Plan Learning Across Textual Observations (PLATO) Phase II

In Phase II of PLATO, we will develop a prototype plan and goal identification system that employs a rich model of domain actions and events, extracted automatically from a massive amount of real-world domain-relevant information, to model agent-specific action costs and to process sequences of agent actions and events, extracted from text, as an agent plan in progress. The system will be able to (1) model the inferential knowledge of preconditions, effects, and quantities associated with domain actions and events, derived from web-scale textual resources; (2) extract and structure domain-relevant agent actions and states from text for incorporation into an explorable knowledge base; (3) model action costs for a particular agent in a variety of dimensions including financial cost, legal and moral constraints, and educational requirements; (4) perform goal identification over agent event sequences by estimating agent distance to a goal through state-of-the-art planning; and (5) provide visualization and exploratory methods for users to identify agent capabilities, likely next steps, and high-level steps within an agent plan.