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This paper has been submitted and is under review. Please do not cite or distribute. 

Anytime algorithms enable intelligent systems to trade computation time with solution quality. To exploit this crucial ability in real-time decision-making, the system must decide when to interrupt the anytime algorithm and act on the current solution. Existing meta-level control techniques, however, address this problem by relying on significant offline work that diminishes their practical utility and accuracy. We formally introduce an online performance prediction framework that enables meta-level control to adapt to each instance of a problem without any preprocessing. Using this framework, we then present a meta-level control technique and two stopping conditions. Finally, we show that our approach outperforms existing techniques that require substantial offline work. The result is efficient nonmyopic meta-level control that reduces the overhead and increases the benefits of using anytime algorithms in intelligent systems.

 

In computational approaches to bounded rationality, metareasoning enables intelligent agents to optimize their own decision-making process in order to produce effective action in a timely manner. While there have been substantial efforts to develop effective meta-level control for anytime algorithms, existing techniques rely on extensive offline work, imposing several critical assumptions that diminish their effectiveness and limit their practical utility in the real world. In order to eliminate these assumptions, adaptive metareasoning enables intelligent agents to adapt to each individual instance of the problem at hand without the need for significant offline preprocessing. Building on our recent work, we first introduce a model-free approach to meta-level control based on reinforcement learning. We then present a meta-level control technique that uses temporal difference learning. Finally, we show empirically that our approach is effective on a common benchmark in meta-level control. 

Malware detection and response is critical to ensuring information security across a wide range of devices. There have been few attempts, however, to develop security systems that exploit the benefits of different malware detection techniques. We formally introduce an automated malware defense framework and represent it as a belief-space planning problem that optimally reduces the impact on the performance of a system. Using the framework, we then provide an example automated malware defense system for email worm detection and response. Finally, we show in simulation that the system outperforms standard security techniques that have been used in practice. The result is a novel belief-space planning approach to auto- mated malware defense designed for robust, accurate, and efficient use in large networks of resource-constrained devices.