Lateralization in Agents' Decision Making: Evidence of Benefits/Costs from Artificial Intelligence

TL;DR

This paper introduces two novel lateralized AI systems that outperform traditional models in complex problem-solving by representing inputs at multiple levels and efficiently managing computations, inspired by biological lateralization.

Contribution

The paper presents and tests two new lateralized AI systems that demonstrate improved performance and efficiency over non-lateralized systems in complex tasks.

Findings

Lateralized AI systems outperform non-lateralized systems in complex problem-solving.

Lateralized systems efficiently represent input at multiple levels simultaneously.

Lateralized systems have comparable or lower computational costs than traditional models.

Abstract

Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization has been incorporated into artificially intelligent systems to solve complex problems in computer vision and navigation domains. Here we describe and test two novel lateralized artificial intelligent systems that simultaneously represent and address given problems at constituent and holistic levels. The experimental results demonstrate that the lateralized systems outperformed state-of-the-art non-lateralized systems in resolving complex problems. The advantages arise from the abilities, (i) to represent an input signal at both the constituent level and holistic level simultaneously, such that the most appropriate viewpoint controls the system; (ii) to avoid extraneous computations by generating excite and inhibit signals. The computational costs associated with the lateralized AI systems are either less than the conventional AI systems or countered by providing better solutions.