Multi-agent Embodied Question Answering in Interactive Environments.

We investigate a new AI task—Multi-Agent Interactive Question Answering—where several agents explore the scene jointly in interactive environments to answer a question. To cooperate efficiently and answer accurately, agents must be well-organized to have balanced work division and share knowledge about the objects involved. We address this new problem in two stages: Multi-Agent 3D Reconstruction in Interactive Environments and Question Answering. Our proposed framework features multi-layer structural and semantic memories shared by all agents, as well as a question answering model built upon a 3D-CNN network to encode the scene memories. During the reconstruction, agents simultaneously explore and scan the scene with a clear division of work, organized by next viewpoints planning. We evaluate our framework on the IQuADv1 dataset and outperform the IQA baseline in a single-agent scenario. In multi-agent scenarios, our framework shows favorable speedups while remaining high accuracy.