Algorithmics: the spirit of computing
Algorithmics: the spirit of computing
Artificial Intelligence
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Dynamic Programming
Solving Time-Dependent Problems: A Decision-Theoretic Approach to Planning in Dynamic Environments
Solving Time-Dependent Problems: A Decision-Theoretic Approach to Planning in Dynamic Environments
Learning and Sequential Decision Making
Learning and Sequential Decision Making
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A Dynamic Scheduling Benchmark: Design, Implementation and Performance Evaluation
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Deliberation scheduling for time-critical sequential decision making
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Quantitative abstraction refinement
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
| Hi-index | 0.00 |
In previous work, we have advocated explicitly scheduling computation time for planning and problem solving (deliberation) using a framework called expectation-driven iterative refinement. Within this framework, we have explored the problem of allocating deliberation time when the procedures used for deliberation implement anytime algorithms: algorithms that return some answer for any allocation of time. In our search for useful techniques for constructing anytime algorithms, we have discovered that dynamic programming shows considerable promise for the construction of anytime algorithms for a wide variety of problems. In this paper, we show how dynamic programming techniques can be used to construct useful anytime procedures for two problems: multiplying sequences of matrices, and the Travelling Salesman Problem. Dynamic programming can be applied to a wide variety of optimization and control problems, many of them relevant to current AI research (e.g., scheduling, probabilistic reasoning, and controlling machinery). Being able to solve these kinds of problems using anytime procedures increases the range of problems to which expectation-driven iterative refinement can be applied.