ABSTRACT Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. ALoc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.

We present a new approach to reasoning about planning strategies in multiagent domains: an agent learns a boundedly-optimal planning strategy, relative to the current state of the environment and its goals, using a myopic attitude based on its repertoire of planning policies. In self-play, our approach also allows boundedly-optimal coordination. In terms of the exploration-exploitation tradeoff, the agent need not generate a model of its environment (nor of other domain agents) as part of its exploratory activities, which allows efficient exploration as well as tractability in real-world scenarios. As complement to the above approach, we present the Leap-and-Stride strategy, a novel probabilistic strategy that serves agents in situations where they can make no assumptions about their surroundings, either due to the lack of a priori knowledge about the environment, or because its complex and dynamic nature renders it inherently unpredictable. The Leap-and-Stride strategy interleaves exploratory activities into the agent’s planning logic, which enables it to converge to effective modes of interaction with its environment through trial-and-error. This strategy is tunable in the sense that the agent can control the intensity of its exploratory activities according to a risk management

ABSTRACT In any single agent system, exploration is a critical component of learning. It ensures that all possible actions receive some degree of attention, allowing an agent to converge to good policies. The same concept has been adopted by multiagent learning systems. However, there is a fundamentally different dynamic in multiagent learning: each agent operates in a non-stationary environment, as a direct result of the evolving policies of other agents in the system. As such, exploratory actions taken by agents bias the policies of other agents, forcing them to perform optimally in the presence of agent exploration. CLEAN rewards address this issue by privatizing exploration (agents take their best action, but internally compute rewards for counterfactual actions). However, CLEAN rewards require each agent to know the mathematical form of the system evaluation function, which is typically unavailable to agents. In this paper, we present an algorithm to approximate CLEAN rewards, eliminating exploratory action noise without the need for expert system knowledge. Results in both coordination and congestion domains demonstrate the approximated CLEAN rewards obtain up to 95% of the performance of directly computed CLEAN rewards, without the need for expert domain knowledge while utilizing 99% less system information.