Intelligent Agents Diagnostics—Enhancing Cyber‐Physical Systems with Self‐Diagnostic Capabilities. (6th January 2021)

• Record Type: Journal Article
• Authors: Kaufmann, David; Nica, Iulia; Wotawa, Franz
• Is Part Of: Advanced intelligent systems. Volume 3:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-06
• Subjects: diagnosis -- model‐based reasoning -- self‐healing systems; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202000218
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Allowing intelligent agents to deal with unforeseen situations that have not been considered during development in a smart way is a first step for increasing their autonomy. This requires diagnostic capabilities to detect the unforeseen situation and to identify a root cause that can be used afterward for carrying out repair and other compensating actions. Herein, foundations for diagnostic reasoning based on models of the system are provided. In particular, a diagnostic solution is presented that utilizes answer set solvers, which allow implementing non‐monotonic reasoning. The underlying ideas are introduced, an algorithm is discussed, and experimental results are obtained to clarify the question whether the approach can be used in practical applications. The obtained results indicate that answer set solving provides similar and sometimes even better results than specialized diagnosis algorithms, and can be used in practice. Abstract : Allowing intelligent agents to smartly handle unforeseen situations is a first step toward increasing autonomy. This requires diagnostic capabilities for detecting, locating, and compensating such situations during runtime. For this purpose, foundations for diagnostic reasoning are provided, a diagnosis architecture and a diagnosis algorithm are discussed, and experimental results are presented, indicating the practical applicability of the introduced approach.