Ensemble Decision Making in Real-Time Games

Rodgers, Philip; Levine, John; Anderson, Damien

doi:10.1109/cig.2018.8490401

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Research into using multiple models to address complexity goes back decades. [25][26][27] Recent research specifically investigating ensemble methods for games include Rodgers et al 28 and Anderson et al 29 Although the authors reported positive outcomes in their first study, 28 in their second study 29 they found that, although an Ensemble Decision System (EDS) for General Video Game Playing (GVGP) retained the strengths of each individual algorithm and increases the generality of the agent, it did not manage to surpass the performance of its individual algorithms.…”

Section: Many Model Thinking and Ensemble Approachesmentioning

confidence: 99%

A multi-model approach to modeling intelligent combat behaviors for wargaming

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2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate the development of robust artificial intelligence (AI) for wargaming. In this study, we contribute to the research of AI methods to develop intelligent agent behaviors in combat simulations by investigating the effectiveness of a multi-model approach as compared to a single-model approach. To accomplish this, we first develop a multi-model framework that leverages supervised learning, reinforcement learning, and scripted behavior models. We then design and conduct an experiment where we compare the performance of each one of eight individual behavior models to four multi-models composed of varying numbers of these individual behavior models. Using a combat simulation with a random scenario generator, we find that a multi-model approach improved the mean score by 62.6% over the mean game score of the best-performing single-model. Additionally, we find that a multi-model with more embedded behavior models outperforms a multi-model with fewer behavior models. The outcomes of this study contribute to the broader research endeavors aimed at expanding AI capabilities to handle the intricate and expansive state-spaces characteristic of combat modeling and simulation.

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Section: Many Model Thinking and Ensemble Approachesmentioning

confidence: 99%

A multi-model approach to modeling intelligent combat behaviors for wargaming

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2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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“…Techniques that aim to predict the best choice from a collection of heuristics are commonly called hyper-heuristic approaches [3]. Such approaches have been proposed for different games and research frameworks, including the general video game AI (GVGAI) framework [4], [5], the general game playing (GGP) framework [6], Starcraft [7], Angry Birds [8], Pac-Man [9], Jawbreaker [10], FreeCell [11], and theoretical games [12].…”

Section: Introductionmentioning

confidence: 99%

General Game Heuristic Prediction Based on Ludeme Descriptions

Stephenson¹,

Soemers²,

Piette³

et al. 2021

Preprint

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“…Similarly, an Ensemble Decision System (EDS) has achieved a world record for an AI playing the game of Ms. Pacman. The EDS achieves this by allowing complex behaviour to emerge from the combining of simple algorithms that focus on specific tasks, such as collecting pills or dodging ghosts [11].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Decision Systems for General Video Game Playing

Anderson

Rodgers

Levine

et al. 2019

2019 IEEE Conference on Games (CoG)

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Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses, while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.

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Ensemble Decision Making in Real-Time Games

Cited by 4 publications

References 11 publications

A multi-model approach to modeling intelligent combat behaviors for wargaming

A multi-model approach to modeling intelligent combat behaviors for wargaming

General Game Heuristic Prediction Based on Ludeme Descriptions

Ensemble Decision Systems for General Video Game Playing

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