Abstract
A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations, and partial descriptions of domain objects and events extracted from sensor observations. Existing explanation generation systems are based on ideas drawn from two broad classes of systems, and do not support all the desired explanation generation capabilities for robots. The objective of this paper is to first compare the explanation generation capabilities of a state of the art system from each of these two classes, using execution scenarios of a robot waiter assisting in a
restaurant. Specifically, we investigate KRASP, a system based on the declarative language Answer Set Prolog, which uses an elaborate system description and observations of system behavior to explain unexpected observations and partial descriptions. We also explore UMBRA, an architecture that provides explanations using a weaker system description, a heuristic representation of past experience, and other heuristics for selectively and incrementally searching through relevant ground literals. Based on this study, this paper identifies some key criteria, and provides some recommendations, for developing an explanation generation system for robots that exploits the complementary strengths of the two classes
of explanation generation systems.
restaurant. Specifically, we investigate KRASP, a system based on the declarative language Answer Set Prolog, which uses an elaborate system description and observations of system behavior to explain unexpected observations and partial descriptions. We also explore UMBRA, an architecture that provides explanations using a weaker system description, a heuristic representation of past experience, and other heuristics for selectively and incrementally searching through relevant ground literals. Based on this study, this paper identifies some key criteria, and provides some recommendations, for developing an explanation generation system for robots that exploits the complementary strengths of the two classes
of explanation generation systems.
| Original language | English |
|---|---|
| Title of host publication | SAC '16 - Proceedings of the 31st Annual ACM Symposium on Applied Computing |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 260-267 |
| Volume | 2016 |
| ISBN (Print) | 978-1-4503-3739-7 |
| DOIs | |
| Publication status | Published - 4 Apr 2016 |
| Event | The 31st Annual ACM Symposium on Applied Computing (SAC '16) - Pisa, Italy Duration: 4 Apr 2016 → 8 Apr 2016 |
Conference
| Conference | The 31st Annual ACM Symposium on Applied Computing (SAC '16) |
|---|---|
| Country/Territory | Italy |
| City | Pisa |
| Period | 4/04/16 → 8/04/16 |
Keywords
- Explanation Generation
- Robotics
- Answer Set Prolog
- Heuristic Guidance
- Cognitive Systems