Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences

2012 ◽  
Vol 4 (4) ◽  
pp. 70-86 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Computational Intelligence ◽  
Fuzzy Inference ◽  
Human Reasoning ◽  
Linguistic Variables ◽  
Cognitive Computing ◽  
Extended Form ◽  
Rational Reasoning ◽  
Formal Rules

Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoning the semantics of fuzzy causalities. This paper presents a set of formal rules for causal analyses and fuzzy inferences such as those of deductive, inductive, abductive, and analogical inferences. Rules and methodologies for each of the fuzzy inferences are formally modeled and illustrated with real-world examples and cases of applications. The formalization of fuzzy inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, soft computing, abstract intelligence, and computational intelligence.

On Cognitive Models of Causal Inferences and Causation Networks

2011 ◽  
Vol 3 (1) ◽  
pp. 50-60 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

On Cognitive Models of Causal Inferences and Causation Networks

2013 ◽  
pp. 103-113
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

Alice in the Real World

2012 ◽  
Vol 17 (7) ◽  
pp. 410-416 ◽  
Author(s):  
Tom Parker
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Mathematical Problem ◽  
Computer Application ◽  
Mathematical Problem Solving ◽  
The Real ◽  
Programming Knowledge

A computer application promotes programming knowledge and allows students to create their own worlds through mathematical problem solving.

From Problem-Solving Paradigm to Co-Ontogenic Drift: How Do Learning Narratives Self-Generate?

Leonardo ◽  
2011 ◽  
Vol 44 (2) ◽  
pp. 133-138
Author(s):  
Johann van der Merwe ◽  
Julia Brewis
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Design Education ◽  
Design Theory ◽  
Social Situation ◽  
Theory And Practice ◽  
Use Of Self ◽  
Real World Problems

It is now an accepted maxim in design theory and practice that real-world problems needing the attention of design practitioners are not neat and well-structured, but ill-structured and “wicked”—part of a larger, complex social situation. For design education, then, to take its lead from contemporary social, political and economic structures, it will have to seriously re-think its problem-solving paradigms. The authors investigate the use of self-generating learning narratives in the classroom and contrast the approach they introduce with the still-too-prevalent notion that knowledge can be transferred from teacher to student. Their methodology draws from ideas formulated by Maturana and Varela on autopoiesis, specifically the notion of co-ontogenic drift.

Boosting branch-and-bound MaxSAT solvers with clause learning

2021 ◽  
pp. 1-21
Author(s):  
Chu-Min Li ◽  
Zhenxing Xu ◽  
Jordi Coll ◽  
Felip Manyà ◽  
Djamal Habet ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Combinatorial Optimization ◽  
Problem Solving ◽  
Branch And Bound ◽  
Real World ◽  
Optimization Problems ◽  
Satisfiability Problem ◽  
Maximum Satisfiability ◽  
Combinatorial Optimization Problems ◽  
Clause Learning

The Maximum Satisfiability Problem, or MaxSAT, offers a suitable problem solving formalism for combinatorial optimization problems. Nevertheless, MaxSAT solvers implementing the Branch-and-Bound (BnB) scheme have not succeeded in solving challenging real-world optimization problems. It is widely believed that BnB MaxSAT solvers are only superior on random and some specific crafted instances. At the same time, SAT-based MaxSAT solvers perform particularly well on real-world instances. To overcome this shortcoming of BnB MaxSAT solvers, this paper proposes a new BnB MaxSAT solver called MaxCDCL. The main feature of MaxCDCL is the combination of clause learning of soft conflicts and an efficient bounding procedure. Moreover, the paper reports on an experimental investigation showing that MaxCDCL is competitive when compared with the best performing solvers of the 2020 MaxSAT Evaluation. MaxCDCL performs very well on real-world instances, and solves a number of instances that other solvers cannot solve. Furthermore, MaxCDCL, when combined with the best performing MaxSAT solvers, solves the highest number of instances of a collection from all the MaxSAT evaluations held so far.

Perspectives on Cognitive Informatics and Cognitive Computing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information ◽  
Intelligence Science

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

On Considering Constraints of Different Importance in Fuzzy Constraint Satisfaction Problems

1998 ◽  
Vol 06 (05) ◽  
pp. 489-501 ◽  
Author(s):  
Vicenç Torra I Reventós
Keyword(s):  
Constraint Satisfaction ◽  
Real World ◽  
Constraint Satisfaction Problems ◽  
Human Reasoning ◽  
Fuzzy Constraints ◽  
A Value ◽  
Equal Importance ◽  
Real World Applications ◽  
Fuzzy Constraint ◽  
Fuzzy Constraint Satisfaction

Several real-world applications (e.g., scheduling, configuration, …) can be formulated as Constraint Satisfaction Problems (CSP). In these cases, a set of variables have to be settled to a value with the requirement that they satisfy a set of constraints. Classical CSPs are defined only by means of crisp (Boolean) constraints. However, as sometimes Boolean constraints are too strict in relation to human reasoning, fuzzy constraints were introduced. When fuzzy constraints are considered, human reasoning usually performs some compensation between alternatives. Thus other operators than t-norms are advisable. Besides of that, not all constraints can be considered with equal importance. In this paper we show that the WOWA operator can consider both aspects: compensation between constraints and constraints of different importance.

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