Hypothesizing Device Mechanisms: Opening Up the Black Box

I describe an approach to forming hypotheses about hidden mechanism configurations within devices given external observations and a vocabulary of primitive mechanisms. An implemented causal modelling system called JACK constructs explanations for why a second piece of toast comes out lighter, why the slide in a tire gauge does not slip back inside when the gauge is removed from the tire, and how in a refrigerator a single substance can serve as a heat sink for the interior and a heat source for the exterior. I report the number of hypotheses admitted for each device example, and provide empirical results which isolate the pruning power due to different constraint sources.

A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.