scholarly journals A Multiagent Potential Field-Based Bot for Real-Time Strategy Games

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Johan Hagelbäck ◽  
Stefan J. Johansson
Keyword(s):  
Real Time ◽  
Potential Field ◽  
Imperfect Information ◽  
State Of The Art ◽  
Performance Standards ◽  
Dynamic Environments ◽  
Potential Fields ◽  
Strategy Games ◽  
Strategy Game ◽  
Real Time Strategy Game

Bots for real-time strategy (RTS) games may be very challenging to implement. A bot controls a number of units that will have to navigate in a partially unknown environment, while at the same time avoid each other, search for enemies, and coordinate attacks to fight them down. Potential fields are a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a multiagent potential field-based bot architecture that is evaluated in two different real-time strategy game settings and compare them, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. We show that the solution is a highly configurable bot that can match the performance standards of traditional RTS bots. Furthermore, we show that our approach deals with Fog of War (imperfect information about the opponent units) surprisingly well. We also show that a multiagent potential field-based bot is highly competitive in a resource gathering scenario.

scholarly journals A Comparison of Evolutionary and Tree-Based Approaches for Game Feature Validation in Real-Time Strategy Games with a Novel Metric

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 688 ◽  
Author(s):  
Damijan Novak ◽  
Domen Verber ◽  
Jani Dugonik ◽  
Iztok Fister
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Optimal Path ◽  
Approximate Representation ◽  
Approximate Methods ◽  
Strategy Games ◽  
Game Features ◽  
Series Of Experiments ◽  
Strategy Game ◽  
Real Time Strategy Game

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate representation of the world in a tree-like structure, and then a search is conducted to find the optimal path inside that tree. In this paper, we propose a novel metric for game feature validation in Real-Time Strategy (RTS) games. Firstly, the identification and grouping of Real-Time Strategy game features is carried out, and, secondly, groups are included into weighted classes with regard to their correlation and importance. A novel metric is based on the groups, weighted classes, and how many times the playtesting agent invalidated the game feature in a given game feature scenario. The metric is used in a series of experiments involving recent state-of-the-art evolutionary and tree-based playtesting agents. The experiments revealed that there was no major difference between evolutionary-based and tree-based playtesting agents.

New Generation of Artificial Intelligence for Real-Time Strategy Games

2014 ◽  
pp. 220-229
Author(s):  
Damijan Novak ◽  
Domen Verber
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Computer Games ◽  
Game Development ◽  
Strategy Games ◽  
Artificial Intelligence Algorithm ◽  
Strategy Game ◽  
Real Time Strategy Game ◽  
Memory Resources ◽  
New Generation

Artificial intelligence in computer games is still well behind academic artificial intelligence research. The computer power and memory resources have increased exponentially over the last few years and improved game artificial intelligence should not hinder the performance of the game anymore. Improvements of game artificial intelligence are necessary because an appropriate artificial intelligence for the more advanced players does not exist today. This chapter discusses artificial intelligence for real-time strategy computer games, which are ideal test beds for research on movement, tactic, and strategy. Open-source real-time strategy game development tools are presented and compared, and an enhanced combat artificial intelligence algorithm is proposed.

scholarly journals Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

2020 ◽  
Author(s):  
Cong Fei ◽  
Bin Wang ◽  
Yuzheng Zhuang ◽  
Zongzhang Zhang ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
Data Augmentation ◽  
State Of The Art ◽  
Robot Learning ◽  
Imitation Learning ◽  
Proper Understanding ◽  
Learning Framework ◽  
Strategy Game ◽  
Real Time Strategy Game

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

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