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Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are… (More)

- Stuart J. Russell, Peter Norvig
- Prentice Hall series in artificial intelligence
- 2003

- Andrew Y. Ng, Stuart J. Russell
- ICML
- 2000

- Andrew Y. Ng, Daishi Harada, Stuart J. Russell
- ICML
- 1999

This paper investigates conditions under which modi cations to the reward function of a Markov decision process preserve the op timal policy It is shown that besides the positive linear transformation familiar from utility theory one can add a reward for tran sitions between states that is expressible as the di erence in value of an arbitrary poten tial… (More)

This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can… (More)

- Nir Friedman, Kevin P. Murphy, Stuart J. Russell
- UAI
- 1998

Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two… (More)

- Ronald E. Parr, Stuart J. Russell
- NIPS
- 1997

We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can be transferred across problems and in which component solutions can… (More)

- Nir Friedman, Stuart J. Russell
- UAI
- 1997

"Background subtraction" is an old technique for finding moving objects in a video sequence-for example, cars driving on a freeway. The idea is that subtracting the current image from a time averaged background image will leave only non stationary objects. It is, however, a crude ap proximation to the task of classifying each pixel of the current image;… (More)

- John Binder, Daphne Koller, Stuart J. Russell, Keiji Kanazawa
- Machine Learning
- 1997

Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are used widely for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This… (More)