Discrete-time battery models for system-level low-power design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Neuro-Dynamic Programming
VAL: Automatic Plan Validation, Continuous Effects and Mixed Initiative Planning Using PDDL
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Journal of Artificial Intelligence Research
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
Planning with durative actions in stochastic domains
Journal of Artificial Intelligence Research
A heuristic search approach to planning with continuous resources in stochastic domains
Journal of Artificial Intelligence Research
Using learned policies in heuristic-search planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Scheduling battery usage in mobile systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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There is a huge and growing number of systems that depend on batteries for power supply, ranging from small mobile devices to large high-powered systems such as electrical substations. In most of these systems, there are significant user-benefits or engineering reasons to base the supply on multiple batteries, with load being switched between batteries by a control system. The key to efficient use of multiple batteries lies in the design of effective policies for the management of the switching of load between them. This paper describes work in which we show that automated planning can produce much more effective policies than other approaches to multiple battery load management in the literature.