TY - GEN

T1 - A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems

AU - Stein, Anthony

AU - Tomforde, Sven

AU - Diaconescu, Ada

AU - Hahner, Jorg

AU - Müller-Schloer, Christian

PY - 2018

Y1 - 2018

N2 - The research initiative of self-improving and self-integrating systems (SISSY) emerged as response to the dramatically increasing complexity in information and communication technology. Such systems' ability of autonomous online learning has been identified as a key enabler for SISSY as well as for the broader field of self-adaptive and self-organizing (SASO) systems, since it provides the technical basis for dealing with the inherent dynamics of non-stationary environments that continually challenge these systems with unforeseen situations, disturbances, and changing goals. However, the learning progress is guided by the experiences in terms of situations the system has been exposed to so far - this reactive learning strategy naturally results in missing or inappropriate knowledge. In this paper, we define a formal system model and formulate an abstract learning task for SISSY systems. We further introduce the notion of knowledge and knowledge gaps to subsequently present a novel concept to automatically assess a system's existing knowledge base and, consequently, to proactively acquire knowledge to prepare SISSY/SASO systems for coping with disturbances and other changes that occur at runtime. By the proposed a priori construction of knowledge, we pursue the overall goal to increase the robustness as well as the learning efficiency of self-learning autonomous systems. Endowing these systems with the ability of identifying regions in their knowledge base that are not appropriately covered, strengthens their self-awareness property.

AB - The research initiative of self-improving and self-integrating systems (SISSY) emerged as response to the dramatically increasing complexity in information and communication technology. Such systems' ability of autonomous online learning has been identified as a key enabler for SISSY as well as for the broader field of self-adaptive and self-organizing (SASO) systems, since it provides the technical basis for dealing with the inherent dynamics of non-stationary environments that continually challenge these systems with unforeseen situations, disturbances, and changing goals. However, the learning progress is guided by the experiences in terms of situations the system has been exposed to so far - this reactive learning strategy naturally results in missing or inappropriate knowledge. In this paper, we define a formal system model and formulate an abstract learning task for SISSY systems. We further introduce the notion of knowledge and knowledge gaps to subsequently present a novel concept to automatically assess a system's existing knowledge base and, consequently, to proactively acquire knowledge to prepare SISSY/SASO systems for coping with disturbances and other changes that occur at runtime. By the proposed a priori construction of knowledge, we pursue the overall goal to increase the robustness as well as the learning efficiency of self-learning autonomous systems. Endowing these systems with the ability of identifying regions in their knowledge base that are not appropriately covered, strengthens their self-awareness property.

KW - Active learning

KW - Kernel density estimation

KW - Knowledge

KW - Knowledge gap

KW - Multi layer observer/controller architecture

KW - Proactive knowledge construction

KW - Self awareness

KW - Self learning

UR - http://www.scopus.com/inward/record.url?scp=85061557933&partnerID=8YFLogxK

U2 - 10.1109/FAS-W.2018.00048

DO - 10.1109/FAS-W.2018.00048

M3 - Conference contribution

AN - SCOPUS:85061557933

SN - 9781538651766

SP - 204

EP - 213

BT - 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2018

Y2 - 3 September 2018 through 7 September 2018

ER -