TY - GEN
T1 - A Study on Behavioural Agents for StarCraft 2
AU - Williams, Ivan
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - With the recent trend of artificial intelligence, specifically within machine learning, there are some powerful tools that can be utilized to create video game artificial intelligence bots. Bots that can beat professional players or immerse players within the game to the point where enemies are considered intelligent and react to situations similar to how a real human would. However, some of these processes and tasks to create a bot can be an expensive and time-consuming process. In this research paper, we look at two models to building an AI bot and comparing the two, namely a simple reflex model and a recurrent neural network model. From the results, we can see that the recurrent neural network goes further into the tech tree and is able to produce a more complexed set of units as compared to the simple reflex solution. The simple reflex solution, however, is able to reach the win condition by defeating the enemy bot much quicker than the recurrent neural network solution at 5 min and 39 s and costs less in terms of production and complexity. The recurrent neural network solution was also able to get a higher food supply count and spent the most amount of resources in all areas including technology, economy and army supply.
AB - With the recent trend of artificial intelligence, specifically within machine learning, there are some powerful tools that can be utilized to create video game artificial intelligence bots. Bots that can beat professional players or immerse players within the game to the point where enemies are considered intelligent and react to situations similar to how a real human would. However, some of these processes and tasks to create a bot can be an expensive and time-consuming process. In this research paper, we look at two models to building an AI bot and comparing the two, namely a simple reflex model and a recurrent neural network model. From the results, we can see that the recurrent neural network goes further into the tech tree and is able to produce a more complexed set of units as compared to the simple reflex solution. The simple reflex solution, however, is able to reach the win condition by defeating the enemy bot much quicker than the recurrent neural network solution at 5 min and 39 s and costs less in terms of production and complexity. The recurrent neural network solution was also able to get a higher food supply count and spent the most amount of resources in all areas including technology, economy and army supply.
KW - Artificial intelligence
KW - DeepMind
KW - Game agents
KW - Heuristics
KW - Neural networks
KW - Reinforced learning
KW - StarCraft
UR - http://www.scopus.com/inward/record.url?scp=85096419431&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-5856-6_47
DO - 10.1007/978-981-15-5856-6_47
M3 - Conference contribution
AN - SCOPUS:85096419431
SN - 9789811558559
T3 - Advances in Intelligent Systems and Computing
SP - 479
EP - 489
BT - Proceedings of 5th International Congress on Information and Communication Technology, ICICT 2020
A2 - Yang, Xin-She
A2 - Sherratt, R Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Congress on Information and Communication Technology, ICICT 2020
Y2 - 20 February 2020 through 21 February 2020
ER -