The Power and Limits of Reason: Al-Razı on the Possibility of General and Particular Metaphysical Knowledge

This article examines al-Rāzī’s views on the possibility of metaphysical knowledge. Firstly, after outlining his classification of the metaphysical knowable into essence and existence as well as undetailed (ijmālī) and detailed (tafṣīlī), the article analyzes al-Rāzī’s acceptance of the possibility of general knowledge of metaphysics under a few headings by delving into some major themes. These include the claims that the category of existence is broader than the world of the sensible, that theoretical reasoning leads to metaphysical knowledge, and lastly that the theoretical evidence provides necessary knowledge about the existence of a creator. Al-Rāzī has also been demonstrated in al-Matālib to have inherited the arguments rejecting metaphysical knowledge, which he had attributed in his earlier works to a group with the name muhandisiyyūn, by restricting them to the issue of God’s essence being knowable. For al-Rāzī, theoretical reasoning could provide knowledge about the existence of a particular metaphysical being but not about its quiddity. The article further underlines the metaphysical and epistemic theses for the position on the unknowability of God’s essence and discusses its semantic interpretation. The debate on the potential of theoretical reason to provide uncertain knowledge of detailed metaphysics in the form of the best possible explanations (the metaphysics of the best explanation, or al-awlawiyya), however, is left to another article.

A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics

Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.

Caliber and Chain Conditions in Soft Topologies

In this paper, we contribute to the growing literature on soft topology. Its theoretical underpinning merges point-set or classical topology with the characteristics of soft sets (a model for the representation of uncertain knowledge initiated in 1999). We introduce two types of axioms that generalize suitable concepts of soft separability. They are respectively concerned with calibers and chain conditions. We investigate explicit procedures for the construction of non-trivial soft topological spaces that satisfy these new axioms. Then we explore the role of cardinality in their study, and the relationships among these and other properties. Our results bring to light a fruitful field for future research in soft topology.

Investigating the Short-Circuit Problem Using the Planarity Index of Complex q-Rung Orthopair Fuzzy Planar Graphs

Planar graphs play an effective role in many practical applications where the crossing of edges becomes problematic. This paper aims to investigate the complex q-rung orthopair fuzzy (CQROF) planar graphs (CQROFPGs). In a CQROFPG, the nodes and edges are based on complex QROF information that represents the uncertain knowledge in the range of unit circles in terms of complex numbers. The motivation in discussing such a topic is the wide flexibility of QROF information in the expression of uncertain knowledge compared to intuitionistic and Pythagorean fuzzy settings. We discussed the complex QROF graphs (CQROFGs), complex QROF multigraphs (CQROFMGs), and related terms followed by examples. Furthermore, the notion of strength and planarity index (PI) of the CQROFPGs is defined and exemplified followed by a study of strong and weak edges. We further defined the notion of complex QROF face (CQROFF) and complex QROF dual graph (CQROFDG) and exemplified these concepts. A study of isomorphism, coweak and weak isomorphism, is set up, and some results relating to the CQROFPG and isomorphisms are explored using examples. Furthermore, the problem of short circuits that results due to crossing is discussed because of the proposed study where an algorithm based on complex QROF (CQROF) information is presented for reducing the crossing in networks. Some advantages of the projected study over the previous study are observed, and some future study is predicted.

Vagueness in Medicine: On Disciplinary Indistinctness, Fuzzy Phenomena, Vague Concepts, Uncertain Knowledge, and Fact-Value-Interaction

This article investigates five kinds of vagueness in medicine: disciplinary, ontological, conceptual, epistemic, and vagueness with respect to descriptive-prescriptive connections. First, medicine is a discipline with unclear borders, as it builds on a wide range of other disciplines and subjects. Second, medicine deals with many indistinct phenomena resulting in borderline cases. Third, medicine uses a variety of vague concepts, making it unclear which situations, conditions, and processes that fall under them. Fourth, medicine is based on and produces uncertain knowledge and evidence. Fifth, vagueness emerges in medicine as a result of a wide range of fact-value-interactions. The various kinds of vagueness in medicine can explain many of the basic challenges of modern medicine, such as overdiagnosis, underdiagnosis, and medicalization. Even more, it illustrates how complex and challenging the field of medicine is, but also how important contributions from the philosophy can be for the practice of medicine. By clarifying and, where possible, reducing or limiting vagueness, philosophy can help improving care. Reducing the various types of vagueness can improve clinical decision-making, informing individuals, and health policy making.

A method for root cause diagnosis with picture fuzzy sets based dynamic uncertain causality graph

Root cause diagnosis is of great significance to make efficient decisions in industrial production processes. It is a procedure of fusing knowledge, such as empirical knowledge, process knowledge, and mechanism knowledge. However, it is insufficient and low reliability of cause analysis methods by using crisp values or fuzzy numbers to represent uncertain knowledge. Therefore, a dynamic uncertain causality graph model (DUCG) based on picture fuzzy set (PFS) is proposed to address the problem of uncertain knowledge representation and reasoning. It combines the PFS with DUCG model to express expert doubtful ideas in a complex system. Then, a new PFS operator is introduced to characterize the importance of factors and connections among various information. Moreover, an enhanced knowledge reasoning algorithm is developed based on the PFS operators to resolve causal inference problems. Finally, a numerical example illustrates the effectiveness of the method, and the results show that the proposed model is more reliable and flexible than the existing models.