Crafting a Jeopardy Simulation with AutoGen

Setting Up the Moderator

The initial step involved configuring the moderator, which operates on GPT-4. The moderator is responsible for guiding the game's progression, including choosing questions and verifying answers. Below is an example of how the moderator was configured:

Setting Up the Players

For the contestants, we designated Peter and Bob. Peter was programmed to utilize the local LLaMA2 model, while Bob was equipped with GPT-3.5. This diversity in AI models introduced an engaging dynamic to the gameplay.

Integrating the Agents

Once the agents were set up, the following task was to integrate them into a group chat setting for interaction. This integration was achieved with AutoGen's GroupChat and GroupChatManager functionalities, facilitating seamless communication and smooth flow of the game.

Database Configuration and Function Integration

To accurately simulate the Jeopardy game, it was necessary to establish a database for questions and implement a method for tracking the players' scores. SQLite, known for its simplicity and efficiency, was chosen for the database, which was then integrated with the AutoGen framework.

Database Configuration

The database's structure was crafted to hold an array of Jeopardy questions, complete with their answers, categories, and point values. This diversity was essential in offering a wide range of questions for the AI agents to address.

Managing the scores was a pivotal element of the game's framework. To address this, I developed functions within the database that would allow for adding to and retrieving player scores. This functionality was key to maintaining an accurate and up-to-date leaderboard.

Function Mapping

Function mapping plays a crucial role in our Jeopardy simulation, as it connects the AI agents with specific actions within the SQLite database. This connection is essential for enabling the agents to execute tasks such as pulling questions from the database. A prime example of this is the get_random_question method.

The get_random_question method is crafted to select a random question from the database's questions table. This functionality is fundamental to the Jeopardy game, serving as the mechanism through which the moderator presents questions to the contestants.

A declaration serves as a functional blueprint, outlining the structure and prerequisites of the function. This blueprint clarifies for the AI agents the manner in which they can engage with the function.

Regarding the get_random_question method, its declaration encompasses specifics such as the function's name, a narrative of its objective, and the parameters it necessitates. This ensures the AI agents are well-informed on how to correctly utilize the function within the game's context.

In our Jeopardy simulation, the moderator uses this setup to know how to invoke the get_random_question method and which parameter (specifically, the category) should be passed to it. This ensures the moderator can efficiently fetch questions from the designated category, maintaining the game's flow.

Bringing the AI Jeopardy Game to Life

With the AI agents and the database prepared, the subsequent step involved activating the Jeopardy game through an intuitive interface coupled with ingenious programming methods, such as monkey patching.

Gameplay Dynamics

Throughout the game, the AI agents engage within a group chat setup. The moderator poses questions sourced from the database, while the contestant agents, Bob and Peter, provide answers drawing on their respective AI model's strengths. These answers are assessed, leading to the appropriate adjustments in their scores.

Showcasing the Game

To enhance the game's interactivity and appeal, I crafted a web interface utilizing Flask. This interface acts as the visual stage for the game, showcasing questions, answers, and real-time score updates. The simplicity of Flask's development server facilitated the swift creation of this engaging platform.

Flask SocketIO played a crucial role in establishing real-time communication between the server and the web interface. It enabled the dynamic push of updates to the web page in sync with the game's progress, allowing players to witness the unfolding of the game live.

 

 

Implementing Monkey Patching

Given that AutoGen, the framework for our AI agents, did not inherently support socket-based messaging for live web updates, I resorted to monkey patching. This approach enabled us to alter the functionality of existing functions within our Python code.

Through monkey patching, we redirected the output messages from the agents, which were initially intended for the console, to our web interface instead. This adaptation guaranteed that every move and reply within the game was instantly displayed on the web page, enhancing the live game experience.

 

 

I designed a straightforward but efficient web page to exhibit the game's progress. The core component of this interface is a JavaScript script dedicated to managing the real-time communication between the Flask server and the web page via SocketIO. This setup ensures seamless updates of the game's events directly on the web page, providing a dynamic and interactive user experience.

Let's take a look at a captivating exchange between our AI moderator and Bob during the gameplay. 

Final Reflections

The AI Jeopardy game showcased not just the seamless integration of AI models but also addressed substantial technical hurdles, culminating in a dynamic web-based gaming experience. More than its technical achievements, the game acted as a compelling and informative demonstration of AI's capabilities within the realms of gaming and entertainment.