Wed 10 Apr 1996, 12:00, WeH 7220 ================================================================== Making Decisions with a "quasi"-Bayesian Model Fabio Cozman There has been great progress in finding best decisions when probabilities are known precisely (e.g. Michael Littman's talk). So it seems that an agent should strive to "learn" the probabilities of interest and then pick the best decision. In this talk I'll present a class of Markov decisions where we *can* find admissible decisions very fast if we work with a set of probability distributions, but we *can't* if we constrain ourselves to a single probability. I'm currently applying this problem to estimation of material properties with a robotic probe. So I'd like to discuss some points: Do we really need to "learn" a single probability distribution? Wouldn't it be more reasonable to say that, when we learn, we are actually "constructing" the probability? And how would we look for best decisions while our probabilities are being learned --- i.e., constructed? I'll show how these questions can be formalized in the framework of Quasi-Bayesian theory, which has been used in Statistics for robustness studies. Hopefully we will have time to discuss how this fresh look at old problems can reduce the complexity of our learning problems.