Connections AI ML Model

This project provides a machine learning-based solution for generating optimal guesses in the Connections AI Game. The game challenges players to group related words into four sets of four. The model uses Sentence-BERT embeddings and Agglomerative Clustering to identify relationships between words and propose accurate guesses.

Features

• Semantic Understanding: The model leverages a pre-trained Sentence-BERT model (paraphrase-MiniLM-L6-v2) to compute embeddings for each word, enabling the detection of subtle relationships and meanings.

• Clustering-Based Grouping: Using Agglomerative Clustering with cosine similarity as the metric, the model groups words into semantically related clusters, providing logical and data-driven guesses.

• Adaptive Guessing: The model incorporates game-specific parameters like:

  • Handling cases where the player is "one word away" from a correct group.
  • Tracking previously guessed groups to avoid repetition.
  • Modifying guesses dynamically based on feedback.

• Robust Fallback Mechanism: If clustering does not produce a valid guess, the model falls back to a randomized selection of four words, ensuring continuous gameplay.

• Error Handling: The system gracefully handles exceptions such as model loading errors and invalid inputs.

Key Components

1. Word Preprocessing:

   • Converts all words to uppercase for uniformity.
   • Excludes already grouped words from further consideration.

2. Embedding Generation:

   • Computes word embeddings using Sentence-BERT, capturing semantic nuances.

3. Clustering:

   • Groups words into four clusters based on similarity.
   • Uses cosine distance and average linkage for optimal clustering.

4. Dynamic Guessing:

   • Adapts guesses based on the game's state (e.g., previous guesses, correct groups, and strikes).

5. Game Logic Integration:

   • Ensures compliance with game rules, such as selecting exactly four unique words per guess.
   • Ends the turn when strikes reach the limit or all words are grouped.

Challenges Addressed

• Identifying semantic relationships between words in diverse and ambiguous datasets.
• Balancing logical guess generation with fallback strategies for edge cases.
• Adapting machine learning techniques to a dynamic and rule-based game environment.

Technologies Used

• Python: Programming language for implementation.
• Sentence-BERT: Pre-trained transformer model for embedding generation.
• Scikit-learn: For clustering and other machine learning utilities.
• NumPy: For efficient numerical computations.
• Random: For generating fallback guesses when necessary.

Acknowledgments

• Sentence-BERT: Thanks to the creators of the paraphrase-MiniLM-L6-v2 model for providing robust word embeddings.
• Scikit-learn: For providing the tools necessary for clustering and similarity computation.
• Game concept inspired by the Connections AI Game mechanics.

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This project demonstrates the application of machine learning techniques to create a fun and engaging game experience while exploring semantic relationships in natural language data.