Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Aģentu balstīta šūnu automāti× | Stohastiskie šūnu automāti× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1986–1996 | 1940s–1980s |
| Autors≠ | Wolfram, S.; Epstein, J. M. & Axtell, R. | von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B. |
| Tips≠ | Hybrid spatial simulation | Grid-based stochastic simulation |
| Pirmavots | Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 978-1579550080 | Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080 |
| Citi nosaukumi | ABCA, CA-ABM, Agent-CA, Hybrid Agent-Cellular Automaton | SCA, Probabilistic Cellular Automata, PCA, Stochastic CA |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution. | Stochastic Cellular Automata (SCA) extend classical cellular automata by replacing deterministic transition rules with probabilistic ones, allowing each cell on a grid to change state according to a probability distribution conditioned on its neighborhood. This makes SCA a powerful tool for simulating real-world spatial processes where randomness, noise, and uncertainty govern local interactions — from epidemic spread and forest fires to traffic flow and material diffusion. |
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