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2008

A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC

8 years 7 months ago
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC
Many real-world problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms proceed by first fully instantiating the first-order theory and then working at the propositional level. The applicability of such approaches is severely limited by the exponential time and memory cost of propositionalization. Singla and Domingos (2006) addressed this by developing a "lazy" version of the WalkSAT algorithm, which grounds atoms and clauses only as needed. In this paper we generalize their ideas to a much broader class of algorithms, including other types of SAT solvers and probabilistic inference methods like MCMC. Lazy inference is potentially applicable whenever variables and functions have default values (i.e., a value that is much more frequent than the others). In relational domains, the default is false for atoms and true for clauses. We illustrate our framework by applying it ...
Hoifung Poon, Pedro Domingos, Marc Sumner
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Hoifung Poon, Pedro Domingos, Marc Sumner
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