Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Online computation and competitive analysis
Online computation and competitive analysis
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Towards Stochastic Constraint Programming: A Study of Online Multi-choice Knapsack with Deadlines
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Regrets only! online stochastic optimization under time constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Dynamic vehicle routing with stochastic requests
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real-Time decision making for large POMDPs
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Online stochastic reservation systems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
On complexity of multistage stochastic programs
Operations Research Letters
Proceedings of the 9th annual conference on Genetic and evolutionary computation
R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a Stochastic Resource Allocation Problem
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Probabilistic planning via determinization in hindsight
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Online stochastic optimization in the large: application to kidney exchange
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Gap reduction techniques for online stochastic project scheduling
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Amsaa: a multistep anticipatory algorithm for online stochastic combinatorial optimization
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
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Despite significant algorithmic advances in recent years, finding optimal policies for large-scale, multistage stochastic combinatorial optimization problems remains far beyond the reach of existing methods. This paper studies a complementary approach, online anticipatory algorithms, that make decisions at each step by solving the anticipatory relaxation for a polynomial number of scenarios. Online anticipatory algorithms have exhibited surprisingly good results on a variety of applications and this paper aims at understanding their success. In particular, the paper derives sufficient conditions under which online anticipatory algorithms achieve good expected utility and studies the various types of errors arising in the algorithms including the anticipativity and sampling errors. The sampling error is shown to be negligible with a logarithmic number of scenarios. The anticipativity error is harder to bound and is shown to be low, both theoretically and experimentally, for the existing applications.