Sukeerthi_KBAI_Project1.pdf - 1 Project 1 Reflection...

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1 Project 1 Reflection Sukeerthi Varadarajan [email protected] Question 1 How does the agent work? The agent solves the problem by representing the objects as sentences in a Semantic Network. We then apply Generate and Test to compare each of the answer options as figure D and then comparing the transformations and alignments between figures A, B, C and D, i.e. A to B, C to D and so on. Then the agent compares the pairs thus created to arrive at the answer. As there are six potential answers to the question, we use similarity scores to pick the best match. The greater the similarity, the better the match. Question 2 What was your approach towards solving the problem? Initially we started by solving the basic problems B-01 and B-02, which are easier to solve. Once we created the agent to solve the easier problems, we then tweaked the agent to answer further question. As the problems progressed, there are more transformations happening to figure D, so we added different transformation weights to help the agent identify the correct answer from the given options. Figure 1. Increasing complexity of the Basic Problems
2 The agent uses similarity metrics to score each of the options. The option with the highest similarity score is chosen as the answer. The transformations are recorded and stored in the transforms dictionary, along with the similarity scores for each of the correct transformations.

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