An approach to artificial nonsymbolic cognition

Abstract

In this paper we describe an approach to the artificial recognition of events of a nonsymbolic nature, such as the bidimensional perspective views of scenes of our everyday world. Scenes are presented as colored pictures and the objective of the cognitive task is the labeling of the interpreted scene objects. The method is based on three major components: 1. (i) a preprocessed version of the scene (stimulus), 2. (ii) a semantic map and 3. (iii) an algorithm which performs interpretation of the stimulus under the guidance of the semantic map. The algorithm is sequential and proceeds from general to specific, thereby achieving efficient tree-pruning (contextual elimination). Stimulus interpretation is based on attribute-value matching, but classification relies strongly on the accumulated context. Backtracking provisions are available in the sequential cognitive algorithm for correction of earlier wrong hypotheses. Experiments are presented and described. The major weakness of the approach is the present lack of a satisfactory theory of inference. Flexibility, generalizability, and efficiency appear to be valid merits.