Rationality and Intelligence

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Abstract
This chapter considers how to formalize intelligence or rationality in a way that has value for the development of agents built for a specific application and of general theories of intelligence. It presents three candidates that traditionally have stood as formalizations of intelligence: perfect rationality, calculative rationality, and meta-level rationality. Perfect rationality is an abstraction that does not correspond to any physical reasoner. Calculative rationality fails to scale up to problems of sufficient and interesting complexity. Meta-level rationality pushes the problem into a never-ending regress. As an alternative, this chapter considers the notion of bounded optimality as a workable proxy for theorizing about machine intelligence. This notion rests on two crucial elements: that behaviors and decisions happen in real time and that an agent is defined by a particular (software and hardware) architecture and a particular program that runs on that architecture. Under this view, an agent is bounded optimal if it maximizes the utility of its behavior for a task within the demands of the environment. The chapter then elaborates on the role of adaptive, inductive mechanisms as the means for making gains in calculative and meta-level rationality for real-world application systems, and for bounded optimality more generally.

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