Post-apocalyptic computing from cellular automata
By: Genaro J. Martinez, Andrew Adamatzky, Guanrong Chen
Potential Business Impact:
Computers can learn from nature's patterns.
Cellular automata are arrays of finite state machines that can exist in a finite number of states. These machines update their states simultaneously based on specific local rules that govern their interactions. This framework provides a simple yet powerful model for studying complex systems and emergent behaviors. We revisit and reconsider the traditional notion of an algorithm, proposing a novel perspective in which algorithms are represented through the dynamic state-space configurations of cellular automata. By doing so, we establish a conceptual framework that connects computation to physical processes in a unique and innovative way. This approach not only enhances our understanding of computation but also paves the way for the future development of unconventional computing devices. Such devices could be engineered to leverage the inherent computational capabilities of physical, chemical, and biological substrates. This opens up new possibilities for designing systems that are more efficient, adaptive, and capable of solving problems in ways that traditional silicon-based computers cannot. The integration of cellular automata into these domains highlights their potential as a transformative tool in the ongoing evolution of computational theory and practice.
Similar Papers
Evolution of Spots and Stripes in Cellular Automata
Cellular Automata and Lattice Gases
Evolves computer patterns that look like animal spots.
Self-replication and Computational Universality
Cellular Automata and Lattice Gases
Makes computers understand how life copies itself.
Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Cellular Automata and Lattice Gases
Finds hidden patterns to build smarter AI.