Visual search light model for mental problem solving

Sub-Symbols and Icons

“…By means of this correlation, it is possible to determine the associations, which manipulate the information of the visual buffer [48].…”

“…This problem is called the permutation problem, and a neural associative memory architecture, in which the problem could be simplified, was presented by Wichert et al [45,47,48]. We do not need to form all possible permutations.…”

“…In this case, the association can be executed, and the conclusion sub-vectors of the association can replace the corresponding sub-vectors. Out of several possible associations, one has to be chosen [45,47,48].…”

“…Image representation also enables learning from examples through the definition of similarity between different problems. Empirical experiments in popular problem-solving domains of Artificial Intelligence, like robot in a maze, block world or eight-puzzle indicated that the distance between the states in the problem space is actually related to the distance between the icons representing the states [45,47,48]. It was also shown by computer experiments that learning from examples speeds up the search in the problem space considerably [46].…”

“…An important part is the transition from the sub-symbolical representation into symbolical representation and the corresponding heuristic function [39]. For example, the iconic representation of the states of eight-puzzle problem is equivalent to the symbolical representation of the states and the heuristic function city-block distance well known in symbolical Artificial Intelligence [48].…”