TY - GEN
T1 - A Hierarchical Monitoring and Diagnosis System for Autonomous Robots
AU - Steinbauer-Wagner, Gerald
AU - Fürbaß, Leo
AU - De Bortoli, Marco
AU - Travé-Massuyès, Louise
N1 - Publisher Copyright:
© Gerald Steinbauer-Wagner, Leo Fürbaß, Marco De Bortoli, and Louise Travé-Massuyès.
PY - 2024/11/26
Y1 - 2024/11/26
N2 - This paper addresses the capability of autonomous robots to achieve flexible goals in dynamic environments. In such a setting numerous challenges jeopardize the robustness of such systems. Thus, we propose a hierarchical diagnosis concept for layered control architectures, that can detect and deal with such challenges to maintain a consistent knowledge about the world and to allow reliable decision-making. Layered control systems use various knowledge representations and decision-making mechanisms teamed with specialized isolated fault-handling approaches. However, some issues can only be identified if the information from different layers is combined. Our approach addresses challenges like failing actions, uncertain observations, and unmodeled events by propagating observations and diagnoses results throughout the hierarchy. This enhances adaptability and dependability in various domains. In this paper, we present a prototype architecture following this approach.
AB - This paper addresses the capability of autonomous robots to achieve flexible goals in dynamic environments. In such a setting numerous challenges jeopardize the robustness of such systems. Thus, we propose a hierarchical diagnosis concept for layered control architectures, that can detect and deal with such challenges to maintain a consistent knowledge about the world and to allow reliable decision-making. Layered control systems use various knowledge representations and decision-making mechanisms teamed with specialized isolated fault-handling approaches. However, some issues can only be identified if the information from different layers is combined. Our approach addresses challenges like failing actions, uncertain observations, and unmodeled events by propagating observations and diagnoses results throughout the hierarchy. This enhances adaptability and dependability in various domains. In this paper, we present a prototype architecture following this approach.
KW - Autonomous Agents
KW - Cognitive Architecture
KW - Dependability
KW - Hierarchical Monitoring and Diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85211954128&partnerID=8YFLogxK
U2 - 10.4230/OASIcs.DX.2024.1
DO - 10.4230/OASIcs.DX.2024.1
M3 - Conference paper
AN - SCOPUS:85211954128
T3 - OpenAccess Series in Informatics
BT - 35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
A2 - Pill, Ingo
A2 - Natan, Avraham
A2 - Wotawa, Franz
PB - Schloss Dagstuhl - Leibniz-Zentrum für Informatik
T2 - 35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
Y2 - 4 November 2024 through 7 November 2024
ER -