Interpretable goal recognition for path planning with ART networks

Publication Type

Conference Proceeding Article

Publication Date

7-2021

Abstract

Goal recognition for path planning is an important task of intention identification and situation awareness, requiring an observer to predict the goal of an evader given observations of its movements. While existing models based on planning or Markov Decision Process (MDP) show superior performance over traditional library based methods, they require much effort in model design and can hardly provide legible decision rules for their users. To make the system more user-friendly while preserving accuracy of goal inference, this paper proposes a novel self-organizing neural network based inference model, which learns compact rule sets through generalizing the streaming observations of an evader. More critically, the system manifests a high level of interpretability with the linguistic if-then rule base, making it easily comprehensible for human decision makers. We conducted extensive experiments on a large-scale real-world road network. Results show that the proposed model produces accuracy comparable to those of two state-of-the-art methods while uniquely providing legible inference rules and strong robustness against multiple goals with missing data.

Keywords

Roads, Neural networks, Subspace constraints, Observers, Markov processes, Linguistics, Path planning

Discipline

OS and Networks | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN 2021), Shenzhen, China, July 18-22

ISBN

9781665439008

Identifier

10.1109/IJCNN52387.2021.9534409

Publisher

IEEE

City or Country

Shenzhen, China

Additional URL

https://doi.org/10.1109/IJCNN52387.2021.9534409

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