I’ve played a bit the beta version with the new combat system. Having the option for the full control mode improves (my) combat experiences a lot, since you’re less likely to lose your favorite unit and can make more use of exploration and/or fodder units. E.g., having two bats block a tunnel for enemy reinforcements for two turns while your orcs beat up a dwarf can really make a difference.
The new mana system is great! It improves the strategic progression of the game a lot.
I’m especially looking forward to the new combat mechanics. When attacking an elven village in the current version you immediately lose track of what’s happening since all your units run off in the forest chasing elves.
… the units move sequentially, so after single move a map can get invalidated.
Yes this was one of the difficulties that I encountered. Another is that as in the case below the repelling influence of Y on X can be low when Y’s shortest path to X (given flying, etc) is long. If the wall among them would be broken down, then the influence would be large. Given that there are many dungeon-maze situations in KeeperRL, we would have to use much potential path planning, which does not make things easier.
|--------| X |Y | | |--| | | |--| | | | |--------|
When I’m working on it I often feel like perhaps it’s better to first focus on some more simple mechanisms such as flocking behaviors and relative formations and hope for cool emergent effects.
Thanks for asking! Yes, I’m still working on the tactical A.I., but have taken a bit of a different development approach. The KeeperRL code is still under development and C++ isn’t my most comfortable language, so I’m currently developing a testing environment for ideas in Java. This way I can focus more productively on setting up experiments to try out ideas and then, if something works, I can port it to C++/KeeperRL.
My A.I. approach relies heavily on high-level observations for which I’m still in the phase of determining which ones are useful. I’ve been going through some research papers and books (e.g. A.I. programming wisdom) on tactical A.I. for turn-based games. Most sources say “make an influence map” and “search the game tree smartly”. I do not want to build game trees due to the computational cost of such algorithms. I’m not trying to make the best tactical A.I., but a fun tactical A.I. However, influence maps is definitely something I’m looking at.
The hard part with influence maps is determining how to go from different maps to a plan to execute. If you have any ideas/experience with this then I’d love to have a talk about it : )
On a side note regarding formations:
If you’re interested we could collaborate on making the simple formation system that you mentioned earlier where:
1. You order units to stay.
2. You position yourself.
3. You hit a button “hold formation” that keeps the units at the relative position.
4. You can hit a button to release the formation.
5. You can hit a button to turn the formation 45 degrees clock/counterwise.
Your texture pack looks great! I would love to be able to switch between texture packs in the future. Nice touch that statues are statues of the keeper.
Unless you mean another sprite, you can see a key when you lock a door (by clicking on it).
Sounds like solid plans. I’m looking forward to the endless mode! Just an idea for ongoing resource supply: you may introduce a transmutation workshop where shamans can transform equipment into resources (the equipment is ongoing due to the raiding parties). This way you also don’t accumulate hundreds of equipment items.
Edit: if transmutation also costs mana, then you also maintain a reason to keep studying after having researched everything. Instead of furniture a spell (like summon imp) could be used to that transforms all items on a square into resources. This is probably easier to implement.
For future readers/compilers;
In Ubuntu 16.04 for KeeperRL Alpha 20.1 I had to deviate a little from the Github instructions. I had to install:
Then it compiled correctly with
make -j 8 OPT=true RELEASE=true CLANG=true
The Docker method gave me errors on missing packages, and after adding them, SDL2 library errors (maybe someone with Docker experience can look into that?).
I’m planning on adjusting the tactical A.I. as a hobby, which is the main reason why I bought the game last Friday. My initial plan is to implement some basic commands that the teamleader can issue: ‘charge’ (go forward and attack on sight), ‘follow’ (current behavior) and ‘stay’ (stay at the current location). This way you can for instance set up your archers at a distance, place some heavy hitters ahead and send an expendible creature to lure an enemy to you. A next step would be some basic formations.
However, I still need to get things compiling (Ubuntu 16.04, the clang and Docker steps gave some errors that I still need to figure out).