CPT-L: an Efficient Model for Relational Stochastic Processes
نویسندگان
چکیده
Agents that learn and act in real-world environments have to cope with both complex state descriptions and non-deterministic transition behavior of the world. Standard statistical relational learning techniques can capture this complexity, but are often inefficient. We present a simple probabilistic model for such environments based on CP-Logic. Efficiency is maintained by restriction to a fully observable setting and the use of efficient inference algorithms based on binary decision diagrams.
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