Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Optimal schedules for monitoring anytime algorithms
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
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Crawling the web: discovery and maintenance of large-scale web data
Crawling the web: discovery and maintenance of large-scale web data
Effective page refresh policies for Web crawlers
ACM Transactions on Database Systems (TODS)
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
WIC: a general-purpose algorithm for monitoring web information sources
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Light at the end of the tunnel: a Monte Carlo approach to computing value of information
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Look versus leap: computing value of information with high-dimensional streaming evidence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In many real-world situations we are charged with detecting change as soon as possible. Important examples include detecting medical conditions, detecting security breaches, and updating caches of distributed databases. In those situations, sensing can be expensive, but it is also important to detect change in a timely manner. In this paper we present tractable greedy algorithms and prove that they solve this decision problem either optimally or approximate the optimal solution in many cases. Our problem model is a POMDP that includes a cost for sensing, a cost for delayed detection, a reward for successful detection, and no-cost partial observations. Making optimal decisions is difficult in general. We show that our tractable greedy approach finds optimal policies for sensing both a single variable and multiple correlated variables. Further, we provide approximations for the optimal solution to multiple hidden or observed variables per step. Our algorithms outperform previous algorithms in experiments over simulated data and live Wikipedia WWW pages.