Understanding Player Preferences in Card Games

Preference Learning is about figuring out what people like based on their choices. In games, especially card games like Magic: The Gathering, players must choose cards from a limited selection. These choices reflect their preferences, which can be influenced by many factors. For instance, what card is best can depend on the other cards a player already has. Learning these preferences can help create better AI opponents or even tools that assist players in making their decisions.

#The Problem with Contextual Preferences

When studying how players select cards, we often face a challenge. Typically, a player will choose one card from a group of many options. This setup creates a skewed view of preferences because the chosen card is the focus while the rest may not get enough attention. If we're trying to build a system that understands these preferences, we need to find a better way to compare these choices and not just rely on the single selected card.

#Using CLIP for Better Comparisons

CLIP is a popular tool that uses a method called contrastive learning. It helps link images and text by figuring out which items go together. However, using it for our needs in card games isn't simple. The way it typically compares items relies on having clear positive and negative connections between pairs. In our case, we can only observe certain selections, which complicates how we can build comparisons.

#Adapting CLIP for Card Selection

To make CLIP work for our card selection problem, we need to adjust how we use it. Instead of looking at pairs of items, we treat a selection of cards as a group. By doing this, we can learn better about which cards fit well together based on player choices. Our approach focuses on gathering data from real gameplay to build a clearer picture of preferences.

#Data from Magic: The Gathering

For our research, we gathered data from players of Magic: The Gathering. Players go through a drafting process where they pick cards from a limited pool. By examining the choices players make, we can learn about their preferences. Each draft gives us a set of possible cards and the one chosen, allowing us to see how players value certain cards over others.

#How Drafting Works

In a typical draft, each player starts with a pack of cards and chooses one card to add to their collection. The rest of the cards are passed to other players, and the process repeats. Each player ends up making multiple selections throughout the drafting phase. Since the choices are interconnected, understanding how these decisions are made becomes crucial for our analysis.

#Context Matters in Card Selection

When players pick cards, the context plays a significant role. For example, a player might prefer one card over another depending on the cards they already have. This context is vital for building models that accurately reflect preferences since the best choice isn't just about the individual cards, but how they work together.

#The Role of Neural Networks

To analyze player preferences, we deploy neural networks. These systems can learn complex patterns from data. For our work, we need two types of networks: one to understand individual cards and another to comprehend groups of cards, or pools. By Training these networks on player data, we can create an embedding space where similar preferences are grouped together.

#Training with Behavioral Data

Training our models involves using the decisions players make during drafts. We can create training samples that pair the chosen card with the pool of unselected cards. This structured approach allows us to compare how well different combinations of cards are preferred over others in a specific context.

#The Challenge of Large Numbers

Since the number of potential card combinations can be quite large, managing this data becomes tricky. Every time a player selects a card, they create a series of pairwise preferences. However, because one card is selected while many others are rejected, the dataset can become heavily biased. This bias could distort the performance of our models if not handled properly.

#Improving Comparison Methods

To refine our approach, we modify the training technique. Instead of directly using pairwise comparisons, we aim to capture the essence of selections based on their context and relationships. Through this method, we can ensure that we are focusing on the most relevant comparisons and disregarding those that don't help inform our understanding of preferences.

#Results from Experiments

When we put our methods to the test, we noticed several interesting results. Our adapted version of the learning process outperformed traditional methods. By emphasizing contextual preferences over arbitrary comparisons, the model became more accurate in predicting which cards players would select.

#Future Directions

Looking ahead, we want to explore more advanced techniques for understanding preferences. While our current method shows promise, the potential for even greater accuracy and efficiency exists. By developing strategies that incorporate context even further into loss computations, we could enhance our models.

#Conclusion

In summary, learning about player preferences in card games like Magic: The Gathering is a complex but valuable endeavor. By using tools like CLIP and adapting them for our needs, we can build AI that better understands how players make decisions. This research lays the groundwork for future advancements in the field, and we look forward to seeing how these methods evolve to provide deeper insights into player behavior.