Raven's Progressive Matrices are a set of visual problems commonly used to measure intelligence. The goal is to design an agent that can solve these problems just like humans do. To design such an agent, I will use a combination of semantic networks and generate and test. Semantic networks are a form of knowledge representation that consists of nodes, links, and link labels (Winston, 1977, p. 19). An agent can use this problem representation to discern what the missing figure is. To start, the agent needs to represent each of the given digits, then use generate and test to create what the answer should be assuming the same transformations apply. The response choice most similar to the generated figure will be the one chosen by the agent as the response. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay This problem is difficult because there is no "correct" way to represent a Raven's Progressive Matrix problem with semantic networks. Figure 1. Challenge Problem D-12 (Raven, J. 2003, p. 235)For example, in the image above of Challenge Problem D-12, it is difficult to initially see what needs to be represented. It could be the name of the shape, the orientation of the objects in relation to each other, the number of objects, and many other possibilities. A simple semantic network to represent the first row transformation can be: A: x top left of y B: x top of y, y top of z C: x top left of y, y left of z , z bottom left of w Transformation from A to B: x remains the same, a new z on which y is above Transformation from B to C: x remains the same, y remains the same, a new w on which z is above Another one that might work could be:A: x equals y B: x equals y, y equals z C: x equals y, y equals z equals transformation from wA to B: the relationship between x and y is the itself, a new z that maintains this relationship. x,y,z all have different shapes compared to the transformation from x,yB to C in the last figure: the relationship of x and y and z is the same, a new w that makes this one go forward too. x,y,z,w all have different shapes than x,y,z in the last figure. These 2 knowledge representations can lead to different answers when applied to the last line:G: x to the top left of y, top right of z H: x to the top left of y, z to the top right of y , w at the top right of zG in transformation H: new z at the top left of y, new w at the top right of zH in ? transformation: new shape a at the top left of z, new shape b at the top right of a. Using the second rule: G: x has the same shape as y, y has the same shape as z H: x has the same shape of y, y has the same shape as z, z same shape as the transformation from wG to H: new shapes z, which has the same shape as y, and new shape w equals z. Do the new x,y,z,w also have a different shape from x,yH a ? transformation: new form a, which is equal to z, and new form b, which is equal to a. The new a,b,x,y,z,w also have a different shape than x,y,z,w in the last figure. The first rule will allow you to choose any answer choice that satisfies the H to ? transformation rule, which is based on the spatial relationships between the new shapes, while the second rule will allow you to choose answers based on the identity of the shapes and how they are different from the last figure. Both of these rules unfortunately do not solve this question, which shows that there may be rules that seem to explain the transformations but are incorrect. Generate and Test mitigates this problem by generating all possible transformations and testing each case to see which one.