This is an implementation of the Cognitively-Inspired Simulated Annealing Teams (CISAT) Framework in Rust.
This is currently an incomplete implementation. Progress on CISAT characteristics includes: - [x] Multi-agency - [x] Organic interaction timing - [x] Quality-informed solutions haring - [x] Quality bias reduction - [x] Self-bias - [ ] Operational learning - [ ] Locally-sensitive search - [ ] Satisficing
Here is a basic examples of usage
```rust
use cisat::{Cohort, Parameters, problems::Ackley};
fn main() {
let mut x = Cohort::
x.solve();
println!("{:?}", x);
}
``
You can also implement new problem, agent, and team types using theSolution,AgentMethods, andTeamMethods`
traits, respectively. This allows significant flexibility within the basic CISAT structure.
Aspects of CISAT have been published in several places. You can learn more about it here: 1. McComb, C., Cagan, J., & Kotovsky, K. (2015). Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model. Design Studies, 40, 119-142. doi:10.1016/j.destud.2015.06.005. PDF 1. McComb, C., Cagan, J., & Kotovsky, K. (2016). Drawing inspiration from human design teams for better search and optimization: The heterogeneous simulated annealing teams algorithm. Journal of Mechanical Design, 138(4). doi:10.1115/1.4032810. PDF 2. McComb, C., Cagan, J., & Kotovsky, K. (2017). Capturing human sequence-learning abilities in configuration design tasks through markov chains. Journal of Mechanical Design, 139(9). doi:10.1115/1.4037185. PDF 1. McComb, C., Cagan, J., & Kotovsky, K. (2017). Optimizing design teams based on problem properties: computational team simulations and an applied empirical test. Journal of Mechanical Design, 139(4). doi:10.1115/1.4035793. PDF