Editing Openai/693343d7-a38c-8012-a67c-11cbed4c0fd9 (section)

===== 1. Multi-Agent Systems (MAS): - In MAS, agents are active actors that make decisions and interact with each other. Entities (passive actors) are the resources that agents act upon. - By treating both agents and entities as actors, we get a unified model for system dynamics where change and interaction are central to the system's evolution. =====
# Autonomous Systems: - Autonomous systems (like self-driving cars, AI systems, or robots) can be modeled using the actor framework, where agents make decisions and change their behavior based on the environment, while entities (like obstacles or traffic signals) change passively in response to agents' actions.
# Complex Systems: - Systems with mixed components (e.g., environmental systems, social networks) can benefit from this unified framework, where both passive components (entities) and active components (agents) interact to produce complex outcomes.
# Machine Learning and Reinforcement Learning: - In reinforcement learning, the agent is the active actor that adapts its behavior to maximize rewards. The environment (entities) provides feedback, and the agent's behavior evolves as it learns from past interactions.