Some Notes

Defining Artificial Life (A-Life)

Strong A-Life -> Truly alive, synthesized life.

Weak A-life -> Not truly alive, but modelling lifelike behaviour.

Genetic algorithms and genetic programming. These are similar and related yet subtly different approaches to breeding solutions and behavioural models.

Genetic algorithms (GA) – Take a best fit solution to a specific problem.

Genetic programming (GP) – modifies the solution to fit the problem by changing the actual algorithm.

Both use algorithms borrowed from our observation of life – mutation, inheritance, and genetic crossover – combined with some selection criteria. These selection criteria may differ accordingly to what the designer is trying to achieve, but they are commonly linked to the success of GA/GP implementation with respect to a given problem domain.

In our case, the algorithms are derived form classic AI building blocks and A-life theory to create more natural in-game behaviour models.

Calculated A-Life and Synthesized A-Life

Take for an example, a tile in sim city, which is designated as residential. If the population is already housed, then the square will remain empty until a calculation indicates there are more bodies than houses – at which point the square will be built apon. This is calculated A-life. The synthesized A-life might allow a passing sim to see a virtual for sale sign, which beginning a series of decisions that lead it to build on that square or not. Another visiting sim might see the 1^st sims house for sale, note that there are good jobs in the area, a nice shopping mall, and decide to buy it. The result is the same. On the one hand we have dictated the outcome and on the other hand we have allowed it to take place.

I would be looking to make use of both Calculated and Synthesized A-life in my project.

If the player chooses to influence the game somehow, we allow it to react to that interaction from the ground up, whatever the consequences.