Approximate Algorithms for the 0/1 Knapsack Problem
Journal of the ACM (JACM)
Stochastic dynamic programming with factored representations
Artificial Intelligence
Principles and applications of continual computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Continual Computation Policies for Allocating Offline and Real-Time Resources
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast approximation algorithms for knapsack problems
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Resonance on the web: web dynamics and revisitation patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
A note on maximizing a submodular set function subject to a knapsack constraint
Operations Research Letters
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
Autonomous agents that sense, reason, and act in real-world environments for extended periods often need to solve streams of incoming problems. Traditionally, effort is applied only to problems that have already arrived and have been noted. We examine continual computation methods that allow agents to ideally allocate time to solving current as well as potential future problems under uncertainty. We first review prior work on continual computation. Then, we present new directions and results, including the consideration of shared subtasks and multiple tasks. We present results on the computational complexity of the continual-computation problem and provide approximations for arbitrary models of computational performance. Finally, we review special formulations for addressing uncertainty about the best algorithm to apply, learning about performance, and considering costs associated with delayed use of results.