  This paper reconsiders the problem of the absent-minded driver who must
choose between alternatives with different payoff with imperfect recall and
varying degrees of knowledge of the system. The classical absent-minded driver
problem represents the case with limited information and it has bearing on the
general area of communication and learning, social choice, mechanism design,
auctions, theories of knowledge, belief, and rational agency. Within the
framework of extensive games, this problem has applications to many artificial
intelligence scenarios. It is obvious that the performance of the agent
improves as information available increases. It is shown that a non-uniform
assignment strategy for successive choices does better than a fixed probability
strategy. We consider both classical and quantum approaches to the problem. We
argue that the superior performance of quantum decisions with access to
entanglement cannot be fairly compared to a classical algorithm. If the
cognitive systems of agents are taken to have access to quantum resources, or
have a quantum mechanical basis, then that can be leveraged into superior
performance.
