State True or False

$1.$ A simple$-$reflex agent will maximise the expected performance measure in a partially observable environment.

$2.$ In a deterministic environment, if you are in state S$0$ and you perform action A$,$ the probability of reaching the next state S$1$ is $1.$

$3.$ Breadth$-$first search $($BFS$)$ with repeated state check can guarantee to find the optimal solution.

$4.$ Greedy Best First Search $($GBFS$)$ with an admissible heuristic function can guarantee to find the optimal solution.

$5.$ Alpha$-$beta pruning with a heuristic evaluation function yields an optimal playing strategy against an optimal opponent.