autonoesis

Create a cognitive agent mini-application using the Python package 'autonoesis'. This application will simulate a personal assistant capable of understanding its own state and environment, making decisions based on this understanding, and learning from its interactions. The app should include the following functionalities:

1. **Self-Modeling**: The agent must have the ability to model itself, understanding its current state and capabilities.
2. **Environmental Awareness**: Implement a feature where the agent can perceive its environment through sensors or input data, updating its internal model based on these perceptions.
3. **Decision Making**: Based on its self-model and environmental awareness, the agent should make decisions about actions to take, such as responding to user queries or performing tasks.
4. **Learning Mechanism**: Incorporate a learning mechanism that allows the agent to improve its decision-making over time through feedback loops and reinforcement learning techniques.
5. **Interaction Interface**: Develop an intuitive interface for users to interact with the agent, including both text-based and voice commands.
6. **Scenario Simulation**: Integrate scenario simulation capabilities where the agent can predict outcomes of different actions before executing them, enhancing its decision-making process.

Use 'autonoesis' to facilitate the self-modeling and phenomenological aspects of the agent. For instance, utilize the package's features to enable the agent to understand its own limitations and strengths, adapt its behavior based on environmental changes, and continuously refine its self-understanding and interaction strategies. Your goal is to create a fully-functional mini-app that demonstrates the potential of cognitive agents in everyday scenarios, showcasing the power of 'autonoesis' in enabling sophisticated self-awareness and adaptive behavior.