Abstract
A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations and partial descriptions extracted from sensor observations. Existing explanation generation systems draw on ideas that can be mapped to a multidimensional space of system characteristics, defined by distinctions, such as how they represent knowledge and if and how they reason with heuristic guidance. Instances in this multidimensional space corresponding to existing systems do not support all of the desired explanation generation capabilities for robots. We seek to address this limitation by thoroughly understanding the range of explanation generation capabilities and the interplay between the distinctions that characterize them. Towards this objective, this paper first specifies three fundamental distinctions that can be used to characterize many existing explanation generation systems. We explore and understand the effects of these distinctions by comparing the capabilities of two systems that differ substantially along these axes, using execution scenarios involving a robot waiter assisting in seating people and delivering orders in a restaurant. The second part of the paper uses this study to argue that the desired explanation generation capabilities corresponding to these three distinctions can mostly be achieved by exploiting the complementary strengths of the two systems that were explored. This is followed by a discussion of the capabilities related to other major distinctions to provide detailed recommendations for developing an explanation generation system for robots.
Highlights
Robots equipped with multiple sensors are increasingly used to interact and collaborate with humans in different applications
We investigate KRASP, a system that uses an elaborate description of domain knowledge and observations of system behaviour to generate explanations (Section 3.2)
We introduce the following three fundamental distinctions to characterize, in terms of components, capabilities and approaches, the multidimensional space populated by existing explanation generation systems: (1)
Summary
Robots (we use the term interchangeably with “agents” in this paper) equipped with multiple sensors are increasingly used to interact and collaborate with humans in different applications. These robots receive an incomplete and inaccurate description of the domain based on the information extracted from the sensor data they acquire. They receive useful common sense information that holds in all, but a few exceptional situations, but it is challenging to represent and reason with such information. The robot may be given default knowledge, such as “plates are usually in the kitchen, Robotics 2016, 5, 21; doi:10.3390/robotics5040021 www.mdpi.com/journal/robotics
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
