Schneider, J., Boyan, J., Moore, A.: Value Function Based Production Scheduling. Proceedings of the 15th International Conference on Machine Learning (1998) Home/Search Document Details and Download
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Fynesse: A New Architecture For Sequential Decision Problems - Schoknecht, Spott.. (Correct)
....learning situations, where judgement about success or failure is the only training information provided to the learning system. In recent years, many successful applications of this powerful learning technique have been described, ranging from escalator control [2] job shop scheduling [6] [16], 15] to technical process control and robotics [9] 13] 17] However, there are two main obstacles hindering broad industrial application: long training times and the instability of learning in large continuous state spaces, when general function approximators have to be used [22] 5] One ....
Je Schneider, Justin Boyan, and Andrew Moore. Value function based production scheduling. In International Conference on Machine Learning, 1998.
A Neural Reinforcement Learning Approach to Learn Local.. - Riedmiller, Riedmiller (1999) (4 citations) (Correct)
....as a consequence the type of control decisions. Zhang and Dietterich [ Dietterich and Zhang, 1995 ] propose an RL approach that learns a neural value function to guide a repairbased scheduler. An action in this approach is the decision for a certain repair operation. Schneider, Boyan and Moore [ Schneider et al. 1998 ] present a value function based approach for the problem of demand based scheduling. The learning scheduler decides over a set of possible factory configurations to maximize expected production profit in the presence of varying demand curves. In contrast to these global approaches, a local ....
Jeff Schneider, Justin Boyan, and Andrew Moore. Value function based production scheduling. In International Conference on Machine Learning, 1998.
Distributed Value Functions - Schneider, Wong, Moore, Riedmiller (1999) (16 citations) Self-citation (Schneider Moore) (Correct)
....by the use of local state variables. All of the equations in our derivation have been written strictly in terms of the value function. At first glance, it might seem that this will lead to the use of a kind of value iteration coupled with the learning of system models to solve the problem ([10, 11], for example) This can t be done easily, though, because the use of local state means neighbors can t use state as a common language with which to communicate. It isn t possible for one node to ask another If I am in state x and choose action a, what state will that put you in, and what action ....
J. Schneider, J. Boyan, and A. Moore. Value function based production scheduling. In International Conference on Machine Learning, 1998.
Stochastic Reactive Production Scheduling by - Asynchronous (2005) (Correct)
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Schneider, J., Boyan, J., Moore, A.: Value Function Based Production Scheduling. Proceedings of the 15th International Conference on Machine Learning (1998)
A Reinforcement Learning Approach for Supply Chain Management - Stockheim, Schwind, Koenig (Correct)
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Schneider, J. G., Boyan, J. A. & Moore, A. W. (1998), Value function based production scheduling, in `Proceedings 15th International Conference on Machine Learning', Morgan Kaufmann, San Francisco, CA, pp. 522--530.
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