PDDL+ planning with events and linear processes

Abstract

We present a scalable fully-automated forward-chaining planner capable of reasoning with PDDL+ events and linear processes. Processes and events model (respectively) continuous and discrete exogenous activity in the environment, occurring when certain conditions hold. We discuss the significant challenges posed in creating a planner that can reason with these, and present novel state-progression and consistency enforcing techniques that allow us to meet these challenges. We present results showing that our new planner, using Pddl+ models, is able to solve realistic expressive problems more efficiently than the state-of-the-art alternative: a compiled Pddl.2.1 representation with continuous numeric effects.