Enriching Communication between Humans and AI Agents

In this talk, I will present new training and evaluation frameworks that enrich communication between humans and AI agents. My work focuses on improving two important capabilities of an agent: (1) the ability to learn from humans through language-based communication and (2) the ability to request and interpret information from humans to make better decisions. To improve the first capability, I propose a learning framework that allows agents to learn from descriptive human language. To enhance the second capability, I introduce new benchmarks that simulate and measure an agent’s ability to actively ask for and interpret information from humans to successfully complete tasks. On these benchmarks, I build agents that are capable of requesting rich, contextually useful information and show that they significantly outperform agents without such capability. I will conclude with a discussion on future directions to endow AI agents with more expressive communication capabilities and models of cognition.