Towards a Universal Theory of Artiicial Intelligence Based on Algorithmic Probability and Sequential Decision Theory
نویسنده
چکیده
Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoo's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AI model behaves optimal in any computable environment. The major drawback of the AI model is that it is uncomputable. To overcome this problem, we construct a modiied algorithm AI tl , which is still superior to any other time t and space l bounded agent. The computation time of AI tl is of the order t2 l .
منابع مشابه
Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory
Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff’s theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIξ model behaves optimal in any computable environment...
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Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff’s theory of universal induction formally solves the problem of sequence prediction for unknown distributions. We unify both theories and give strong arguments that the resulting universal AIξ model behaves optimally in any computable environm...
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