A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems' movements, adversely impacting the system’s reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

EXPERT SYSTEMS

In recent decades IT and computer systems have evolved rapidly in economic informatics field. The goal is to create user friendly information systems that respond promptly and accurately to requests. Informatics systems evolved into decision assisted systems, and such systems are converted, based on gained experience, in expert systems for creative problem solving that an organization is facing. Expert systems are aimed at rebuilding human reasoning on the expertise obtained from experts, stores knowledge, establishes links between knowledge, have the knowledge and ability to perform human intellectual activities. From the informatics development point of view, expert systems are based on the principle of the knowledge separation from the treating program. Expert systems simulate the human experts reasoning on knowledge available to them, multiply the knowledge and explain their own lines of reasoning.expert systems, artificial intelligence, knowledge, expertise

Logical Reasoning in Management: From “Philosopher Kings” to Logical Managers?

To what extent does a manager’s logical reasoning ability impact their managerial responsibility? This study delves into the significance of logical reasoning ability in the realm of management. To accomplish this objective, I developed a logical reasoning assessment whose internal consistency was confirmed. Subsequently, I conducted an online survey with a sample of 83 managers (Mage = 39.6; SDage = 11.77). The econometric model (R2 adj = 0.431) revealed a cubic relationship, indicating an influence that logical reasoning ability might have on management responsibility. Notably, managers who pursued formal science education exhibited the highest proficiency in logical reasoning. Conversely, neither age nor GPA exhibited any significant correlation with logical reasoning ability among managers. A comparative analysis of managers’ logical reasoning performance against previous studies involving students yielded noteworthy findings, indicating that university students outperformed their managerial counterparts. Whilst acknowledging the study’s limitations, these findings shed light on the relevance of logical reasoning ability in the management domain, offering valuable insights and a starting point for both researchers and practitioners.Keywords: Logical reasoning; Managerial decision making; Formal logic; Management research.To what extent does a manager’s logical reasoning ability impact their managerial responsibility? This study delves into the significance of logical reasoning ability in the realm of management. To accomplish this objective, I developed a logical reasoning assessment whose internal consistency was confirmed. Subsequently, I conducted an online survey with a sample of 83 managers (Mage = 39.6; SDage = 11.77). The econometric model (R2 adj = 0.431) revealed a cubic relationship, indicating an influence that logical reasoning ability might have on management responsibility. Notably, managers who pursued formal science education exhibited the highest proficiency in logical reasoning. Conversely, neither age nor GPA exhibited any significant correlation with logical reasoning ability among managers. A comparative analysis of managers’ logical reasoning performance against previous studies involving students yielded noteworthy findings, indicating that university students outperformed their managerial counterparts. Whilst acknowledging the study’s limitations, these findings shed light on the relevance of logical reasoning ability in the management domain, offering valuable insights and a starting point for both researchers and practitioners.Keywords: Logical reasoning; Managerial decision making; Formal logic; Management research