I don’t know for certain, but I can make a guess. This is just my opinion, some others may disagree.
The field of ALife has four branches that I’m aware of:
Self-Organizing/self assembly behavior. This is the application you refer to, another context it’s useful is swarm control (for drone swarms, for example). While this is technically ALife, as far as I’m aware it’s not really where most of the emphasis is. Swarm control and self assembly are seen as “different” problems, as machines that can work together and also build more of themselves is interesting (and potentially dangerous), but is missing out on the diversity, the open-endedness that life on earth has. Much of ALife research is focused on trying to formally define this open-endedness and coming up with systems that achieve that. Self assembly and swarm control are interesting and difficult problems, just different. This leads to the other three sides of ALife research:
Coming up with environments, and running tests on them. This is a constant game of coming up with a definition that seems to capture open-endedness, then coming up with ALife sims that meet that criteria but fall short of our expectations. So new definitions are made and we repeat. Geb is a classic example: Geb has passed pretty much every test so far, but it’s fairly uninspiring to watch. Most of those programs you reference chose a particular ALife paradigm, but that paradigm may not be the right one, and is often disappointing. Because we still haven’t found something that really “looks like life”, new paradigms and programs are constantly being created and abandoned when they fail to work (Or perhaps some would have already worked, but the computing time is too much). That’s what you’re seeing. Without any unifying theory or sim that is really convincing, I suspect it’ll stay this way for a while. And because:
- we still haven’t made much progress since Karl Sims in the 90s, or since Geb (this point is debatable)
- these sorts of sims don’t really have much commercial use aside from games
the direction of making new simulators seems to be lacking funding and research interest, as far as I can tell. Commercial sim games seem to push the boundary these days.
Fortunately there’s a sub field of cellular automata life that’s pretty interesting, its software is slightly more developed due to the overlap with cellular automata and ease of implementation, and research seems to be progressing there at an okay rate.
Realistically, there seem to be two things people want: novel behavior, and novel bodies. My two cents is that these are separate problems, and achieving both is more expensive than just achieving one. But most of these sims end up not balancing development happening in both of these factors (doing this is very difficult), so one factor develops much further than the other, and this disconnect is disappointing to the sim creator. For example, Geb does behavioural diversity really well, while Karl Sims does body diversity well. Sensitivity to small details like mutation rate or genetic encoding also can be quite frustrating. Fortunately, eventually we’ll sorta get behavioural diversity for free in any sim once RL/AI is really understood well.
The third piece of ALife research I’m aware of is the theoretical side, which right now mostly isn’t really far enough along to warrant practical implementation. One big branch of this is the learning theory side, represented by Valiant’s Evolvability theory and followups. Essentially this talks about what functions are possible to evolve, and using stuff like PAC Learning theory they are able to prove some things. Some of these models are more natural than others, but it’s an interesting perpendicular approach to coming up with sims and seeing if they do what we want. Maybe eventually these two approaches will meet in the middle at some point, but they haven’t yet.
The fourth piece is Artificial Chemistry. I recommend this paper as a somewhat dated overview. While this is technically a field of ALife, and is centered around understanding a chemical system that has the necessary emergent properties, it has broken off into applications that may have industrial relevance. For example, robust self repairing and self assembling electronic systems, DNA computing (DNA is capable of simulating arbitrary chemical reaction networks which are capable of arbitrary computing), and artificial hormone systems for automatic task assignment. This has some software developed, but much of that software isn’t really considered ALife anymore since it has branched off into its own domain.
