Authors:
Dogan Altan
and
Sanem Sariel-Talay
Affiliation:
Istanbul Technical University, Turkey
Keyword(s):
Probabilistic Failure Isolation, Cognitive Robots, Hierarchical Hidden Markov Models, Model-based Diagnosis, Uncertain Reasoning.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Autonomous Systems
;
Cognitive Robotics
;
Informatics in Control, Automation and Robotics
;
Model-Based Reasoning
;
Robotics and Automation
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
Robots execute their planned actions in the physical world to accomplish their goals. However, since the real world is partially observable and dynamic, failures may occur during the execution of their actions. These failures should be detected immediately, and the underlying reasons of these failures should be isolated to ensure robustness. In this paper, we propose a probabilistic and temporal model-based failure isolation method that maintains Hierarchical Hidden Markov Models (HHMMs) in order to represent and reason about different
failure types. The underlying reason of a failure can be isolated efficiently by multi-hypothesis tracking.