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A tale of many explanations
- Source :
- SAC
- Publication Year :
- 2016
- Publisher :
- ACM, 2016.
-
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.
- Subjects :
- 0209 industrial biotechnology
business.industry
Computer science
Robotics
02 engineering and technology
Domain (software engineering)
Prolog
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
Robot
Domain knowledge
020201 artificial intelligence & image processing
Artificial intelligence
Heuristics
Representation (mathematics)
business
computer
computer.programming_language
Declarative programming
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 31st Annual ACM Symposium on Applied Computing
- Accession number :
- edsair.doi...........4c94787b2702da1d9c59548db35c8b3a