CPT-L: An efficient model for relational stochastic processes

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.