Titlebook: Mathematical Learning Models — Theory and Algorithms; Proceedings of a Con Ulrich Herkenrath,Dieter Kalin,Walter Vogel Conference proceedin

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Asymptotic Properties of Learning Models,te we consider a measurable mapping v of S × E into S and postulate that S.. = v(S., E..), n ≥ 0. Finally, we assume that the conditional probability distribution of E.. given E., S.,... depends only on the state S. and denote it Q(S.,.).

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Bandit Problems with Random Discounting,discounted: the m. observation is weighted by α.. The α. are random variables. They may be dependent and their distributions unknown; in such a case one can learn about the character of the discounting as well as about the processes. The objective is to maximize the expected sum of the weighted obse

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Learning Automaton for Finite Semi-Markov Decision Processes,parameter taking values in a subset [., .] of ℝ.. A controller modelled as a learning automaton updates sequentially the probabilities of generating decisions based on the observed decisions, states, and jump times. Convergence results are stated in the form of theorems and some examples are given.

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Asymptotic Properties of Learning Models,r of the subject on trial . = 0,1,... is determined by his . S. (an indicator of the subject’s response tendencies) at the beginning of the trial. S. is a random variable taking on values in a measurable . (S,.). On trial n an . E.. occurs that results in a change of state. E.. is a randon variable

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Uniform Bounds for a Dynamic Programming Model under Adaptive Control Using Exponentially Bounded Eted by T.. We give a bound on how much we loose compared with the maximum total reward if we use a plan that would be optimal if the estimated value T. was the true value of θ. We generalize a result of Rényi for finite Θ to a discretized infinite Θ to give a bound that holds independently of the tr

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On a Class of Learning Algorithms with Symmetric Behavior under Success and Failure,and Mathematical Statistics [1] [2] [8] [11] [16]. In Mathematical Psychology the interest in learning stems from the desire to understand the observed animal learning and associated changes in their behavior. However, in Learning Automata Theory and Mathematical Statistics the aim is to build algor