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Merging ai and or to solve high-dimensional stochastic optimization problems using approximate dynamic programming.
| Content Provider | CiteSeerX |
|---|---|
| Author | Powell, Warren B. |
| Abstract | We consider the problem of optimizing over time hundreds or thousands of discrete entities that may be characterized by relatively complex attributes, in the presence of different forms of uncertainty. Such problems arise in a range of operational settings such as transportation and logistics, where the entities may be aircraft, locomotives, containers or people. These problems can be formulated using dynamic programming, but encounter the widely cited “curse of dimensionality. ” Even deterministic formulations of these problems can produce math programs with millions of rows, far beyond anything being solved today. This paper shows how we can combine concepts from artificial intelligence and operations research to produce practical solution methods that scale to industrial-strength problems. Throughout, we emphasize concepts, techniques and notation from artificial intelligence and operations research to show how the fields can be brought together for complex stochastic, dynamic problems. Consider the problem of training a computer to play a game such as backgammon. The computer has to recognize the state of the board, evaluate a number of choices and determine which move will put the player into the best position. The logic has to consider the decisions of the opponent and the roll of the dice. To determine the best decision, the computer has to compute the value of the state resulting from the decision. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
