7.3 Toward a Formal Characterization of Real-World General Intelligence 135 7.3 Toward a Formal Characterization of Real-World General Intelligence Having defined what we mean by an agent acting in an environment, we now turn to the question of what it means for such an agent to be “intelligent.” As we have reviewed extensively in Chapter 2 above, “intelligence” is a commonsense, “folk psychology” concept, with all the imprecision and contextuality that this generally entails. One cannot expect any compact, elegant formalism to capture all of its meanings. Even in the psychology and AI research communities, divergent definitions abound; Legg and Hutter [L107a] lists and organizes 70+ definitions from the literature. Practical study of natural intelligence in humans and other organisms, and practical de- sign, creation and instruction of artificial intelligences, can proceed perfectly well without an agreed-upon formalization of the “intelligence” concept. Some researchers may conceive their own formalisms to guide their own work, others may feel no need for any such thing. But nevertheless, it is of interest to seek formalizations of the concept of intelligence, which capture useful fragments of the commonsense notion of intelligence, and provide guidance for practical research in cognitive science and AI. A number of such formalizations have been given in recent decades, with varying degrees of mathematical rigor. Perhaps the most carefully- wrought formalization of intelligence so far is the theory of “universal intelligence” presented by Shane Legg and Marcus Hutter in [LI07b], which draws on ideas from algorithmic information theory. Universal intelligence captures a certain aspect of the “intelligence” concept very well, and has the advantage of connecting closely with ideas in learning theory, decision theory and computation theory. However, the kind of general intelligence it captures best, is a kind which is in a sense more general in scope than human