Exploring Simple AI Projects with Expert Systems and Game Development

Artificial Intelligence (AI) can be approached through various projects, ranging from complex machine learning models to simpler, more straightforward tasks. Two interesting project ideas include building an expert system and developing a simple AI game. In this article, we'll explore both of these projects, providing a comprehensive guide for each.

1. Building an Expert System in Prolog

One intriguing project for AI enthusiasts is to build an expert system. An expert system is a type of program that can answer questions in a specific domain based on a set of predefined rules and knowledge. For instance, a simple expert system could answer questions about a short story by analyzing the input and providing relevant answers based on the provided context.

In this article, we'll explore a famous expert system challenge from the 1980s, where an AI program needs to answer questions about a short story. The problem statement was as follows:

A short story written in plain English, e.g., "Jack needed money. Jack got a gun. Jack went to the liquor store." Two sample questions that need answering, e.g., "What did Jack need?" and "Why did Jack go to the liquor store?"

The first question is straightforward, but the second requires the AI to infer and provide a logical answer. Most programs struggle with the second question, as it requires the AI to understand the context and make inferences.

For this project, Prolog is an excellent choice. Prolog is a logic programming language well-suited for building expert systems. Here's a basic outline of how to approach this project:

Define the knowledge base: Create a set of rules that the AI can use to answer questions. For the given example, the rules might include things like the relationship between the actions and their intentions. Guide the AI: Implement a way for the AI to ask follow-up questions or gather more context if needed. This ensures the AI can provide more detailed and accurate answers. Test and refine: Test the system with different stories and questions to ensure it can handle a variety of scenarios. Refine the rules and knowledge base as necessary.

While this seems like a challenging task, Prolog's declarative nature makes it easier to define and maintain the knowledge base. By providing a logical framework for the AI to follow, you can create a system that can answer complex questions about a story.

2. Simple AI Game Development

In contrast to the expert system, another engaging AI project is game development. A classic example is a simple game where you control a vehicle to collect as many mines as possible within a time limit. This project is ideal for AI beginners because it provides a clear structure for input, processing, and output.

Let's break down this project into steps:

User Interface: You can use Windows Forms and Graphics objects for a desktop application. Alternatively, you can opt for JavaScript and web technologies for a web application. Both options are feasible and can be implemented quickly. Define Sensory Data: The game should have clear inputs for the AI. For instance, define sensor data such as the distance and direction to the nearest mine. This can be represented as three numbers: one for distance and one for the direction. Artificial Neural Network: Create a neural network that can make decisions based on the sensor data. The output should include actions such as steering the wheels, applying gas, or breaking. Genetic Algorithm: Implement a genetic algorithm to allow the AI to learn and improve over time. By observing and evaluating the AI's performance, you can fine-tune the system and watch it get better.

Implementing the Game

The project can be completed within a short timeframe, making it a great starting point for anyone new to AI programming. Here's a step-by-step guide to help you get started:

User Interface: Set up a graphical user interface (GUI) where the user can control the vehicle and interact with the game. This can be done through a window form in a desktop application or a web interface using HTML and JavaScript. Sensor Data: Define the sensor data for the vehicle. For example, the distance to the nearest mine and the direction from which it is coming. This data should be processed to generate the necessary outputs for the AI to make decisions. Neural Network: Develop an artificial neural network (ANN) that can take the sensor data as input and provide appropriate actions. The network should be modifiable to allow for experimentation and fine-tuning. Genetic Algorithm: Implement a genetic algorithm to train the neural network. By using a population of AI agents, you can iteratively improve their performance over time. Testing and Evaluation: Run the game multiple times to observe the AI's performance. Use the results to make adjustments to the neural network and genetic algorithm until you achieve the desired level of performance.

Conclusion

Whether you choose to build an expert system using Prolog or develop a simple AI game, these projects offer excellent opportunities to explore the world of AI programming. Both projects are relatively easy to implement yet provide a lot of room for creativity and improvement.

By choosing one of these projects, you can gain valuable experience in AI and develop skills that will benefit you in your future endeavors. Remember, the key to success in AI is not just about writing code, but also about understanding the underlying concepts and continually refining your approach.