Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Optimal schedules for monitoring anytime algorithms
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Iterative state-space reduction for flexible computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Principles and applications of continual computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Decision making is the ability to decide on the best alternative among a set of candidates based on their value. In many real-world domains the value depends on events that occur dynamically, so that the decision is based on dynamically changing uncertain information. When there is a cost to waiting for more information, the question is when to make the decision. Do you stop and make the best decision you can, given the information you have so far, or do you wait until more information arrives so you can make a better decision? We propose a model that characterizes the influence of dynamic information on the utility of the decision. Based on this model, we present an optimal algorithm that guarantees the best time to stop. Unfortunately, its complexity is exponential in the number of candidates. We present an alternative framework in which the different candidates are solved separately. We formally analyze the alternative framework, and show how it leads to a range of specific heuristic algorithms. We evaluate the optimal and the simplest heuristic algorithms through experiments, and show that the heuristic algorithm is much faster than the optimal algorithm, and the utility of the winner it finds is close to the optimum.