Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice

Highlights：

• We present an Explainable AI system based on logical reasoning that supports the monitoring of users’ behaviors and persuades them to follow healthy lifestyles.

• The ontology is exploited by a SPARQL-based reasoner for detecting undesired situations within users’ behaviors, i.e., verifying if user's dietary and activities actions are consistent with the monitoring rules defined by domain experts.

• The core part of the Natural Language Generation component relies on templates (a grammar) that encode the several parts (feedback, arguments and suggestion) of a persuasion message.

• Results compare the persuasive explanations with simple notifications of inconsistencies and show that the former are able to support users in improving their adherence to dietary rules.

摘要

•We present an Explainable AI system based on logical reasoning that supports the monitoring of users’ behaviors and persuades them to follow healthy lifestyles.•The ontology is exploited by a SPARQL-based reasoner for detecting undesired situations within users’ behaviors, i.e., verifying if user's dietary and activities actions are consistent with the monitoring rules defined by domain experts.•The core part of the Natural Language Generation component relies on templates (a grammar) that encode the several parts (feedback, arguments and suggestion) of a persuasion message.•Results compare the persuasive explanations with simple notifications of inconsistencies and show that the former are able to support users in improving their adherence to dietary rules.

论文评审过程：Received 30 September 2019, Revised 21 January 2020, Accepted 27 February 2020, Available online 5 March 2020, Version of Record 12 May 2020.