Scenario Generation-Based Training in Simulation: Pilot Study

Ahmed, M; Saleh, K; Abobakr, A; Nahavandi, S

Scenario Generation-Based Training in Simulation: Pilot Study

Ahmed, M Saleh, K Abobakr, A Nahavandi, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1239-1244
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Ahmed, M
dc.contributor.author	Saleh, K
dc.contributor.author	Abobakr, A
dc.contributor.author	Nahavandi, S
dc.date	2019-10-06
dc.date.accessioned	2020-06-02T23:04:45Z
dc.date.available	2020-06-02T23:04:45Z
dc.date.issued	2019
dc.identifier.citation	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1239-1244
dc.identifier.isbn	978-1-7281-4569-3
dc.identifier.issn	2577-1655
dc.identifier.uri	http://hdl.handle.net/10453/141076
dc.description.abstract	Scenario generation-based training in simulated environments has recently gained importance, since real life training environments can be costly, risky, time consuming, and requires substantial resources. In this paper, we propose a narrative-based scenario generation methodology for training, measuring, and analysing the trainee performance level in simulation. We use a driving simulator as an application domain for the training process. Furthermore, we utilised an autonomous Artificial Intelligence (AI) agent for the training experiments. Using the AI agent for practising the generated scenarios offers benefits in many aspects. Firstly, the AI agent simulates the behaviour of a human (trainee). Secondly, it is easy to collect the performance data with the AI agent, as compared with recruiting many trainees for data collection, particularly for the early stage of validation and verification of the proposed scenario generation methodology. We formulate an experimental study to measure and assess the AI agent's behaviours in scenarios with different levels of complexity. The collected performance metrics are used to evaluate the efficiency of the designed scenarios and its capabilities in capturing the variations in performance levels. The empirical results depict that the AI agent's behaviours pertain to the level of scenario complexity including varying weather conditions.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.ispartof	IEEE International Conference on Systems, Man and Cybernetics
dc.relation.isbasedon	10.1109/SMC.2019.8913985
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Scenario Generation-Based Training in Simulation: Pilot Study
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Bari, Italy
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-02T23:04:39Z
pubs.finish-date	2019-10-09
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-06
pubs.volume	2019-October
utslib.start-page	1239
dc.location	Piscataway, USA

Abstract:

Scenario generation-based training in simulated environments has recently gained importance, since real life training environments can be costly, risky, time consuming, and requires substantial resources. In this paper, we propose a narrative-based scenario generation methodology for training, measuring, and analysing the trainee performance level in simulation. We use a driving simulator as an application domain for the training process. Furthermore, we utilised an autonomous Artificial Intelligence (AI) agent for the training experiments. Using the AI agent for practising the generated scenarios offers benefits in many aspects. Firstly, the AI agent simulates the behaviour of a human (trainee). Secondly, it is easy to collect the performance data with the AI agent, as compared with recruiting many trainees for data collection, particularly for the early stage of validation and verification of the proposed scenario generation methodology. We formulate an experimental study to measure and assess the AI agent's behaviours in scenarios with different levels of complexity. The collected performance metrics are used to evaluate the efficiency of the designed scenarios and its capabilities in capturing the variations in performance levels. The empirical results depict that the AI agent's behaviours pertain to the level of scenario complexity including varying weather conditions.

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http://hdl.handle.net/10453/141076