A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs

Xiaowei Liu, Kevin McAreavey, Weiru Liu

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution of appliance tasks, pay for energy according to dynamic energy tariffs, have access to high-capacity battery storage, and are able to sell energy to the grid. The concurrent scheduling of devices makes this a multi-effector planning problem, while the dynamic tariffs yield costs that are non-stationary (alternatively, costs that are stationary but depend on exogenous events). These characteristics are such that the planning problems are generally not supported by existing PDDL-based planners, so we instead design a custom domain-dependent planner that scales to reasonable appliance numbers and time horizons. We conduct a controlled user study with 128 participants using an online crowd-sourcing platform based on two user stories. Our results indicate that users provided with contrastive questions and explanations have higher levels of satisfaction, tend to gain improved understanding, and rate the helpfulness more favourably with the recommended AI schedule compared to those without access to these features.
Original languageEnglish
Title of host publication2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages413-419
Number of pages7
ISBN (Electronic)9798331527235
ISBN (Print)9798331527242
DOIs
Publication statusPublished - 28 Jan 2025
EventICTAI 2024 : 36th International Conference on Tools with Artificial Intelligence - Herndon, United States
Duration: 28 Oct 202430 Oct 2024
Conference number: 2024
https://ictai.computer.org/2024/

Publication series

NameInternational Conference on Tools with Artificial Intelligence
PublisherIEEE
ISSN (Print)1082-3409
ISSN (Electronic)2375-0197

Conference

ConferenceICTAI 2024 : 36th International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI
Country/TerritoryUnited States
CityHerndon
Period28/10/2430/10/24
Internet address

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • explainable AI planning
  • user study
  • contrastive explanations
  • temporal planning
  • multi-effector
  • non-stationary

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