Bounded Rationality in Artificial Intelligence to Sensing

Abstract

The field of artificial intelligence (AI) has made significant strides in mimicking human cognitive processes, yet the concept of bounded rationality introduces a realistic lens through which AI systems approach decision-making, especially in the realm of sensing. Bounded rationality acknowledges the inherent limitations faced by AI systems in accessing complete information, allocating resources, and processing data in real-time. This abstract explores the implications of bounded rationality on AI systems engaged in sensing tasks within dynamic environments. In sensing applications, AI systems rely on various sensors to collect data from their surroundings. However, these systems often operate under constraints, such as limited computational resources, energy availability, and time pressure. Bounded rationality prompts a shift in perspective, emphasizing the need for adaptive and flexible decision-making processes that can effectively navigate uncertainties. The challenges posed by bounded rationality underscore the importance of developing algorithms that strike a balance between accuracy and computational efficiency. As AI continues to be integrated into various applications, understanding and addressing these challenges will be critical for the successful deployment of intelligent systems in real-world, dynamic environments. In conclusion, this abstract provides an overview of the impact of bounded rationality on AI systems engaged in sensing tasks. By recognizing and embracing the constraints inherent in decision-making processes, researchers and developers can pave the way for more realistic, efficient, and adaptable AI systems that navigate the complexities of dynamic environments.

Keywords: Artificial, Intelligence, Rationality, Bounded, Rationality, Decision-making, Sensing.