Measuring Agent Intelligence via Hierarchies of Environments

Under Legg’s and Hutter’s formal measure [1], performance in easy environments counts more toward an agent’s intelligence than does performance in difficult environments. An alternate measure of intelligence is proposed based on a hierarchy of sets of increasingly difficult environments, in a reinforcement learning framework. An agent’s intelligence is measured as the ordinal of the most difficult set of environments it can pass. This measure is defined in both Turing machine and finite state machine models of computing. In the finite model the measure includes the number of time steps required to pass the test.