知識グラフから未見の問題に対応する機能的なタスクツリーを生成する手法
Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems
知識グラフを基に、未知の材料や状態の組み合わせでも柔軟なタスク計画（タスクツリー）を生成する手法を提案し、レシピデータで高い精度を確認した。
著者: Md. Sadman Sakib, David Paulius, Yu Sun
分類: cs.RO, cs.AI
原文アブストラクト
A major component for developing intelligent and autonomous robots is a suitable knowledge representation, from which a robot can acquire knowledge about its actions or world. However, unlike humans, robots cannot creatively adapt to novel scenarios, as their knowledge and environment are rigidly defined. To address the problem of producing novel and flexible task plans called task trees, we explore how we can derive plans with concepts not originally in the robot's knowledge base. Existing knowledge in the form of a knowledge graph is used as a base of reference to create task trees that are modified with new object or state combinations. To demonstrate the flexibility of our method, we randomly selected recipes from the Recipe1M+ dataset and generated their task trees. The task trees were then thoroughly checked with a visualization tool that portrays how each ingredient changes with each action to produce the desired meal. Our results indicate that the proposed method can produce task plans with high accuracy even for never-before-seen ingredient combinations.
