Plan Learning Across Textual Observations (PLATO)

In Phase I of PLATO (Plan Learning Across Textual Observations ), Language Computer Corporation will explore how best to migrate state-of-the-art plan recognition techniques to the more complex genre of textual data addressing such issues as varied textual inputs, diverse subject matter domains, changes in agent plans and goals, unclear or underspecified temporal relationships between actions, and action sequences which are simultaneously uncertain, noisy, and lossy. Specifically, we will demonstrate (1) how to extract plan actions from complex speech acts and task-oriented dialogues; (2) how to automatically produce plan and action resources for detecting plans and goals across multiple domains; (3) how to apply abductive inference to drive robust plan identification over textual data; and (4) how to identify agent progress towards or away from a goal, using state-of-the-art, multi-layered inference and a rich model of temporal reasoning.