Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context

Kridalukmana, R; Lu, H; Naderpour, M

Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context

Kridalukmana, R Lu, H

Naderpour, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Human-Machine Systems, 2022, 52, (6), pp. 1155-1165
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Kridalukmana, R
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875
dc.date.accessioned	2023-03-09T01:11:44Z
dc.date.available	2023-03-09T01:11:44Z
dc.date.issued	2022-12-01
dc.identifier.citation	IEEE Transactions on Human-Machine Systems, 2022, 52, (6), pp. 1155-1165
dc.identifier.issn	2168-2291
dc.identifier.issn	2168-2305
dc.identifier.uri	http://hdl.handle.net/10453/166745
dc.description.abstract	A critical challenge in human-autonomy teaming is for human players to comprehend their nonhuman teammates (agents). Transparency in agents' behaviors is the key for such comprehension, which may be obtained by embedding a self-explanation ability into the agent to explain its own behaviors. Previous studies have relied on searching for the executed functions and logics to generate explanations for behaviors of goal-following logic-based agents. With the increasing number of functions and logics, current methods, such as component and process-based methods, have become impractical. This article proposes a new method exploiting the agent's artificial situation awareness states for generating explanations that involves several techniques: A Bayesian network, fuzzy theory, and Hamming distance. Our new method is evaluated in a collaborative driving context, in which a significant number of accidents recently occurred around the globe due to the lack of understanding of the autopilot agents. Using an autonomous driving simulator called Carla, two typical scenarios in collaborative driving, namely, traffic light and overtaking situations, are used. The findings show that the new method potentially reduces the search space in generating explanations and exhibits better computational performance and a lower cognitive workload. This work is important to calibrate human trust and to enhance comprehension of the agent.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Human-Machine Systems
dc.relation.isbasedon	10.1109/THMS.2022.3202900
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-12-01T00:00:00+1000Z
dc.date.updated	2023-03-09T01:11:34Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	52
utslib.citation.issue	6

Abstract:

A critical challenge in human-autonomy teaming is for human players to comprehend their nonhuman teammates (agents). Transparency in agents' behaviors is the key for such comprehension, which may be obtained by embedding a self-explanation ability into the agent to explain its own behaviors. Previous studies have relied on searching for the executed functions and logics to generate explanations for behaviors of goal-following logic-based agents. With the increasing number of functions and logics, current methods, such as component and process-based methods, have become impractical. This article proposes a new method exploiting the agent's artificial situation awareness states for generating explanations that involves several techniques: A Bayesian network, fuzzy theory, and Hamming distance. Our new method is evaluated in a collaborative driving context, in which a significant number of accidents recently occurred around the globe due to the lack of understanding of the autopilot agents. Using an autonomous driving simulator called Carla, two typical scenarios in collaborative driving, namely, traffic light and overtaking situations, are used. The findings show that the new method potentially reduces the search space in generating explanations and exhibits better computational performance and a lower cognitive workload. This work is important to calibrate human trust and to enhance comprehension of the agent.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166745