An Artificial Intelligence System to Help the Player of Real-Time Strategy Games

Cunha, Renato Luiz de Freitas; Chaimowicz, Luiz

doi:10.1109/sbgames.2010.23

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Cunha and Chaimowicz in [5], for example, developed an advice system to RTS players that implemented a predictor of the technological level of an agent based on its units. It was done in the game Wargus modeling a reverse path in the dependency tree of the game, deriving what are the technologies known by the enemy from the observation of existent buildings and units.…”

Section: Player Modeling Taxonomymentioning

confidence: 99%

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Player modeling: Towards a common taxonomy

Machado

Fantini

Chaimowicz

2011

2011 16th International Conference on Computer Games (CGAMES)

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Abstract-Artificial Intelligence (AI) is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, Player Modeling is becoming an important one. The main idea is to try to understand and model the player characteristics and behaviors in order to develop a better AI. This paper presents a survey of the field, discussing the main concepts and proposing a taxonomy to better organize them. We also present several game platforms that can be used by player modeling and AI researchers. We believe that compiling this information can be important to the field, specially to new researchers.

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Section: Player Modeling Taxonomymentioning

confidence: 99%

“…It is an open source project organized in two levels: the core, with its basic game functions (engine Stratagus) done in C++ and a higher level, the game logic, implemented in Lua. Two of the many papers that used Wargus are: [5], [32].…”

Section: G Strategy Games (Rts and Tbs)mentioning

confidence: 99%

Player modeling: Towards a common taxonomy

Machado

Fantini

Chaimowicz

2011

2011 16th International Conference on Computer Games (CGAMES)

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“…The primary task of the gameplaying agent is to win games, and the secondary task is to win them with a higher score [28]. For the RTS gameplaying agent [29] to be able to cope with the high computational complexity of the game space, it has to be able to function inside different segments of the game, which are as follows: resource and production management (also categorized as economy) [30], strategical [31], tactical [32] and micromanagement [33] operations, scouting [34] and sometimes even diplomacy [35]. For one to be successful when playing an RTS game, a balanced combination of all those segments must be considered by the agent [36].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Tavares

Azpúrua

Chaimowicz

2014

2014 Brazilian Symposium on Computer Games and Digital Entertainment

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Real time strategy games are complex scenarios where multiple agents must be coordinated in a dynamic, partially observable environment. In this work, we model the coordination of these agents as a task allocation problem, in which specific tasks are given to the agents that are more suited to execute them. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. To evaluate this approach, we implement this coordination mechanism in the AI of a popular video game: StarCraft: BroodWar. Experiment results show that the genetic algorithm enhances performance of the task allocation algorithm. Besides, performance of the proposed approach in matches against StarCraft's native AI is comparable to that of a tournament-level software-controlled player for StarCraft.

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An Artificial Intelligence System to Help the Player of Real-Time Strategy Games

Cited by 4 publications

References 6 publications

Player modeling: Towards a common taxonomy

Player modeling: Towards a common taxonomy

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

Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

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