diff --git a/.travis.yml b/.travis.yml
@@ -14,6 +14,7 @@ install:
   - pip install matplotlib
   - pip install networkx
   - pip install ipywidgets
+  - pip install Pillow
 
 script:
   - py.test

diff --git a/README.md b/README.md
@@ -90,7 +90,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
 | 6      | CSP                               | `CSP`                         | [`csp.py`][csp]                 | Done | Included |
 | 6.3    | AC-3                              | `AC3`                         | [`csp.py`][csp]                 | Done |          |
 | 6.5    | Backtracking-Search               | `backtracking_search`         | [`csp.py`][csp]                 | Done | Included |
-| 6.8    | Min-Conflicts                     | `min_conflicts`               | [`csp.py`][csp]                 | Done |          |
+| 6.8    | Min-Conflicts                     | `min_conflicts`               | [`csp.py`][csp]                 | Done | Included |
 | 6.11   | Tree-CSP-Solver                   | `tree_csp_solver`             | [`csp.py`][csp]                 | Done | Included |
 | 7      | KB                                | `KB`                          | [`logic.py`][logic]             | Done | Included |
 | 7.1    | KB-Agent                          | `KB_Agent`                    | [`logic.py`][logic]             | Done |          |

diff --git a/csp.ipynb b/csp.ipynb
diff --git a/images/queen_s.png b/images/queen_s.png
diff --git a/notebook.py b/notebook.py
@@ -9,6 +9,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from PIL import Image
 
 import os, struct
 import array
@@ -1041,4 +1042,31 @@ def visualize_callback(Visualize):
 
         slider = widgets.IntSlider(min=0, max=1, step=1, value=0)
         slider_visual = widgets.interactive(slider_callback, iteration=slider)
-        display(slider_visual)
+        display(slider_visual)
+
+
+# Function to plot NQueensCSP in csp.py and NQueensProblem in search.py
+def plot_NQueens(solution):
+    n = len(solution)
+    board = np.array([2 * int((i + j) % 2) for j in range(n) for i in range(n)]).reshape((n, n))        
+    im = Image.open('images/queen_s.png')
+    height = im.size[1]
+    im = np.array(im).astype(np.float) / 255
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(111)
+    ax.set_title('{} Queens'.format(n))
+    plt.imshow(board, cmap='binary', interpolation='nearest')
+    # NQueensCSP gives a solution as a dictionary
+    if isinstance(solution, dict):
+        for (k, v) in solution.items():
+            newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
+            newax.imshow(im)
+            newax.axis('off')
+    # NQueensProblem gives a solution as a list
+    elif isinstance(solution, list):
+        for (k, v) in enumerate(solution):
+            newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
+            newax.imshow(im)
+            newax.axis('off')
+    fig.tight_layout()
+    plt.show()