Reasoning about capabilities in multi-agent systems is crucial for many applications. There are two aspects of reasoning about the capabilities of an agent to achieve its goals. O...
Among the many techniques for system-level power management, it is not currently possible to guarantee timing constraints and have a comprehensive system model at the same time. S...
The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling ...
The polyhedral model provides powerful abstractions to optimize loop nests with regular accesses. Affine transformations in this model capture a complex sequence of execution-reord...
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...