Title

Exploiting Key Events For Learning Interception Policies

Abstract

One scenario that commonly arises in computer games and military training simulations is predator-prey pursuit in which the goal of the non-player character agent is to successfully intercept a fleeing player. In this paper, we focus on a variant of the problem in which the agent does not have perfect information about the player's location but has prior experience in combating the player. Effectively addressing this problem requires a combination of learning the opponent's tactics while planning an interception strategy. Although for small maps, solving the problem with standard POMDP (Partially Observable Markov Decision Process) solvers is feasible, increasing the search area renders many standard techniques intractable due to the increase in the belief state size and required plan length. Here we introduce a new approach for solving the problem on large maps that exploits key events, high reward regions in the belief state discovered at the higher level of abstraction, to plan efficiently over the low-level map. We demonstrate that our hierarchical key-events planner can learn intercept policies from traces of previous pursuits significantly faster than a standard point-based POMDP solver, particularly as the maps scale in size. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

40-45

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889847862 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889847862

This document is currently not available here.

Share

COinS