Abstract

We develop an approach to planning with incomplete information that is based on three elements: 1. a high-level language for describing the effects of actions on both the world and the agent's beliefs that we call pomdp theories 2. a semantics that translates such theories into actual pomdps 3. a real time dynamic programming algorithm that produces controllers from such pomdps. We show that the resulting approach is not only clean and general but that is practical as well. We have implemented a shell that accepts pomdp theories and produces controllers, and have tested it over a number of problems. In this paper we present the main elements of the approach and report results for the `omelette problem' where the resulting controller exhibits a better performance than the handcrafted controller. Introduction Consider an agent that has a large supply of eggs and whose goal is to get three good eggs and no bad ones into one of two bowls. The eggs can be either good or bad, and at any...

Citations