Description: A Finite State Machine (FSM) is a computational model used to design algorithms in various fields, including reinforcement learning, where a system can be in one of a finite number of states. This model is characterized by its ability to represent systems that change state in response to external events, making it a powerful tool for simulation and process control. In an FSM, each state can have defined transitions that depend on the actions taken, allowing the system to learn and adapt to its environment. FSMs are fundamental in reinforcement learning as they enable the representation of decision policies and the optimization of actions based on received rewards. Their simple and clear structure facilitates the understanding and design of complex algorithms, making them ideal for applications in artificial intelligence and robotics. Additionally, their discrete nature allows for efficient implementation in hardware and software, making them a popular choice in the development of autonomous systems and in simulating behaviors in controlled environments.
History: Finite State Machines have their roots in automata theory, which was formalized in the 1950s by mathematician John von Neumann and others. Over the years, their application has expanded from computation theory to software engineering and artificial intelligence. In the 1980s, with the rise of artificial intelligence, FSMs began to be used in control systems and robotics, where decision-making in dynamic environments became crucial.
Uses: Finite State Machines are used in a variety of applications, including the design of communication protocols, control systems in robotics, video games, and in the implementation of reinforcement learning algorithms. Their ability to model discrete behaviors makes them ideal for situations where decisions must be made based on a limited set of states.
Examples: A practical example of a Finite State Machine is the control system of a traffic light, where the states represent the different lights (red, yellow, green) and the transitions depend on time and traffic conditions. Another example can be found in video games, where non-player characters (NPCs) use FSMs to decide their actions based on their current state and interactions with the player.
