About

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:

• Multi-agency
• Organic interaction timing
  • Frequency-based interaction
  • Interaction at regular intervals
  • Interaction at specific scheduled meetings
• Quality-informed solution sharing
• Quality bias reduction
• Self-bias
• Operational learning
  • Multinomial reinforcement
  • Markov chain reinforcement
  • Hidden Markov model reinforcement
• Locally-sensitive search
  • Geoemtric annealing schedule
  • Cauchy annealing schedule
  • Triki annealing schedule
• Satisficing

Usage

Here is a basic examples of usage

use cisat::{Cohort, Parameters, problems::Ackley};
fn main() {
    let mut x = Cohort::<Ackley>::new(Parameters::default());

    x.solve();

    println!("{:?}", x);
}

You can also implement new problem, agent, and team types using the Solution, AgentMethods, and TeamMethods traits, respectively. This allows significant flexibility within the basic CISAT structure.

References

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
2. 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
3. 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
4. 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