アドバイスを活用したロボットとの自然言語対話の改善
Improving Natural Language Interaction with Robots Using Advice
ブロックワールドタスクにおいて、人間からの高レベルなアドバイスを組み込むことでエージェントの予測を制約し性能を向上させる手法を提案し、モデル自己生成アドバイスの有効性も示した。
著者: Nikhil Mehta, Dan Goldwasser
分類: cs.CL, cs.AI, cs.RO, stat.ML
原文アブストラクト
Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this paper we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agent's prediction. We evaluate our approach on the blocks world task, and show that even simple advice can help lead to significant performance improvements. To help reduce the effort involved in supplying the advice, we also explore model self-generated advice which can still improve results.
