M. C. Horsch and D. Poole. "An anytime algorithm for decision making under uncertainty, " 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....would be no guarantee that the agent could get to that point. The outcome would be different again for a partial order planner. Because theses results would be almost useless when generating behavior for an agent, an HTN planner has been used as the basis for an anytime planner. Horsch et al. [46] presents an anytime algorithm for reasoning under uncertainty. He presents the algorithm with a maze solving agent with either perfect or noisy sensors and actuators. 8.6.1 Operator Selection Closely related to the inference mechanism is the task of operator selection. Burke et al. 16] ....

M. C. Horsch and D. Poole. "An anytime algorithm for decision making under uncertainty, " 1998.

....the assumption about full observability. It remains to show that their solution is efficient enough for real time use, however. One might have a similar concern for the anytime algorithm of Horsch and Poole for computing policies for decision problems represented as multi stage influence diagrams [22]. That said, the authors do report on valuable (typically non optimal) decision policies being found also for large problems, and treatments which take into account the cost of computation are otherwise rare (cf. also [5] 5 Conclusions and Further Research We have analysed artificial decision ....

M. C. Horsch & D. Poole: "An Anytime Algorithm for Decision Making under Uncertainty", Proc UAI98, forthcoming, July 1998.

....failures are. It might be that the techniques used are inherently slow, or it might only be that the particular implementations of the techniques have not been optimized for real time applications. In the former case we must look for alternatives. One alternative is to use anytime algorithms. In [ Horsch and Poole, 1998 ] an anytime algorithm for decision making under uncertainty is given which shows promising results for complex decision networks. Whether it is useful for smaller decision problems is not evident. Another anytime algorithm is mentioned in [ Boman, 1997 ] but no results are given. In the latter ....

Michael C. Horsch and David Poole. An anytime algorithm for decision making under uncertainty. In 14th Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, July 1998.

....address: Intelligent Systems Lab, School of Computing Science, Simon Fraser University, Burnaby, B.C. Canada V5A 1S6. Email: mhorsch cs.sfu.ca In this paper, we study a particular anytime algorithm for the problem of constructing policies for decision problems represented as influence diagrams [Horsch Poole, 1998; Horsch, 1998] This algorithm has a number of general features: the optimal solution is not known before it is computed; the current best solution is incrementally improved, although it is not known in advance how much improvement will be gained by a single computational step; the value of the ....

....Systems Lab, School of Computing Science, Simon Fraser University, Burnaby, B.C. Canada V5A 1S6. Email: mhorsch cs.sfu.ca In this paper, we study a particular anytime algorithm for the problem of constructing policies for decision problems represented as influence diagrams [Horsch Poole, 1998; Horsch, 1998] . This algorithm has a number of general features: the optimal solution is not known before it is computed; the current best solution is incrementally improved, although it is not known in advance how much improvement will be gained by a single computational step; the value of the current best ....

[Article contains additional citation context not shown here]

Horsch, M. C., and Poole, D. 1998. An anytime algorithm for decision making under uncertainty. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 246--255.

....address: Intelligent Systems Lab, School of Computing Science, Simon Fraser University, Burnaby, B.C. Canada V5A 1S6. Email: mhorsch cs.sfu.ca In this paper, we study a particular anytime algorithm for the problem of constructing policies for decision problems represented as influence diagrams [Horsch Poole, 1998; Horsch, 1998] This algorithm has a number of general features: the optimal solution is not known before it is computed; the current best solution is incrementally improved, although it is not known in advance how much improvement will be gained by a single computational step; the value of the ....

....Systems Lab, School of Computing Science, Simon Fraser University, Burnaby, B.C. Canada V5A 1S6. Email: mhorsch cs.sfu.ca In this paper, we study a particular anytime algorithm for the problem of constructing policies for decision problems represented as influence diagrams [Horsch Poole, 1998; Horsch, 1998] . This algorithm has a number of general features: the optimal solution is not known before it is computed; the current best solution is incrementally improved, although it is not known in advance how much improvement will be gained by a single computational step; the value of the current best ....

[Article contains additional citation context not shown here]

Horsch, M. C., and Poole, D. 1998. An anytime algorithm for decision making under uncertainty. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 246--255.

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Michael C. Horsch and David Poole. An anytime algorithm for decision making under uncertainty. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 246--255, 1998.

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