93 citations · 164 across the 4 of their papers we have counts for
4 papers
SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation
Avi Pfeffer, Daphne Koller, Brian Milch +1
In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling lan…
Probabilistic Models for Agents' Beliefs and Decisions
Brian Milch, Daphne Koller
Many applications of intelligent systems require reasoning about the mental states of agents in the domain. We may want to reason about an agent's beliefs, including beliefs about…
General-Purpose MCMC Inference over Relational Structures
Brian Milch, Stuart Russell
Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabili…
Learning Probabilistic Relational Dynamics for Multiple Tasks
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer +1
The ways in which an agent's actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of lea…