Towards Personalized Explanation of Robotic Planning via User Feedback

11/01/2020
by   Kayla Boggess, et al.
0

Prior studies have found that providing explanations about robots' decisions and actions help to improve system transparency, increase human users' trust of robots, and enable effective human-robot collaboration. Different users have various preferences about what should be included in explanations. However, little research has been conducted for the generation of personalized explanations. In this paper, we present a system for generating personalized explanations of robotic planning via user feedback. We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. Our user study results show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering, and conflict detection and resolution.

READ FULL TEXT
research
03/16/2020

Towards Transparent Robotic Planning via Contrastive Explanations

Providing explanations of chosen robotic actions can help to increase th...
research
06/23/2021

Not all users are the same: Providing personalized explanations for sequential decision making problems

There is a growing interest in designing autonomous agents that can work...
research
11/19/2020

Iterative Planning with Plan-Space Explanations: A Tool and User Study

In a variety of application settings, the user preference for a planning...
research
03/16/2022

Explaining Preference-driven Schedules: the EXPRES Framework

Scheduling is the task of assigning a set of scarce resources distribute...
research
05/01/2020

Eliciting User Preferences for Personalized Explanations for Video Summaries

Video summaries or highlights are a compelling alternative for exploring...
research
05/06/2021

A Framework of Explanation Generation toward Reliable Autonomous Robots

To realize autonomous collaborative robots, it is important to increase ...
research
07/21/2023

Providing personalized Explanations: a Conversational Approach

The increasing applications of AI systems require personalized explanati...

Please sign up or login with your details

Forgot password? Click here to reset