Updated theory with formatting

Author: mrudhvika940

experiment/theory.md

@@ -65,7 +65,6 @@ For further information, refer the references given in the references section fo
 
 
 
-
 **Figure 3**: *The state of the CS model after each cycle, starting with an initial state where the units 'ceiling' and 'oven' are clamped.*
 
 ### Illustration of Constraint Satisfaction Model
@@ -74,26 +73,39 @@ In this experiment, we use the PDP model called the Constraint Satisfaction Mode
 
 <img src="images/CSNN_1.png">
 
+
+
 **Figure 1**: *Experiment window showing descriptors and other buttons.*
 
 The constraint satisfaction model which is illustrated in this experiment is already trained. We can view the model using Hinton Diagram by clicking on the "CLICK" button on top. A new panel showing the Hinton Diagram for all the descriptors appears where 40 hypotheses have their corresponding squares with constraints between them and other hypotheses being displayed within the larger square. Moving the mouse over the squares presents their zoomed picture at the bottom of the page.
 
 
+
 <img src="images/CSNN_2.png">
 
 
+
 **Figure 2**: *Experiment panel showing the Hinton diagram for the descriptors.*
 
 In the above diagram we notice that each large square represents a unit. There are 40 units corresponding to 40 descriptors. Within the square of each unit a replica of all the 40 units are displayed as smaller squares or dots, each dot representing the unit in its relative position in the diagram. Around each dot, weak constraints are depicted by smaller squares in lighter gray shade. Similarly strong constraints is represented by darker gray or black color with a square of larger size. There are many units which have no connections at all.
 
 The constraint satisfaction model is used to illustrate the ability of human beings to build concepts or arrive at conclusion based on partial and sometime erroneous knowledge. The key idea of the model is that a large number of weak constraints when put together, evolve into a definitive conclusion. To test the model, some units can be clamped or say provided a constant external bias. As shown in the figure below, we can click on the units and have these clamped.
 
+
+
 <img src="images/CSNN_3.png">
 
+
+
+
 **Figure 3**: *Testing of the model by clamping some units.*
 
 After clamping the model is allowed to relax by computing the next state for each unit selected at random. The state computation is done by computing sum of the weighted inputs and thresholding the weighted sum using hard-limiting output function. After we click the test network button, we find that after running through some iterations the network settles to a state where no further change in the state of descriptors is visible. And then the state description gives the description of the concept of the room satisfying the external evidence or clamping
 
+
+
 <img src="images/CSNN_4.png">
 
+
+
 Figure 4: Figure showing the states of descriptors after reaching equilibrium.