Can humans form hierarchically embedded mental representations?

1998 ◽  
Vol 21 (5) ◽  
pp. 687-688
Author(s):  
Denise Dellarosa Cummins
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Developmental Psychology ◽  
Mental Representations ◽  
Domain Specificity ◽  
Human Reasoning ◽  
Goal Structure ◽  
Action Sequences ◽  
Intelligent Behavior

Certain recurring themes have emerged from research on intelligent behavior from literatures as diverse as developmental psychology, artificial intelligence, human reasoning and problem solving, and primatology. These themes include the importance of sensitivity to goal structure rather than action sequences in intelligent learning, the capacity to construct and manipulate hierarchically embedded mental representations, and a troubling domain specificity in the manifestation of each.

Images of Aging in the Psychotherapeutic Context

GeroPsych ◽  
2015 ◽  
Vol 28 (2) ◽  
pp. 47-55 ◽  
Author(s):  
Eva-Marie Kessler ◽  
Catherine E. Bowen
Keyword(s):  
Old Age ◽  
Diagnostic Tests ◽  
Aging Process ◽  
Developmental Psychology ◽  
Mental Representations ◽  
Later Life ◽  
Future Research ◽  
Psychotherapeutic Process ◽  
Images Of Aging ◽  
Client Communication

Both psychotherapists and their clients have mental representations of old age and the aging process. In this conceptual review, we draw on available research from gerontology, social and developmental psychology, and communication science to consider how these “images of aging” may affect the psychotherapeutic process with older clients. On the basis of selected empirical findings we hypothesize that such images may affect the pathways to psychotherapy in later life, therapist-client communication, client performance on diagnostic tests as well as how therapists select and apply a therapeutic method. We posit that interventions to help both older clients and therapists to reflect on their own images of aging may increase the likelihood of successful treatment. We conclude by making suggestions for future research.

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.

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.

scholarly journals Representations, Affordances, and Interactive Systems

2021 ◽  
Vol 5 (5) ◽  
pp. 23
Author(s):  
Robert Rowe
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Data Structures ◽  
Digital Computer ◽  
Mental Representations ◽  
Interactive Systems ◽  
Algorithmic Composition ◽  
The World ◽  
History Of

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.

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.

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

scholarly journals Strategy selection as rational metareasoning

2016 ◽  
Author(s):  
Falk Lieder ◽  
Tom Griffiths
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cost Benefit ◽  
Human Reasoning ◽  
Strategy Selection ◽  
Heuristics And Biases ◽  
Rational Model ◽  
The Mind ◽  
Over Time ◽  
Human Rationality

Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy’s performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to problem-solving and arithmetic by capturing the variability of people’s strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and four new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the two poles of the debate about human rationality by integrating heuristics and biases with learning and rationality.

scholarly journals Gesture Recognition in Images Using Neural Networks

2019 ◽  
Vol 8 (11) ◽  
pp. e278111470
Author(s):  
André Ricardo Nascimento das Neves ◽  
Hugo Kenji Rodrigues Okada ◽  
Ricardo Shitsuka
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Gesture Recognition ◽  
Experimental Tests ◽  
Product Reviews ◽  
Human Reasoning ◽  
Initial Part ◽  
Computer Research

Artificial Intelligence is an area of computer research that is focused on developing mechanisms and devices to simulate human reasoning. Within this, an important subarea is the recognition of images. This article aims to describe the initial part of a research that aims to analyze and identify registered feelings of body expressions in videos of product reviews. Experimental tests have been planned to identify the best technique to solve the problem. Some forms of gesture identification through the use of neural networks were analyzed and tested.

Sign in / Sign up

Export Citation Format

Share Document