Relational sequential inference with reliable observations

ABSTRACT

We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. Our method assumes "reliable observations", i.e. that each process state persists long enough to be reliably inferred from the observations it generates. We introduce the idea of a "state-inference function" (from observation sequences to underlying hidden states) for representing knowledge about a process and develop an efficient sequential-inference algorithm, utilizing this function, that is correct for processes that generate reliable observations consistent with the state-inference function. We describe a representation for state-inference functions in relational domains and give a corresponding supervised learning algorithm. Experiments, in relational video interpretation, show that our technique provides significantly improved accuracy and speed relative to a variety of recent, hand-coded, non-trainable systems.

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