Professor Zilberstein's research in artificial intelligence is focused on the computational foundations of automated reasoning and action. He studies the implications of uncertainty and limited computational resources on the design of autonomous agents. In most practical settings, it is not feasible or desirable to find optimal plans or actions, making it necessarily to resort to some form of approximate reasoning. This raises a simple fundamental question: what does it mean for an agent to be "rational" when it does not have enough knowledge or computational power to derive the best course of action? Professor Zilberstein's approach to this problem is based on probabilistic reasoning and decision-theoretic principles, used both to develop planning algorithms and to monitor their execution and maximize the value of computation. The meta-level control mechanisms reason explicitly about the cost of decision-making and can optimize the amount of deliberation (or "thinking") an agent does before taking action. This research spans both theoretical issues and the development of effective algorithms and applications. Zilberstein's most recent work has produced new models and algorithms to tackle these challenges in situations involving multiple decision makers operating in either collaborative or adversarial domains.