A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

human-autonomy teaming, intelligent agent, human-agent interaction

Predicting immediate future actions taken by an intelligent agent is considered an essential problem in human-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly to improve human comprehension of the agent as their non-human counterpart. Moreover, the results of such predictions can shorten the human response time to gain control back from their non-human counterpart when it is required. An example case of HAT that can benefit from the action predictor is partially automated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop an approach to predict
the immediate future actions of an intelligent agent with partially automated driving as the experimental case. The proposed approach relies on a machine learning method called naive Bayes to develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. The autonomous driving simulation software called Carla is used for the simulation. The results show that the proposed approach is applicable to predict an intelligent agent’s three-second time-window for immediate future action.

Rinta Kridalukmana, Dania Eridani, Risma Septiana

http://dx.doi.org/10.21609/jiki.v17i1.1199

Faculty of Computer Science Universitas Indonesia

 2024-02-25

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

PDF

English

Text

Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)