THE COMPETENCE OF A MULTIPLE CONTEXT LEARNING SYSTEM

Abstract

Abstract There are four ways in which a finite (and therefore realizable) robot learning system can be given beyond-finile-slutc computational power: (1) by being given “auxiliary” actions, like speech, the system achieves a beyond-finile-stale “competence”(2) by being coupled to (made “open” to) a beyond-finite-statc system, like the real world, the system attains a beyond-finite-state “performance” (3) by being limited to a finite “lifetime”, the system's finite-stale behaviour can be made indistinguishable from beyond-finite-state “competence” and “performance” and (4) by being taught the grammar of a beyond-finite-state language, the system acquires a beyond-finite-state “competence”. The “competence” and “performance” of our Multiple Context Learning System (MCLS) have been argued and demonstrated, previously, in the first three ways, even though our critics choose to ignore the record! Here, it is shown explicitly and in detail how the MCLS can learn, hold and simulate both the finite-stale controller and the tape of a Universal Turing Machine. INDEX TERMS: Competencelearningmultiple contextTuring machine.