A Language for Relational Decision Theory
Aniruddh Nath and Pedro Domingos
Abstract:
In recent years, many representations have
been proposed that combine graphical models
with aspects of first-order logic, along
with learning and inference algorithms for
them. However, the problem of extending
decision theory to these representations remains
largely unaddressed. In this paper, we
propose a framework for relational decision
theory based on Markov logic, which treats
weighted first-order clauses as templates for
features of Markov networks. By allowing
clauses to have utility weights as well as probability
weights, very rich utility functions can
be represented. In particular, both classical
planning and Markov decision processes are
special cases of this framework.
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