Cases and Clusters in Reuse Policies for Decision-Making in Card Games

Paulus, Gustavo Bathu; Assunção, Joaquim; Silva, Luís A. L.

doi:10.1109/ictai.2019.00190

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…However, any agent modeled with such structures has a problem not approached in any know work [Paulus et al 2019, Moral et al 2020, Vargas et al 2021, Rossato et al 2020; as the game rolls the personality of the player or the approach of the agent, can be learned. Such a task is easy for humans but not so for AI.…”

Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A few works used Case-Based Reasoning (CBR) [Richter and Weber 2013] as an AI framework to solve problems in the game of Truco [Paulus et al 2019, Moral et al 2020, Vargas et al 2021, such approach relies on creating a database of cases for the game, latter exploring the data with different approaches. Specifically, [Paulus et al 2019] did a major comparison between shallow learning approaches, [Vargas et al 2021] built a database focusing on bluff and [Rossato et al 2020], without using CBR, built a lightweight Markov model using the natural force of all possible hands. This work focuses on improving the model described in [Rossato et al 2020] by using an HMM instead of a Markov chain (see Section 3), and further enhancing the model (hybrid) with a time tracking parameter aiming to store bluffs and reuse it in a future turn (Section 4) similar to [Vargas et al 2021], except the problem is solved within the model instead of creating a database for cases.…”

Section: Introductionmentioning

confidence: 99%

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Towards a time-aware HMM for the Truco game

Assunção

Paulus²

2022

Anais Estendidos Do XXI Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2022)

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Similar to Poker, the game of Truco has challenges for Artificial intelligence. Considering a large number of game states, a scenario characterized by partial visibility, stochastic behavior, and score susceptible to bluff; this game offers a good set of rules to test and improve AI techniques. In this article, we describe the creation of a Hidden Markov Model (HMM) agent using temporal control. The model has an embedded vector that adjusts its probabilities for further game actions, consequently, improving the model playing performance. The evaluation is given with over 210,000 matches, serving as empirical proof of the idea.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a time-aware HMM for the Truco game

Assunção

Paulus²

2022

Anais Estendidos Do XXI Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2022)

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“…When card designers need to have their design plans evaluated, they are not looking forward to a simple right or wrong judgment, but to reflect the performance of the design plan on the numerical level from the naturally objective perspective of mathematics itself. This can further improve the designer's comprehension of the difference between one's own subjective new card design and the existing mathematical model of this particular game (Paulus, 2019).…”

Section: Introductionmentioning

confidence: 99%

Balanced CCG design based on mathematical model

Dong

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With collectible card games (CCG 1 )* such as Magic: The Gathering being released in 1993, new mechanics were introduced into card game design and quickly developed into a genre with a broad audience. Importantly, numerical balance issues within CCG design have become a challenge for both enthusiasts and researchers. This thesis explores the possibility of achieving a balance of card values within CCG through a mathematical model and an effective probability evaluation mechanism. This study will use play-testing to find useful patterns and formulas from the underlying mechanics of Hearthstone, Shadowverse, Magic:The Gathering, which are all iconic turn-based collectible card games in the current market.Two play-testers complete 16 plus rounds of the game and card customization process. The win rate of each deck is collected as measures of numerical balance within card design achieved by the new evaluation mechanic. The data log will record the comprehensive winning rate of each deck during the playtest. The Wilcoxon signed-rank test using R-Studio will be applied to explore the difference between the original winning rate and winning rate after being mathematically adjusted. Through the analysis of qualitative and quantitative data in game testing and reasonable inferences, the author judges the practicability and limitations of the current mathematical model, along with the future development prospects of this model being visualized.

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