Plan-Similarity Based Heuristics for Goal Recognition

被引：1

作者：

Cohausz, Lea ^{[1
]}

Wilken, Nils ^{[2
]}

Stuckenschmidt, Heiner ^{[1
]}

机构：

[1] Univ Mannheim, Data & Web Sci Grp, Mannheim, Germany

[2] Univ Mannheim, Inst Enterprise Syst, Mannheim, Germany

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS) | 2022年

基金：

美国国家科学基金会;

关键词：

plan recognition; goal recognition; planning; smart-home;

D O I：

10.1109/PerComWorkshops53856.2022.9767517

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Plan and goal recognition are important tasks in constructing smart assistants and can be used to support activity recognition. However, all current approaches have the disadvantage of either being slow and inefficient regarding both online computation time and prior manual modelling effort tackled by human experts due to using domains which makes them infeasible for real-life settings; or requiring large amounts of annotated observation sequences which is not realistic as data annotation is expensive. This paper introduces a new approach requiring neither annotated observation data nor domains but only exemplary plans for each possible goal. It is based on plan-similarity based heuristics which makes it fast, yet is still able to achieve good results. This leads to new possibilities regarding the applicability in real-life settings and increases the usefulness for supporting activity recognition.

引用

页数：6

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