Probability theory provides a sound basis for reasoning under uncertainty.
We can represent an agent's uncertainty about the world using a probability model, and the agent can use the model to draw likely conclusions from its observations. Daphne Koller and I, together with several other colleagues, have developed a powerful new language for representing probability models of rich, complex systems. We have also developed efficient algorithms for reasoning with these models. Our language is based on Bayesian networks, but is much more expressive. It allows the model designer to describe a complex system in terms of objects and the relationships between them, and to describe how properties of an object depend probabilistically on properties of related objects.