*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).
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.
Artificial life (ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, biochemistry, and robotics. There are 3 mains kinds of A-life named for their approaches: soft (as in software), hard (hardware), and wet (biochemistry).
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.Key features of an intelligent agent:• 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.
My project will investigate whether cellular automata and other forms of artificial life (A-Life) can develop genuine intelligence through a multifaceted approach combining literature review, simulation studies, experiments, and philosophical analysis. I will review existing perspectives and evidence, simulate cellular automata behavior under various conditions, experimentally test A-Life's capacity for learning, adaptation and cooperation, and philosophically examine the very concept of intelligence. Rather than seeking to definitively prove or disprove my hypothesis, my goal is to create an open-ended, exploratory final product presenting the data in an aesthetically dynamic and unbiased manner to shed new light on the nature of complexity and intelligence while pushing the boundaries of art.
