Human and Machine Problem Solving Toward a Comparative Cognitive Science

1989 ◽  
pp. 1-12 ◽  
Author(s):  
K. J. Gilhooly
Keyword(s):  
Problem Solving ◽  
Cognitive Science ◽  
Machine Problem ◽  
Comparative Cognitive Science

Taxonomies of Problems and Generic Skills

2010 ◽  
pp. 107-123
Author(s):  
Gilbert Paquette
Keyword(s):  
Problem Solving ◽  
Software Engineering ◽  
Cognitive Science ◽  
Learning Objectives ◽  
Problem Solving Skills ◽  
Generic Skills ◽  
Research In Education ◽  
Human Problem

The aim of this chapter is to define what we call “generic skills,” i.e. structured sets of intellectual actions, attitudes, values, and principles that are at the heart of human competencies. We will first examine the various systems that offer different yet convergent views regarding skills. One multi-viewpoint approach to the concept of skill first analyses the taxonomies of generic problems developed in software engineering. Generic problems correspond to human problem-solving skills as described in cognitive science. Another viewpoint is the concept of active meta-knowledge that situates skills in the realm of meta-cognition, i.e. as knowledge acting on other knowledge. A third viewpoint considers research in education that presents skills in the form of taxonomies of learning objectives in relation to cognitive, affective, social, or psychomotor domains.

scholarly journals On Accelerations in Science Driven by Daring Ideas: Good Messages from Fallibilistic Rationalism

2015 ◽  
Vol 40 (1) ◽  
pp. 19-41 ◽  
Author(s):  
Witold Marciszewski
Keyword(s):  
Problem Solving ◽  
Cognitive Science ◽  
Cognitive Processes ◽  
Classical Version ◽  
Conceptual Apparatus ◽  
Processing Information ◽  
New Ideas ◽  
The Senses

Abstract The first good message is to the effect that people possess reason as a source of intellectual insights, not available to the senses, as e.g. axioms of arithmetic. The awareness of this fact is called rationalism. Another good message is that reason can daringly quest for and gain new plausible insights. Those, if suitably checked and confirmed, can entail a revision of former results, also in mathematics, and - due to the greater efficiency of new ideas - accelerate science’s progress. The awareness that no insight is secured against revision, is called fallibilism. This modern fallibilistic rationalism (Peirce, Popper, Gödel, etc. oppose the fundamentalism of the classical version (Plato, Descartes etc.), i.e. the belief in the attainability of inviolable truths of reason which would forever constitute the foundations of knowledge. Fallibilistic rationalism is based on the idea that any problem-solving consists in processing information. Its results vary with respect to informativeness and its reverse - certainty. It is up to science to look for highly informative solutions, in spite of their uncertainty, and then to make them more certain through testing against suitable evidence. To account for such cognitive processes, one resorts to the conceptual apparatus of logic, informatics, and cognitive science.

scholarly journals Visual cognition from the standpoint of comparative cognitive science

2002 ◽  
Vol 52 (1) ◽  
pp. 29-44
Author(s):  
MASAKI TOMONAGA ◽  
TAKATSUNE KUMADA ◽  
SAKIKO YOSHIKAWA
Keyword(s):  
Cognitive Science ◽  
Visual Cognition ◽  
Comparative Cognitive Science

scholarly journals AI, visual imagery, and a case study on the challenges posed by human intelligence tests

2020 ◽  
Vol 117 (47) ◽  
pp. 29390-29397 ◽  
Author(s):  
Maithilee Kunda
Keyword(s):  
Problem Solving ◽  
Cognitive Science ◽  
Visual Imagery ◽  
Domain Knowledge ◽  
Intelligence Tests ◽  
Human Intelligence ◽  
Artificial Agents ◽  
Knowledge Representations ◽  
Problem Solving Strategies

Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone’s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven’s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.

Sign in / Sign up

Export Citation Format

Share Document