@inproceedings{kasenberg-etal-2019-generating,
title = "Generating justifications for norm-related agent decisions",
author = "Kasenberg, Daniel and
Roque, Antonio and
Thielstrom, Ravenna and
Chita-Tegmark, Meia and
Scheutz, Matthias",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8660",
doi = "10.18653/v1/W19-8660",
pages = "484--493",
abstract = "We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent{'}s rules, actions, and the extent to which the agent violated the rules) as well as {``}why{''} questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.",
}
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<abstract>We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent’s rules, actions, and the extent to which the agent violated the rules) as well as “why” questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.</abstract>
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%0 Conference Proceedings
%T Generating justifications for norm-related agent decisions
%A Kasenberg, Daniel
%A Roque, Antonio
%A Thielstrom, Ravenna
%A Chita-Tegmark, Meia
%A Scheutz, Matthias
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F kasenberg-etal-2019-generating
%X We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent’s rules, actions, and the extent to which the agent violated the rules) as well as “why” questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.
%R 10.18653/v1/W19-8660
%U https://aclanthology.org/W19-8660
%U https://doi.org/10.18653/v1/W19-8660
%P 484-493
Markdown (Informal)
[Generating justifications for norm-related agent decisions](https://aclanthology.org/W19-8660) (Kasenberg et al., INLG 2019)
ACL
- Daniel Kasenberg, Antonio Roque, Ravenna Thielstrom, Meia Chita-Tegmark, and Matthias Scheutz. 2019. Generating justifications for norm-related agent decisions. In Proceedings of the 12th International Conference on Natural Language Generation, pages 484–493, Tokyo, Japan. Association for Computational Linguistics.