Evaluating Vertices Efficiently.

Sup /prog/,

I'm currently doing an AI hobby project and have hit a wall in progress due to my own stupidity.

I'm using Dijkstra's to search the tree. This of course requires Edge values, which, in this case, are decided by changes in state of Vertices [y=V(n)-V(n+1). Each specific change of state [x->x+1] being associated with an Edge. [y=Vn(x)-Vn+1(x+1)]

The specific issue I'm having is figuring out how much each node should be worth. I could arbitrarily assign values to each variable 'each x is worth y', but that feels rather naive. Would arbitrarily assigning values initially, and then using a heuristics to optimize on known test cases to assign 'better' values, be a good way of doing things?

Is there a better way of doing this?

The non-trivial nature of the tree means that an exhaustive search isn't possible, hence why I'm trying to optimize where I search as much as possible. This is going to be used to a) figure out what I should do and b) figure out what my opponent might do, so it's quite important that I get this algorithm spot on.

I'm using Dijkstra's to search the tree.

That's funny because the Dijkstra's algorithm finds the cheapest/shortest path between two vertices in a graph, while in a tree there exists only one path between any two vertices.

OP
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mailto:noko
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my own stupidity.
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>>2
Sorry, I should've used 'a Dijkstra's type algorithm to search the tree'. If you read on, you'd be able to see why I chose that rather than something else.

>>5
In case you haven't realised yet, you're making a fool out of yourself with your imageboard nokos et al.

So do I read you right in that you're confused as to whether you want A* or minmax, but it doesn't matter anyway, because you don't have a heuristic?

>>1
Please read “Artificial Intelligence: A Modern Approach”, then come back and explain us what it is you're trying to do in a terminology we can understand.

>>7
A* would be applicable if I knew the optimal end state, I only have a notion of it.
Doesn't minmax require a zero-sum game?

>>8
* Each Edge is an 'action' and has a value associated with it.
* The action's value is generated by the change in value of the parent node and child node.
* The node's value is generated by its constituent variables. Some variables are only altered by actions. Others are altered by time.
* The depth of the search is limited by real time.
* Hence why I want best out.

The question I had was specifically about generating the value for each node. I was specifically asking about a non-naive way of generating it.

Just assign each node a value of 1, the f(n) = g(n) + h(n) works out everything. If you have hard to pass objects then increase the node value for that region.

Also you can plot heat maps around a goal with successive reduced node values.

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