Poker Assistant

Inspiration

The thrill of poker lies in its blend of skill, strategy, and chance. We wanted to create a tool that empowers players to make smarter decisions by combining statistical analysis, probability simulations, and AI to optimize gameplay.

What it does

The Poker Assistant calculates the probability of winning based on player hands and community cards, simulates potential outcomes, and suggests the best course of action (fold, check, bet, or raise). It provides real-time insights to enhance strategic decisions.

How we built it

We built the Poker Assistant as a cutting-edge tool that combines Monte Carlo simulations, weighted opponent ranges, and Kelly bet sizing to provide real-time poker insights. Here's how it works:

Monte Carlo Simulations:

To estimate the probability of winning, the assistant runs thousands of simulations, randomly generating unknown community cards and opponent hands from a weighted range. Each trial evaluates the player's hand strength relative to an opponent's using poker hand ranking logic, such as detecting straights, flushes, or full houses. Weighted Opponent Ranges:

The assistant incorporates predefined weighted opponent hand ranges, reflecting realistic probabilities for different hands. This approach ensures more accurate equity calculations compared to assuming a uniform distribution. Kelly Bet Sizing:

The Kelly criterion determines optimal bet amounts based on winning probability and pot odds. The assistant dynamically suggests whether to raise, fold, or check, and calculates the raise amount proportionally to the player's bankroll and risk. Probabilistic Reasoning:

We utilized sCASP (a declarative constraint logic programming framework) to model game logic, define poker hand rankings, and evaluate Monte Carlo-generated outcomes efficiently. Modular Design:

The assistant's modular architecture includes dynamic predicates to track game state, community cards, and player hands. Core algorithms for equity calculation and decision-making are decoupled for flexibility and extensibility.

Challenges we ran into

Implementing accurate hand rankings for all poker combinations. Balancing Monte Carlo simulation accuracy with real-time performance. Designing realistic weighted opponent ranges. Integrating Kelly bet sizing with probabilistic outputs. Debugging complex logic with dynamic game states.

Accomplishments that we're proud of

Developing a robust Poker Assistant with real-time decision-making. Successfully integrating Monte Carlo simulations and Kelly criterion. Optimizing performance to handle thousands of simulations efficiently. Creating modular and extensible AI for different poker scenarios.

What we learned

Advanced probabilistic reasoning and decision-making under uncertainty. Efficiently implementing Monte Carlo simulations for complex scenarios. Integrating mathematical models like Kelly bet sizing into game strategy.

What's next for Poker Assistant

Expand to multi-player games and additional poker variants. Incorporate machine learning to adapt strategies based on player behavior. Optimize simulation algorithms for even faster real-time calculations.

Built With

• css
• html
• javascript
• prolog
• python
• scasp