Corpus ID: 8447872

Stratagus : An Open-Source Game Engine for Research in Real-Time Strategy Games

@inproceedings{Ponsen2005StratagusA,
  title={Stratagus : An Open-Source Game Engine for Research in Real-Time Strategy Games},
  author={Marc J. V. Ponsen and Stephen Lee-Urban and Hector Mu{\~n}oz-Avila and David W. Aha and Matthew Molineaux},
  year={2005}
}
This paper advocates using Stratagus, an open-source realtime strategy game engine, in artificial intelligence research on real-time strategy games. We also describe its integration with TIELT, a testbed for integrating and evaluating decision systems with game engines. Together they can be used to study approaches for reasoning, representation, and learning in computer games. 

Figures and Tables from this paper

Building Human-Level AI for Real-Time Strategy Games
TLDR
The need for integrating heterogeneous approaches is motivated by enumerating a range of competencies involved in gameplay and how they are being implemented in EISBot, a reactive planning agent that has been applied to the task of playing real-time strategy games at the same granularity as humans. Expand
An Artificial Intelligence System to Help the Player of Real-Time Strategy Games
TLDR
An Artificial Intelligence system that helps the player during the game, giving him tactical and strategical tips about the best actions to be taken according to the current game state with the objective of improving the player's performance is proposed and developed. Expand
STRATEGA: A General Strategy Games Framework
TLDR
This paper motivates and presents STRATEGA a general strategy games framework for playing n-player turn-based and real-time strategy games, and presents some sample rule-based agents as well as searchbased agents and quantitatively analyse their performance to demonstrate the use of the framework. Expand
Case-based reasoning for improved micromanagement in Real-time strategy games
TLDR
By managing to beat a hard-coded computer opponent, this work concludes that the approach can be used to aid human players against computer opponents and increase the quality of the micromanagement of a computer player. Expand
Implementation of a deterministic video game agent testing environment
TLDR
The article describes ways to implement determinism in an AI testing environment, vastly decreasing the required number of individual agent training sessions. Expand
Mammoth: a massively multiplayer game research framework
TLDR
This paper focuses on the Mammoth architecture, demonstrating how good design practices can be used to create a modular framework where researchers from different research domains can conduct their experiments. Expand
Defeating Novel Opponents in a Real-Time Strategy Game
The Case-Based Tactician (CAT) system, created by Aha, Molineaux, and Ponsen (2005), uses case-based reasoning to learn to win the real-time strategy game Wargus. Previous work has shown CAT’sExpand
A mixed-paradigm component architecture for implementing web-based game servers
TLDR
The architecture of a web-based game server that was developed to support the teaching of artificial intelligence at the university level as well as research in the domain of AI and rule-based language development is outlined. Expand
Towards a Constructionist Serious Game Engine
TLDR
A game engine architecture to support the affordable development of constructionist games is proposed and a serious game built upon this specification and used by 96 students is presented in this paper. Expand
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
TLDR
The Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games is introduced and it is shown that Deep R TS lives up to its promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware. Expand
...
1
2
3
...

References

SHOWING 1-10 OF 21 REFERENCES
Real-Time Strategy Games: A New AI Research Challenge
TLDR
The current status of a project whose goals are to implement an RTS game programming environment and to build AIs that eventually can outperform human experts in this challenging and popular domain is described. Expand
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
TLDR
A plan retrieval algorithm is introduced that, by using three key sources of domain knowledge, removes the assumption of a static opponent and significantly outperforms the best among a set of genetically evolved plans when tested against random Wargus opponents. Expand
Automatically Acquiring Domain Knowledge For Adaptive Game AI Using Evolutionary Learning
TLDR
AKADS is introduced; it uses an evolutionary algorithm to automatically generate high-quality domain knowledge (i.e., tactics) for strong adaptive AI opponents in RTS games, reducing the time and effort required by game developers to create intelligent game AI, thus freeing them to focus on other important topics. Expand
Integrating Learning in Interactive Gaming Simulators
TLDR
This work describes the initial work on a testbed, named TIELT, that is designed to facilitate learning or learned behaviors in systems and the efforts to evaluate their learning systems in these simulators. Expand
Bootstrapping the Learning Process for the Semi-automated Design of a Challenging Game AI
This paper proposes a methodology for the semi-automated design of a game AI for simulation and strategy games which require the player to control a potentially high number of characters or units inExpand
Improving Adaptive Game Ai with Evolutionary Learning
TLDR
The empirical validation shows that the revised rulebase yields a significantly improved performance of dynamic scripting compared to the original rulebase, and concludes that offline evolutionary learning can be used to improve the performance of adaptive game AI. Expand
On-line Adaptation of Game Opponent AI with Dynamic Scripting
TLDR
It is concluded that dynamic scripting can be successfully applied to the online adaptation of computer game opponent AI and is proposed to be a novel technique called "dynamic scripting" that meets these requirements. Expand
Human-Level AI's Killer Application: Interactive Computer Games
TLDR
It is suggested that interactive computer games provide a rich environment for incremental research on human-level AI and the research issues and AI techniques that are relevant to each of these roles. Expand
Stratagus-playing Agents in Concurrent ALisp
We describe Concurrent ALisp, a language that allows the augmentation of reinforcement learning algorithms with prior knowledge about the structure of policies, and show by example how it can be usedExpand
A Gamut of Games
TLDR
The past successes, current projects, and future research directions for AI using computer games as a research test bed are reviewed. Expand
...
1
2
3
...