A Language for Relational Decision Theory

Aniruddh Nath and Pedro Domingos


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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