TY - GEN
T1 - Towards Uncertainty Reduction Tactics for Behavior Adaptation
AU - Kreutz, Andreas
AU - Weiss, Gereon
AU - Trapp, Mario
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - An autonomous system must continuously adapt its behavior to its context in order to fulfill its goals in dynamic environments. Obtaining information about the context, however, often leads to partial knowledge, only, with a high degree of uncertainty. Enabling the systems to actively reduce these uncertainties at run-time by performing additional actions, such as changing a mobile robot’s position to improve the perception with additional perspectives, can increase the systems’ performance. However, incorporating these techniques by adapting behavior plans is not trivial as the potential benefit of such so-called tactics highly depends on the specific context. In this paper, we present an analysis of the performance improvement that can theoretically be achieved with uncertainty reduction tactics. Furthermore, we describe a modeling methodology based on probabilistic data types that makes it possible to estimate the suitability of a tactic in a situation. This methodology is the first step towards enabling autonomous systems to use uncertainty reduction in practice and to plan behavior with more optimal performance.
AB - An autonomous system must continuously adapt its behavior to its context in order to fulfill its goals in dynamic environments. Obtaining information about the context, however, often leads to partial knowledge, only, with a high degree of uncertainty. Enabling the systems to actively reduce these uncertainties at run-time by performing additional actions, such as changing a mobile robot’s position to improve the perception with additional perspectives, can increase the systems’ performance. However, incorporating these techniques by adapting behavior plans is not trivial as the potential benefit of such so-called tactics highly depends on the specific context. In this paper, we present an analysis of the performance improvement that can theoretically be achieved with uncertainty reduction tactics. Furthermore, we describe a modeling methodology based on probabilistic data types that makes it possible to estimate the suitability of a tactic in a situation. This methodology is the first step towards enabling autonomous systems to use uncertainty reduction in practice and to plan behavior with more optimal performance.
KW - Self-adaptive systems
KW - Uncertainty
KW - Uncertainty reduction
UR - http://www.scopus.com/inward/record.url?scp=85185597780&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-36889-9_15
DO - 10.1007/978-3-031-36889-9_15
M3 - Conference contribution
AN - SCOPUS:85185597780
SN - 9783031368882
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 214
BT - Software Architecture - ECSA 2022 Tracks and Workshops, Revised Selected Papers
A2 - Batista, Thais
A2 - Raibulet, Claudia
A2 - Bures, Tomas
A2 - Muccini, Henry
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th European Conference on Software Architecture, ECSA 2022
Y2 - 19 September 2022 through 23 September 2022
ER -