I am about to start working on the planning layer of my simulation game. Originally I thought I was going to use a GOAP design. I believe I understand this approach fairly well. But now that MCTS is becoming more popular, I am wondering if I should look into it more...

Here is my original thread that describes my thoughts on using GOAP to design this part of my game:

http://www.gamedev.net/topic/652272-goap-and-modeling-world-state/

Do you think MCTS is a valid design choice? The game also has a complex crafting/production layer that I did not mention in the post.

I wonder which would require the least work? Both right now and after the game gets expanded with more and more features...

Ok. But perhaps we can get into a bit more detail about how the methods differ.

It seems to me that both methods demand a simplified representation of the game space, with actions and estimated state changes from actions.
In the orginal thread I describe how I would represent the game for GOAP. Perhaps this work would be the same for MCTS also?

Also, GOAP requires an end state to search towards, as well as a heuristic function to guide the search.
MCTS does not require an end state, but it does need an evaluator function... how similar would these functions be?

GOAP will only do a single search to formulate a plan. MCTS will do many simulated playthroughs.
If there are no random factors in the simplified representation (for instance if the result of a battle is represented as deterministic), then MCTS and GOAP would look very similar...

GOAP will only do a single search to formulate a plan. MCTS will do many simulated playthroughs.
If there are no random factors in the simplified representation (for instance if the result of a battle is represented as deterministic), then MCTS and GOAP would look very similar...

No, MCTS and GOAP wouldn't look similar at all. MCTS can simultaneously optimize your decisions and the decisions of other agents, so if some plan is easy to stop by an opponent, it will pick a different course of action. For instance, you can use MCTS to play a board game like tic-tac-toe (or go, it doesn't have to be a trivial game), but I don't think GOAP is a reasonable way to go about it. GOAP assumes the agent does things that change the state of the world, but it's not very good at modelling ways in which the world might react to the actions of the agent.

No, MCTS and GOAP wouldn't look similar at all. MCTS can simultaneously optimize your decisions and the decisions of other agents, so if some plan is easy to stop by an opponent, it will pick a different course of action. For instance, you can use MCTS to play a board game like tic-tac-toe (or go, it doesn't have to be a trivial game), but I don't think GOAP is a reasonable way to go about it. GOAP assumes the agent does things that change the state of the world, but it's not very good at modelling ways in which the world might react to the actions of the agent.

So GOAP is perhaps more suited for games without an antagonistic opponent. Like a simulated world where many agents are trying to make a living in the environment but not really interfering with each other. Then MCTS would be better for traditional RTS or war games...

Besides handling randomness more easily than just about any other method, MCTS can also [not so easily] handle uncertainty in the current state of the world. So if your agent is chasing the player and he disappears behind a corner, you can use something like a particle filter to represent your guess for where the player is, and use the particles as starting configurations for your Monte Carlo simulations. If done right, I think this can create very interesting searching behaviors that would be fun to try to defeat.

Comy chair,

I would like to hear more of your game, a thorough description - it sounds pretty interesting and seems to be something similiar to what I am also interested in. If you are not comfortable in public, feel free to email/message me.

Michael