Knowledge-centered conversational agents with a drive to learn

We create an adaptive conversational agent that assesses the quality of its knowledge and is driven to become more knowledgeable. Unlike agents with predefined tasks, ours can leverage people as diverse sources to meet its knowledge needs. We test the agent in social contexts, where personal and subjective information can be obtained through dialogue. We provide the agent both with generic methods for assessing its knowledge quality (e.g. correctness, completeness, redundancy, interconnectedness, and diversity), as well as with generic capabilities to improve its knowledge by leveraging external sources. We demonstrate that the agent can learn effective policies to acquire the knowledge needed by assessing the efficiency of these capabilities during interaction. Our framework enables on-the-fly learning, offering a dynamic and adaptive approach to shaping conversational interactions.