acts, and so on. If autonomous agents are to effectively interact (or support interaction among humans) in situations such as deciding whom and how to approach the provision of a resource or the performance of an action, there are a number of important questions to address. Who do I choose to delegate a task to? What do I need to say to convince him/her to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? Research in argumentation strategies has received significant attention in recent years, and a number of approaches has been proposed to enable agents to reason about arguments to put forward in order to persuade another. However, current approaches do not adequately address situations where agents may be operating under social constraints (e.g., policies) that regulate behaviour in a society. Furthermore, existing approaches are largely theoretical, lacking rigorous empirical evaluation. In this thesis, we propose a novel combination of techniques that takes into consideration the policies that others may be operating with. First, we present an approach where evidence derived from argumentation-based dialogue is utilised to learn the policies that others may be operating under. We show that this approach enables agents to build more accurate and stable models of others more rapidly. In addition, we demonstrate how background knowledge can be utilised to further refine such models. Secondly, we present an agent decision-making mechanism where models of other agents are used to guide future argumentation strategy. This approach takes into account the learned policy constraints of others, the cost of revealing information, and anticipated resource availability in deciding whom to approach for a resource or for an action to be done. Furthermore, we present a number of strategies that an agent can employ during such interactions. We empirically evaluate our approach within a simulated multi-agent framework, and demonstrate that through the use of such informed strategies agents can both significantly improve the cumulative utility of dialogical outcomes, and reduce communication overhead.