SAS Planning As Topological Sorting

Guillaume Prevost, Stéphane Cardon, Tristan Cazenave, Christophe GUETTIER, Eric Jacopin

Abstract:

We address the problem of scaling the generation of plans in real-time to control the behaviors of several millions of Non-Player Characters (NPCs) in video-games and virtual worlds.
Search-based action planning, introduced in the game F.E.A.R. in 2005, has an exponential time complexity managing
at most several tens of NPCs per frame. A close study of the plans generated in first-person shooters shows that: (1) states are vectors of enumerated values, (2) both initial and final states can be totally defined, (3) operators are both post-unique and unary, (4) plans are totally ordered, and (5) operators occur only once in plans. (1) to (3) satisfy the Simplified Action Structure (SAS) polynomial time planning framework but neither (4) nor (5): we thus present two new restrictions to the SAS planning framework and a new linear-time algorithm which dramatically outperforms previous SAS planners and indeed allow us to consider the management of millions of NPCs per frame.