Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Techniques for plan recogmtwn under uncer­ tainty require a stochastic model of the plan­ generation process. We introduce probabilistic state-dependent grammars (PSDGs) to represent an agent's plan-generation process. The PSDG language model extends probabilistic context­ free grammars (PCFGs) by allowing production probabilities to depend on an explicit model of the planning agent's internal and external state. Given a PSDG description of the plan-generation process, we can then use inference algorithms that exploit the particular independence proper­ ties of the PSDG language to efficiently answer plan-recognition queries. The combination of the PSDG language model and inference algorithms extends the range of plan-recognition domains for which practical probabilistic inference is pos­ sible, as illustrated by applications in traffic mon­ itoring and air combat.

Probabilistic State-Dependent Grammars for Plan Recognition