The Research of Artificial Animal’s Behavior Memory Based on Cognition

Abstract

In some artificial systems, performing the realistic perception for actors, which will handle both the processes to simulate the sensing organs and identify, will spend most computational time. Unfortunately, this matter even ruins the result of decision based on perception. In order to reduce the computation cost from a systemic view and optimize the performance of system, a brand-new perceptual focuser was proposed. The perceptual focuser is the core of artificial animal sensation system, which provides the external environment and internal condition information for the behavior decision system. Artificial animal behavior memory formation is also the result of the focuser’s analysis focusing. This paper proposes and analyzes two kinds of memory-form algorithm. The quadratic method to food spot which gains has made the variance computation, rejects noise spots which are apart from other normal spots, so that it obtains the expected food distribution position. In view of the quadratic method’s insufficiency, the improvement mean-cluster algorithm profits from the data mining theory, which makes the noise-spotrejection accurately. Select the algorithm according to the different situation, which can make the focuser achieve the validity of the realization of artificial animal food memory.KeywordsRoute ChoiceArtificial LifeArtificial FishBehavior MemoryNoise PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.