Name
Abstract | ||
|---|---|---|
In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations. model team-based strategies for Unreal Tournament® (UT) bots. We will present experiments with UT bots supporting this claim. The second one discusses an alternative to HTNs called TMK models. TMK modeled processes and are of interest for game AI because TMK models are used by TIELT to model AI behavior. TIELT is a project funded by DARPA to create a testbed for integrating machine learning algorithms with computer game engines. The goal of TIELT is to bridge decision systems and computer games, allowing researchers to more easily test novel algorithms in sophisticated games while at the same time demonstrating the potential practical utility of these algorithms to game developers. Our case study shows that TMK models are equally expressive as HTNs and, therefore, TMK models share the well-defined properties of HTNs. |
Year | Venue | Keywords |
|---|---|---|
2005 | AIIDE | behavior modeling,hierarchical task network,machine learning,game development,process model |
Field | DocType | Citations |
ENCODE,Computer science,Process modeling,Game design,Artificial intelligence,Syntax,Non-cooperative game,Machine learning,Encoding (memory) | Conference | 32 |
PageRank | References | Authors |
2.57 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai Hoang | 1 | 36 | 3.67 |
| Stephen Lee-Urban | 2 | 182 | 12.40 |
| Héctor Muñoz-Avila | 3 | 674 | 55.13 |