University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2018 Planning For Non-Player Characters By Learning From Demonstration John Drake University of Pennsylvania,
[email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computer Sciences Commons Recommended Citation Drake, John, "Planning For Non-Player Characters By Learning From Demonstration" (2018). Publicly Accessible Penn Dissertations. 2756. https://repository.upenn.edu/edissertations/2756 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2756 For more information, please contact
[email protected]. Planning For Non-Player Characters By Learning From Demonstration Abstract In video games, state of the art non-player character (NPC) behavior generation typically depends on hard-coding NPC actions. In many game situations however, it is hard to foresee how an NPC should behave to appear intelligent or to accommodate human preferences for NPC behavior. We advocate the creation of a more flexible method ot allow players (and developers) to train NPCs to execute novel behaviors which are not hard-coded. In particular, we investigate search-based planning approaches using demonstration to guide the search through high-dimensional spaces that represent the full state of the game. To this end, we developed the Training Graph heuristic, an extension of the Experience Graph heuristic, that guides a search smoothly and effectively even when a demonstration is unreachable in the search space, and ensures that more of the demonstrations are utilized to better train the NPC's behavior. To deal with variance in the initial conditions of such planning problems, we have developed heuristics in the Multi-Heuristic A* framework to adapt demonstration trace data to new problems.