amesa-inference

Create a Python-based mini-application called 'AgentPredictor' that leverages the 'amesa-inference' package to predict agent behaviors in a simulated environment. This application will serve as a tool for researchers and developers interested in understanding complex agent interactions without the need for heavy computational frameworks like Ray or PyTorch.

Step 1: Setup the Environment
- Install Python and the required packages including 'amesa-inference'.
- Set up a virtual environment for dependency management.

Step 2: Define the Simulation Environment
- Create a simple simulation environment where agents interact based on predefined rules.
- Each agent has specific attributes such as position, speed, and interaction radius.

Step 3: Implement the Prediction Module
- Use 'amesa-inference' to load pre-trained ONNX models that predict agent behavior based on current state.
- Integrate the prediction module into the simulation loop to update agent behaviors.

Step 4: Visualization
- Implement basic visualization of the simulation using matplotlib or similar libraries.
- Display real-time updates of agent positions and predicted movements.

Step 5: User Interface
- Develop a simple command-line interface allowing users to control simulation parameters.
- Options include changing the number of agents, their initial positions, and interaction rules.

Suggested Features:
- Support for different types of interaction rules between agents.
- Save and load simulation states for reproducibility.
- Performance metrics to evaluate the accuracy of predictions against actual agent behaviors.

The 'amesa-inference' package is utilized throughout the project for its ability to perform efficient agent behavior predictions using ONNX models. This makes it ideal for real-time simulations where computational efficiency is crucial.