Game environments are becoming more and more mutable from the actions of both Players and Non Player Characters (NPCs). However, current generation AI agents do not take advantage of the tactical abilities these mutable worlds provide. We propose a method to make the game agents aware of the mutability of the world by extending their repertoire of abilities to include world alteration commands and some evaluation functions, which determine when and where to alter the world for the greatest tactical gain. Primarily, our work focuses on the Adversarial Navigation Mesh Alteration (ANMA) algorithm, which evaluates potential changes to the map in adversarial environments from an attacker and defender point of view. We present an empirical evaluation of the ANMA algorithm in a Capture The Flag (CTF) simulation environment with several teams of agents. One group of agents (adaptive) lacks the ability to initiate world deformations, but they can respond and re-plan to take advantage of world modifications. The second team of agents (builders) can only generate additional paths through the world using the attacker portion of ANMA. The third team of agents (universal) is able to fully deform the world by generating new paths or removing existing paths using both the attacker and defender sections of ANMA. We evaluated these teams and observed that builder agents beat adaptive agents at a rate of 1.33 to 1. The more advanced universal agents beat adaptive agents at a rate of 2.75 to 1 and builder agents 1.4 to 1.

We present a modified navigation mesh generation algorithm that allows the mesh to be dynamically altered at runtime. We accomplish this using an extension to the existing spatial decomposition algorithm ASFV (Adaptive Space Filling Volumes) that will allow the algorithm to dynamically adapt to changes to the underlying world geometry without having to rebuild the entire spatial decomposition. This is accomplished by providing two algorithms to deal with alterations to the world. The ability is provided to add arbitrary obstructions into what was negative space and then to build a new correct spatial decomposition around the new obstruction. Functionality is also provided to remove existing obstructions and then to build up new decompositions to fill in the newly created negative space. Finally, we show via an experiment that our dynamic extensions to ASFV reduces the cost of correcting an invalidated decomposition by 90% or more.