From 6558576869b6bac086ac5b32d898dfebfc53ecbd Mon Sep 17 00:00:00 2001
From: Ali Dumlu <57403176+AliDumlu@users.noreply.github.com>
Date: Thu, 12 Sep 2024 09:12:13 +0300
Subject: [PATCH] Fix image URLs in vacuum_world.ipynb by removing 'files/'
 prefix

This commit fixes broken image URLs in the vacuum_world.ipynb notebook. The issue was caused by an incorrect prefix ('files/') in the image paths, which prevented the images from being displayed properly. I have removed the 'files/' prefix from all image URLs, ensuring that the images load as expected.

Changes made:
- Updated all image paths in vacuum_world.ipynb by removing 'files/' from the URLs.
---
 vacuum_world.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/vacuum_world.ipynb b/vacuum_world.ipynb
index 6b05254c7..988150a50 100644
--- a/vacuum_world.ipynb
+++ b/vacuum_world.ipynb
@@ -563,7 +563,7 @@
     "A model-based reflex agent maintains some sort of **internal state** that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state. In addition to this, it also requires a **model** of the world, that is, knowledge about \"how the world works\".\n",
     "\n",
     "The schematic diagram shown in **Figure 2.11** of the book will make this more clear:\n",
-    "<img src=\"files/images/model_based_reflex_agent.jpg\">"
+    "<img src=\"images/model_based_reflex_agent.jpg\">"
    ]
   },
   {
@@ -650,7 +650,7 @@
     "A goal-based agent needs some sort of **goal** information that describes situations that are desirable, apart from the current state description.\n",
     "\n",
     "**Figure 2.13** of the book shows a model-based, goal-based agent:\n",
-    "<img src=\"files/images/model_goal_based_agent.jpg\">\n",
+    "<img src=\"images/model_goal_based_agent.jpg\">\n",
     "\n",
     "**Search** (Chapters 3 to 5) and **Planning** (Chapters 10 to 11) are the subfields of AI devoted to finding action sequences that achieve the agent's goals.\n",
     "\n",
@@ -659,7 +659,7 @@
     "A utility-based agent maximizes its **utility** using the agent's **utility function**, which is essentially an internalization of the agent's performance measure.\n",
     "\n",
     "**Figure 2.14** of the book shows a model-based, utility-based agent:\n",
-    "<img src=\"files/images/model_utility_based_agent.jpg\">"
+    "<img src=\"images/model_utility_based_agent.jpg\">"
    ]
   },
   {
@@ -673,7 +673,7 @@
     "A learning agent can be divided into four conceptual components. The **learning element** is responsible for making improvements. It uses the feedback from the **critic** on how the agent is doing and determines how the performance element should be modified to do better in the future. The **performance element** is responsible for selecting external actions for the agent: it takes in percepts and decides on actions. The critic tells the learning element how well the agent is doing with respect to a fixed performance standard. It is necesaary because the percepts themselves provide no indication of the agent's success. The last component of the learning agent is the **problem generator**. It is responsible for suggesting actions that will lead to new and informative experiences.  \n",
     "\n",
     "**Figure 2.15** of the book sums up the components and their working:  \n",
-    "<img src=\"files/images/general_learning_agent.jpg\">"
+    "<img src=\"images/general_learning_agent.jpg\">"
    ]
   }
  ],