diff --git a/markdown/3-Solving-Problems-By-Searching/exercises/ex_11/answers/answer-flecart-1.md b/markdown/3-Solving-Problems-By-Searching/exercises/ex_11/answers/answer-flecart-1.md
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+---
+Name: Angelo Huang
+Email: angelo.huang.ai@gmail.com
+---
+## Intro
+1. The state of the problem are number of missionaries and cannibals on each side of the river
+This is why i made a structure that takes these data into account. (not sure about the implementation
+of the actions though, because i have to filter it two times)
+
+2. Solve1 is a custom graph-search BFS, i found that the general implementation was present
+at [this](https://github.com/aimacode/aima-python/blob/master/search.py) repo, but it was to late
+so i just used solve2 to see a DFS.
+
+3. Probably because people tend to be frustrated by hard problems and make the same error
+over and over again
+
+## Implementation
+### Program solution
+```python
+# search is the module here
+# https://github.com/aimacode/aima-python/blob/master/search.py
+from search import Problem, Node, depth_first_graph_search
+from queue import Queue
+
+SHIP_SIZE = 2
+
+class RiverSide():
+    """
+    Structure representing the people on one side of the river
+    """
+
+    def __init__(self, missionary=0, cannibals=0, tuple=None):
+        """
+        tuple(missionaries, cannibals) on one riverside
+        """
+        if tuple == None:
+            # security check
+            if missionary < 0 or cannibals < 0:
+                raise ValueError("Coudn't have negative missionaries or cannibals")
+
+            self.missionaries = missionary
+            self.cannibals = cannibals
+        else:
+            # security check
+            if tuple[0] < 0 or tuple[1] < 0:
+                raise ValueError("Coudn't have negative missionaries or cannibals")
+
+            self.missionaries = tuple[0]
+            self.cannibals = tuple[1]
+        self.shipSize = SHIP_SIZE
+
+    def isValid(self):
+        """
+        Check if current state is valid
+        """
+        # check if one or more missionaire is overwhelmed by a cannibal
+        if self.cannibals > self.missionaries and self.missionaries > 0:
+            return False
+
+        return True
+
+    def set(self, tuple):
+        self.missionaries = tuple[0]
+        self.cannibals = tuple[1]
+
+    def action(self):
+        """
+        Returns possible ship transportations, by ship size.
+        """
+        # Tuples of (missionary, cannibals)
+        actions = list()
+        for i in range(self.missionaries + 1):
+            for j in range(self.cannibals + 1):
+                if i + j != 0 and i + j <= self.shipSize:
+                    actions.append((i,j))
+
+        return actions
+
+    def __eq__(self, other):
+        if self.missionaries == other.missionaries and self.cannibals == other.cannibals:
+            return True
+        else:
+            return False
+
+    def __sub__(self, other):
+        missionaries = self.missionaries - other.missionaries
+        cannibals = self.cannibals - other.cannibals
+        if missionaries < 0 or cannibals < 0:
+            raise ValueError("Coudn't have negative missionaries or cannibals")
+        return RiverSide(missionaries, cannibals)
+
+    def __add__(self, other):
+        missionaries = self.missionaries + other.missionaries
+        cannibals = self.cannibals + other.cannibals
+        return RiverSide(missionaries, cannibals)
+
+    def __str__(self):
+        return f"Riverside with {self.missionaries} missionaries and {self.cannibals} cannibals"
+
+    def __repr__(self):
+        return f"{self.missionaries} {self.cannibals}"
+
+    def __hash__(self):
+        return hash((self.missionaries, self.cannibals))
+
+class Mc(Problem):
+    def __init__(self, initial):
+        self.state = (RiverSide(initial, 0), RiverSide(0, initial))
+        # i needed this to make solve2 work, compatibility stuff
+        self.initial = self.state
+        self.goalState = (RiverSide(0, initial), RiverSide(initial, 0))
+
+    def actions(self, state):
+        """
+        The second member of the tuple is direction, 0 is from left to right, 1 is from right
+        to left, the first is the possible ship setup by missionaries and cannibals.
