*Note: This is a public facing README.md for a private project repository. * I am building a simulator to observe emergence and complexity in cellular automata. I am interested in complexity theory, software dynamics, art, and artificial life (ALife). I am doing this project as a way to integrate all of these passions of mine at their respective intersections. Let's begin by defining the terms cellular automata and intelligent agent.

A cellular automaton is a discrete, dynamic model that is made up of a grid of cells. Each cell can be in a certain state, and the state of each cell can change over time, depending on the states of the cells around it. This means that it is a system that is made up of a finite number of discrete states, and that the state of the system changes over time in a discrete manner. The state of the system at any given time is determined by the states of the system at the previous time and by the rules that govern the system. The rules that govern cellular automata are typically very simple. However, the behavior of a cellular automata can be very complex, even with simple rules. This is because the behavior of the system is determined by the interactions between the cells, and these interactions can be very complex, even with simple rules. Cellular automata are often used to simulate the behavior of physical systems, such as the spread of disease, the emergence of life, or the behavior of the brain.

A system that can reason, learn, and act autonomously. It can perceive its environment and make decisions based on its understanding of the world. Intelligent agents are often used in artificial intelligence (AI) applications, such as robotics, natural language processing, and game playing.

• Perception: An intelligent agent must be able to perceive its environment. This means that it must be able to sense the world around it and collect information about it.
• Reasoning: An intelligent agent must be able to reason about the information that it has perceived. This means that it must be able to make inferences and draw conclusions about the world.
• Learning: An intelligent agent must be able to learn from its experiences. This means that it must be able to store information about its experiences and use that information to improve its performance in the future.
• Action: An intelligent agent must be able to act autonomously. This means that it must be able to make decisions about what to do and then take actions to achieve its goals.

Whether or not cellular automata can become intelligent agents is a philosophical quandary and active area of research and debate. While there is no consensus on this issue, there does exist evidence to support both sides of the argument.

On the one hand, cellular automata are capable of exhibiting complex behavior. For example, they can be used to simulate the behavior of traffic flow, the spread of disease, and the evolution of cooperation. This suggests that they may be capable of learning, adapting, and cooperating, which are all essential features of intelligent agents. More arguments that favor the view that cellular automata can become intelligent agents:

• Cellular automata can exhibit complex behavior that is similar to the behavior of intelligent agents.
• Cellular automata can be programmed to learn and adapt their behavior over time.
• Cellular automata can be programmed to communicate and cooperate with each other.

On the other hand, cellular automata are deterministic systems. This raises the question of whether or not they can truly be said to be intelligent, as intelligence is typically associated with the ability to make choices and to act independently. More arguments against the view that cellular automata can become intelligent agents:

• Cellular automata are deterministic systems, which means that their behavior is completely determined by their initial conditions and their rules.
• Intelligence is typically associated with the ability to make choices and to act independently, which is not possible for cellular automata.

As the population of cellular automata increases in complexity, following scaling laws, they will begin to exhibit intelligent and cooperative behavior. This is because the increased complexity will allow them to form more complex relationships with each other, which will lead to the emergence of new behaviors.

For this project, I will be conducting various types of research. I understand the importance of using sound and rigorous methodology and therefore plan to follow the scientific method for all computational aspects of this project , i.e. the simulation studies and experiments. Due to being interested in both the theoretical aspects of this topic and the practical aspects I will conduct the following research:

Literature review: I will conduct a literature review to identify the different perspectives on this issue and the evidence that has been presented to support each perspective.

Simulation studies: I will conduct simulation studies to investigate the behavior of cellular automata under different conditions. This will help to better understand their capabilities and their potential to develop intelligent behavior.

Experiments: I will conduct experiments to test the ability of cellular automata to learn, adapt, and cooperate. This will determine whether or not they have the necessary capabilities to be considered intelligent agents.

Philosophical analysis: I will conduct a philosophical analysis of the concept of intelligence to better understand what it means for a system to be considered intelligent. This will ensure that I develop a more nuanced understanding of the debate over whether or not cellular automata can become intelligent agents.

My goal for this project is not to prove or disprove my hypothesis. Instead, my aim is to create a final product that is open-ended and exploratory, allowing others to see the data represented in a way that is aesthetically dynamic and unbiased. I believe that this project has the potential to shed new light on the nature of complexity and intelligence, and to create new forms of art.