The 'why did you do that?' button:Answering why-questions for end users of robotic systems

Autor: Koeman, Vincent J., Dennis, Louise A., Webster, Matt, Fisher, Michael, Hindriks, Koen, Bordini, Rafael H., Lespérance, Yves
Přispěvatelé: Dennis, Louise A., Bordini, Rafael H., Lespérance, Yves, Dennis, L, Bordini, R, Lespérance, Y, Artificial intelligence, Network Institute, Artificial Intelligence (section level), Social AI
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Koeman, V J, Dennis, L A, Webster, M, Fisher, M & Hindriks, K 2020, The “why did you do that?” button : Answering why-questions for end users of robotic systems . in L A Dennis, R H Bordini & Y Lespérance (eds), Engineering Multi-Agent System : 7th International Workshop, EMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12058 LNAI, Springer, pp. 152-172, 7th International Workshop on Engineering Multi-Agent Systems, EMAS 2019, Montreal, Canada, 13/05/19 . https://doi.org/10.1007/978-3-030-51417-4_8
Engineering Multi-Agent Systems. EMAS 2019
Engineering Multi-Agent System: 7th International Workshop, EMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers, 152-172
STARTPAGE=152;ENDPAGE=172;TITLE=Engineering Multi-Agent System
Engineering Multi-Agent Systems ISBN: 9783030514167
EMAS@AAMAS
Popis: The issue of explainability for autonomous systems is becoming increasingly prominent. Several researchers and organisations have advocated the provision of a “Why did you do that?” button which allows a user to interrogate a robot about its choices and actions. We take previous work on debugging cognitive agent programs and apply it to the question of supplying explanations to end users in the form of answers to why-questions. These previous approaches are based on the generation of a trace of events in the execution of the program and then answering why-questions using the trace. We implemented this framework in the agent infrastructure layer and, in particular, the Gwendolen programming language it supports – extending it in the process to handle the generation of applicable plans and multiple intentions. In order to make the answers to why-questions comprehensible to end users we advocate a two step process in which first a representation of an explanation is created and this is subsequently converted into natural language in a way which abstracts away from some events in the trace and employs application specific predicate dictionaries in order to translate the first-order logic presentation of concepts within the cognitive agent program in natural language. A prototype implementation of these ideas is provided.
Databáze: OpenAIRE
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