+        Data like this:
+        ((missionaries, cannibals), 0)
+        """
+        act = list()
+        for sideAction in state[0].action():
+            act.append(((sideAction), 0))
+        
+        for sideAction in state[1].action():
+            act.append(((sideAction), 1))
+
+        return act
+
+    def result(self, state, action):
+        sideAction, code = action
+        sideAction = RiverSide(tuple=sideAction)
+        leftRiver, rightRiver = state
+
+        if code == 0:
+            state = (leftRiver - sideAction, rightRiver + sideAction)
+        else:
+            state = (leftRiver + sideAction, rightRiver - sideAction)
+
+        # check if these results are valid:
+        # print(state, f"and valid check is { state[0].isValid() and state[1].isValid()}")
+        if state[0].isValid() and state[1].isValid():
+            return state
+        else:
+            return (leftRiver, rightRiver)
+
+    def goal_test(self, state):
+        if state == self.goalState:
+            return True
+        
+        return False
+
+    def solve(self):
+        """
+        solving using BFS
+        """
+        # initial parameters
+        frontier = Queue()
+        explored = set()
+
+        currentNode = Node(self.state)
+        frontier.put(currentNode)
+        explored.add(currentNode)
+
+        while True:
+            if frontier.empty():
+                print("no solution")
+                return False
+            currentNode = frontier.get()
+            if self.goal_test(currentNode.state):
+                print("found solution")
+                print(currentNode.solution())
+                return True
+            explored.add(currentNode)
+
+            for node in currentNode.expand(self):
+                if node not in explored:
+                    frontier.put(node)
+    
+    def solve2(self):
+        solNode = depth_first_graph_search(self)
+        if solNode != None:
+            print(solNode.solution())
+
+def main():
+    # REGION TEST
+    # print("hello, these are some tests")
+    # print(RiverSide(0,0) == RiverSide(0,1))
+
+    # # test __init__ clas
+    # a = RiverSide(1,1)
+    # b = RiverSide(tuple=(1,2))
+
+    # # test string rapresentation
+    # print(a)
+    # print(b)
+
+    # # addition and subtraction
+    # print(a + RiverSide(2, 3))
+    # print(a - RiverSide(0, 1))
+    # ENDREGION
+    
+    # testing problem and solving it
+    probbi = Mc(3)
+    probbi.solve2()
+
+if __name__ == "__main__":
+    main() 
+```
diff --git a/markdown/3-Solving-Problems-By-Searching/exercises/ex_18/answers/answer-flecart-1.md b/markdown/3-Solving-Problems-By-Searching/exercises/ex_18/answers/answer-flecart-1.md
new file mode 100644
index 0000000000..441c2e389c
--- /dev/null
+++ b/markdown/3-Solving-Problems-By-Searching/exercises/ex_18/answers/answer-flecart-1.md
@@ -0,0 +1,21 @@
+---
+Name: Angelo Huang
+Email: angelo.huang.ai@gmail.com
+---
+1: ![Image of the graph](https://i.imgur.com/XGUXMJ5.jpg)
+2:
+BFS: 1-2-3-4-5-6-7-8-9-10-11
+DFS: 1-2-4-8-9-5-10-11
+DLS: 8-9-4-2-5-10-11
+3: Bidirectional search would do quite well, the two directions could meet at node 5 or 2. Branching factor of this problem is 2 as every node except node 1 or leaves are related to 3 nodes.
+4: Yes, starting from the goal is a lot easier, we just need to decompose the goal number with the following algorithm
+5: start from the goal node.
+
+solution <- empty_list
+while goal != 1
+do
+if goal is even then put Left into solution; goal <- goal/2
+else if goal is odd then put Right into solution; goal <- (goal-1)/2
+finish
+reverse order of solution
+print solution