Adapting the Markov Chain based Algorithm M-DBScan to Detect Opponents’ Strategy Changes in the Dynamic Scenario of a StarCraft Player Agent

Eldane Vieira Júnior, Rita Julia, Elaine Faria

2020

Abstract

Digital games represent an appropriate test scenario to investigate the agents’ ability to detect changes in the behavior of other agents trying to prevent them from fulfilling their objectives. Such ability allows the agents to adapt their decision-making process to adequately deal with those changes. The Markov Chain based algorithm M-DBScan is a successful tool conceived to detect novelties in data stream scenarios. Such algorithm has been validated in artificially controlled situations in games in which a single set of features and a single Markov Chain are sufficient to represent the data and to detect the occurrence of novelties, which usually is not enough to make the agents able to adequately perceive the environment changes in real game situations. The main contribution of the present work is then to investigate how to improve the use of M-DBScan as a tool for detecting behavior changes in the context of real and dynamic StarCraft games by using distinct sets of features and Markov Chains to represent the peculiarities of relevant game stages. Further, distinctly from the existing researches, here M-DBScan is validated in situations in which the timestamp, between successive novelties, is not constant.

Paper Citation

in Harvard Style

Vieira Júnior E., Julia R. and Faria E. (2020). Adapting the Markov Chain based Algorithm M-DBScan to Detect Opponents’ Strategy Changes in the Dynamic Scenario of a StarCraft Player Agent. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 214-223. DOI: 10.5220/0008985802140223

in Bibtex Style

@conference{icaart20,
author={Eldane Vieira Júnior and Rita Julia and Elaine Faria},
title={Adapting the Markov Chain based Algorithm M-DBScan to Detect Opponents’ Strategy Changes in the Dynamic Scenario of a StarCraft Player Agent},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={214-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008985802140223},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Adapting the Markov Chain based Algorithm M-DBScan to Detect Opponents’ Strategy Changes in the Dynamic Scenario of a StarCraft Player Agent
SN - 978-989-758-395-7
AU - Vieira Júnior E.
AU - Julia R.
AU - Faria E.
PY - 2020
SP - 214
EP - 223
DO - 10.5220/0008985802140223