Nishchal Dwivedi

Ujaan Rakshit, Nishchal Dwivedi

This work delves into the realm of logic puzzles by focusing on the Knight and Knave problems popularized by Raymond Smullyan in his book series "What is the Name of This Book?". The puzzles revolve around characters known as Knights (truth-tellers) and Knaves (liars), challenging solvers to determine the true identity of each person based on their statements. This paper explores the utilization of Python algorithms to automate the process of solving these puzzles, offering a computational approach that enhances efficiency and accessibility. In this work, we aim to develop a Python algorithm capable of parsing and analyzing the statements provided in the Knight and Knave puzzles. A logical reasoning framework is integrated within the algorithm to deduce the identities of the characters based on their statements. The algorithm processes the input statements, create a knowledge base, and make deductions following the rules of Knight and Knave logic. The developed algorithm is thoroughly tested on various instances of Knight and Knave puzzles, comparing its results to known solutions and manual approaches. We further expand the scope of the problem by introducing a Normal (who can sometimes lie and sometimes say the truth).

Yashvir Tiberwal, Nishchal Dwivedi

This research paper focuses on the implementation of radial Basis Function (RBF) Support Vector Machines (SVM) for classifying asteroid orbits. Asteroids are important astronomical objects, and their orbits play a crucial role in understanding the dynamics of the solar system. The International Astronomical Union maintains data archives that provide a playground to experiment with various machine-learning techniques. In this study, we explore the application of RBF SVM algorithm to classify asteroids. The results show that the RBF SVM algorithm provides a good efficiency and accuracy to the dataset. We also analyze the impact of various parameters on the performance of the RBF SVM algorithm and present the optimal parameter settings. Our study highlights the importance of using machine learning techniques for classifying asteroid orbits and the effectiveness of the RBF SVM algorithm in this regard.

Shreyan Deo, Nishchal Dwivedi

This work focuses on the analysis of Chess 960, also known as Fischer Random Chess, a variant of traditional chess where the starting positions of the pieces are randomized. The study aims to predict the game outcome using machine learning techniques and develop an opening theme for each starting position. The first part of the analysis utilizes machine learning models to predict the game result based on certain moves in each position. The methodology involves segregating raw data from .pgn files into usable formats and creating datasets comprising approximately 500 games for each starting position. Three machine learning algorithms -- KNN Clustering, Random Forest, and Gradient Boosted Trees -- have been used to predict the game outcome. To establish an opening theme, the board is divided into five regions: center, white kingside, white queenside, black kingside, and black queenside. The data from games played by top engines in all 960 positions is used to track the movement of pieces in the opening. By analysing the change in the number of pieces in each region at specific moves, the report predicts the region towards which the game is developing. These models provide valuable insights into predicting game outcomes and understanding the opening theme in Chess 960.