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We update the method of describing and assessing the process of the study ofan abstract environment by a system, proposed earlier. We do not model anybiological cognition mechanisms and consider the system as an agent equippedwith an information processor (or a group of such agents), which makes a movein the environment, consumes information supplied by the environment, and givesout the next move (hence, the process is considered as a game). The systemmoves in an unknown environment and should recognize new objects located in it.In this case, the system should build comprehensive images of visible thingsand memorize them if necessary (and it should also choose the current goalset). The main problems here are object recognition, and the informationalreward rating in the game. Thus, the main novelty of the paper is a new methodof evaluating the amount of visual information about the object as the reward.In such a system, we suggest using a minimally pre-trained neural network to beresponsible for the recognition: at first, we train the network only forBiederman geons (geometrical primitives). The geons are generatedprogrammatically and we demonstrate that such a trained network recognizesgeons in real objects quite well. We also offer to generate, procedurally, newobjects from geon schemes (geon combinations in images) obtained from theenvironment and to store them in a database. In this case, we do not obtain newinformation about an object (i.e., our reward is maximal, thus the game and theobject cognition process stop) when we stop getting new schemes of this kind.These schemes are generated from geons connected with the object. In the caseof a possibly known item, the informational reward is maximal when we have nomore detection uncertainty for any of the objects.

Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration