C4.5: programs for machine learning
C4.5: programs for machine learning
Theoretical Computer Science
Discrete-time battery models for system-level low-power design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Neuro-Dynamic Programming
Analysis of discharge techniques for multiple battery systems
Proceedings of the 2003 international symposium on Low power electronics and design
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
Practical solution techniques for first-order MDPs
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
Incremental plan aggregation for generating policies in MDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Scheduling battery usage in mobile systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Efficient use of multiple batteries is a practical problem with wide and growing application. The problem can be cast as a planning problem under uncertainty. We describe the approach we have adopted to modelling and solving this problem, seen as a Markov Decision Problem, building effective policies for battery switching in the face of stochastic load profiles. Our solution exploits and adapts several existing techniques: planning for deterministic mixed discrete-continuous problems and Monte Carlo sampling for policy learning. The paper describes the development of planning techniques to allow solution of the non-linear continuous dynamic models capturing the battery behaviours. This approach depends on carefully handled discretisation of the temporal dimension. The construction of policies is performed using a classification approach and this idea offers opportunities for wider exploitation in other problems. The approach and its generality are described in the paper. Application of the approach leads to construction of policies that, in simulation, significantly outperform those that are currently in use and the best published solutions to the battery management problem. We achieve solutions that achieve more than 99% efficiency in simulation compared with the theoretical limit and do so with far fewer battery switches than existing policies. Behaviour of physical batteries does not exactly match the simulated models for many reasons, so to confirm that our theoretical results can lead to real measured improvements in performance we also conduct and report experiments using a physical test system. These results demonstrate that we can obtain 5%-15% improvement in lifetimes in the case of a two battery system.