And the goal state is right here.

If there was a single goal state, we could say what other arcs are coming into that goal state.

I now can test whether this state is my final goal state.

And notice that that's the complete goal state.

So we take the description of the goal state.

Suppose the goal state has a value of 100.

The start state is here, and the goal state is outlined in gray here.

And we said one way to decide if this is a plan that satisfies the goal is to say, Is the end state a goal state?

Say the value of the goal state is defined to be zero.

Say our goal state is the one in the corner over here.

As before, I give you a goal state and a set of actions.

The third one is it might actually fail to reach the goal state.

Next, we need a function called Goal Test, which takes a state and returns a Boolean value true or false telling us if this state is a goal or not.

C1 is at JFK and C2 is at SFO, so that's the goal state.

Now we can check to see if we can achieve the goal with this two step plan just by checking if there is an intersection between the reachable state and the goal state.

I happen to know in the goal state that the action was a down action.

You did Shin Moonsun Goal! Goal!

If we have two entities concatenated, we're going to have to go from the current state we're at to some middle state, and then lastly, to the goal state.

In doing so the first time I do this, x and y was the goal state, and x2 y2 become the state before.

Consider the following 3 x 3 world with a goal state in the corner over here.

Remember we can slide around these little tiles and we try to reach a goal state.

Draw goal

Goal Seek

But here, this goal state doesn't represent a single state it represents a whole family of states with different values for all the other variables.

One is it always reaches the goal state and does so in a bounded number of steps.

When we have an impasse we can pick a node that looks closer to the goal state.

So we'll say that this is the goal state, where the numbers 1 15 are in order

Consider this search space. We have a start state and a goal, and there's an impassable barrier.

Assume again the cost is 1, and it's our sole goal state in the corner over here.

In this state the agent has a clear path to the goal, so that's a good situation.

We had a state, like state A, and then we transitioned to another state, like state B, and all we could say about those states was are they identical to each other or not and maybe is one of them a goal state or not.

let's say that the state of being in Bucharest is the only state that counts as a goal, and all the other states are not goals.

Our goal is to make a state of the art product that our partner MTTS can actually manufacture.

Our goal is to make a state of the art product that our partner MTTS can actually manufacture.

Given a state space and a problem description, we can find a solution, a path to the goal.

Now in my dynamic programming procedure check for whether we have reached the goal state, and we have.

State beach, state case.

let's say, goal.

That's my goal.

What's the goal?

Oh, nice goal!

It turns out the start state is 14 steps away from the goal, so 100 minus 14 is 86.

The second possibility is always reaches the goal state, but the number of steps cannot be bounded in advance.

If the goal state is among them, it'll succeed to find a path. Therefore this answer here was wrong.

Without proof, the g value when I hit the goal node will be exactly the number of steps it takes to go from the start state to the goal node.