CRADLE

Author:

Mirsky Reuth1,Gal Ya’akov (Kobi)1,Shieber Stuart M.2

Affiliation:

1. Dept. of Software and Information Systems Engineering, Ben-Gurion University

2. Paulson School of Engineering and Applied Sciences, Harvard University

Abstract

In exploratory domains, agents’ behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users’ activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents’ interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CASPER: Cognitive Architecture for Social Perception and Engagement in Robots;International Journal of Social Robotics;2024-03-14

2. Towards Intelligent Companion Systems in General Aviation using Hierarchical Plan and Goal Recognition;International Conference on Human-Agent Interaction;2023-12-04

3. A unified constraint-based approach for plan and goal recognition from unreliable observations;Knowledge-Based Systems;2023-10

4. Leveraging Imperfect Explanations for Plan Recognition Problems;Explainable and Transparent AI and Multi-Agent Systems;2023

5. Towards Computational Modeling of Human Goal Recognition;Frontiers in Artificial Intelligence;2022-01-19

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