Basic planning algorithm by Ricky Ho.
From the post:
Planning can be think of a Graph search problem where each node in the graph represent a possible “state” of the reality. A directed edge from nodeA to nodeB represent an “action” is available to transition stateA to stateB.
Planning can be thought of another form of constraint optimization problem which is quite different from the one I describe in last blog. In planning case, the constraint is the goal state we want to achieve, where a sequence of actions need to be found to meet the constraint. The sequence of actions will incur cost and our objective is to minimize the cost associated with our chosen actions.
Makes me curious about topic maps that perform merging based on the “cost” of the merge.
That is upon a query, an engine may respond with a merger of topics found on one node but not request data from remote nodes.
In particular thinking of network performance issues which we all experience, waiting for ads to download for example.
Depending upon my requirements, I should be able to evaluate those costs and avoid them.
I may not have the most complete information but that may not be a requirement for some use cases.