5,379 research outputs found
The challenge of complexity for cognitive systems
Complex cognition addresses research on (a) high-level cognitive processes β mainly problem solving, reasoning, and decision making β and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods β analytical, empirical, and engineering methods β which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition β complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research
The Mode of Computing
The Turing Machine is the paradigmatic case of computing machines, but there
are others, such as Artificial Neural Networks, Table Computing,
Relational-Indeterminate Computing and diverse forms of analogical computing,
each of which based on a particular underlying intuition of the phenomenon of
computing. This variety can be captured in terms of system levels,
re-interpreting and generalizing Newell's hierarchy, which includes the
knowledge level at the top and the symbol level immediately below it. In this
re-interpretation the knowledge level consists of human knowledge and the
symbol level is generalized into a new level that here is called The Mode of
Computing. Natural computing performed by the brains of humans and non-human
animals with a developed enough neural system should be understood in terms of
a hierarchy of system levels too. By analogy from standard computing machinery
there must be a system level above the neural circuitry levels and directly
below the knowledge level that is named here The mode of Natural Computing. A
central question for Cognition is the characterization of this mode. The Mode
of Computing provides a novel perspective on the phenomena of computing,
interpreting, the representational and non-representational views of cognition,
and consciousness.Comment: 35 pages, 8 figure
Solving Tree Problems with Category Theory
Artificial Intelligence (AI) has long pursued models, theories, and
techniques to imbue machines with human-like general intelligence. Yet even the
currently predominant data-driven approaches in AI seem to be lacking humans'
unique ability to solve wide ranges of problems. This situation begs the
question of the existence of principles that underlie general problem-solving
capabilities. We approach this question through the mathematical formulation of
analogies across different problems and solutions. We focus in particular on
problems that could be represented as tree-like structures. Most importantly,
we adopt a category-theoretic approach in formalising tree problems as
categories, and in proving the existence of equivalences across apparently
unrelated problem domains. We prove the existence of a functor between the
category of tree problems and the category of solutions. We also provide a
weaker version of the functor by quantifying equivalences of problem categories
using a metric on tree problems.Comment: 10 pages, 4 figures, International Conference on Artificial General
Intelligence (AGI) 201
LEMBAR KERJA DINAMIS GEOGEBRA UNTUK MENINGKATKAN KEMAMPUAN PENALARAN ANALOGIS SISWA
This study aims to develop GeoGebra Dynamic Worksheets to enhance students' analogical reasoning abilities. Students are expected to interact with GeoGebra Dynamic Worksheets or Dynamic Activities. This research used Design Didactical Research (DDR). Based on the learning obstacles faced by students when carrying out the analogical reasoning process, the GeoGebra Dynamic Worksheet was developed as follows: 1) Reinforcement of the retrieval process: The GeoGebra Worksheet is designed so that students remember in advance about the material or situation that is analogous to the material to be discussed; 2) Strengthening the mapping process: GeoGebra Dynamic Worksheets are designed so that students are able to align the previous analog situation with the new analog situation; 3) Strengthening the evaluation process: several questions are displayed on the Dynamic Worksheet
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