Human reasoning with negative defaults

1996 ◽  
pp. 606-621 ◽  
Author(s):  
Carl Vogel
Keyword(s):  
Human Reasoning

A little too technical: The threat of intellectualising technical reasoning

2020 ◽  
Vol 43 ◽  
Author(s):  
Ian Robertson
Keyword(s):  
Social Cognition ◽  
Human Reasoning ◽  
Technological Culture ◽  
Technical Reasoning

Abstract Osiurak and Reynaud (O&R) claim that research into the origin of cumulative technological culture has been too focused on social cognition and has consequently neglected the importance of uniquely human reasoning capacities. This commentary raises two interrelated theoretical concerns about O&R's notion of technical-reasoning capacities, and suggests how these concerns might be met.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

Causation and Conditionals in the Cognitive Science of Human Reasoning~!2009-12-08~!2010-01-18~!2010-07-13~!

2010 ◽  
Vol 3 (2) ◽  
pp. 105-118 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Cognitive Science ◽  
Human Reasoning

Can Humanism be the Social Norm?

2020 ◽  
Author(s):  
Sharon D. Welch
Keyword(s):  
Social Norms ◽  
Social Norm ◽  
Human Cognition ◽  
Human Reasoning ◽  
Individual Freedom ◽  
Social Divisions ◽  
The Social

Assaults on truth and divisions about the nature of wise governance are not momentary political challenges, unique to particular moments in history. Rather, they demonstrate fundamental weaknesses in human reasoning and core dangers in ways of construing both individual freedom and cohesive communities. It will remain an ongoing challenge to learn to deal rationally with what is an intrinsic irrationality in human cognition and with what is an intrinsic tendency toward domination and violence in human collectivities. In times of intense social divisions, it is vital to consider the ways in which humanism might function as the social norm by, paradoxically, functioning in a way different from other social norms. Humanism is not the declaration that a certain set of values or norms are universally valid. At its best and most creative, humanism is not limited to a particular set of norms, but is, rather, the commitment to a certain process in which norms are continuously created, critically evaluated, implemented, sustained or revised. Humanism is a process of connection, perception, implementation, and critique, and it applies this process as much to itself as to other traditions.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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.

Artificial cognitive systems: Where does argumentation fit in?

2011 ◽  
Vol 34 (2) ◽  
pp. 78-79 ◽  
Author(s):  
John Fox
Keyword(s):  
Social Interaction ◽  
Social Interactions ◽  
Cognitive Systems ◽  
Human Reasoning ◽  
Mental Processes ◽  
Cognitive Agents ◽  
Formal Framework ◽  
Shed Light

AbstractMercier and Sperber (M&S) suggest that human reasoning is reflective and has evolved to support social interaction. Cognitive agents benefit from being able to reflect on their beliefs whether they are acting alone or socially. A formal framework for argumentation that has emerged from research on artificial cognitive systems that parallels M&S's proposals may shed light on mental processes that underpin social interactions.

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.

Logics and Networks for Human Reasoning

2009 ◽  
pp. 85-94 ◽  
Author(s):  
Steffen Hölldobler ◽  
Carroline Dewi Puspa Kencana Ramli
Keyword(s):  
Human Reasoning
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