Welcome to the AI Assignments repository! This collection of assignments covers a variety of AI topics, including greedy algorithms, game AI, SAT solvers, and Bayesian networks. Each assignment is designed to provide hands-on experience with key AI concepts and techniques.
- Greedy Hill Climbing
- Rollerball AI Bot (Assignment 1)
- SAT Solver
- Learning Bayesian Networks
- Rollerball AI Bot (Assignment 2)
Description:
This assignment focuses on the Greedy Hill Climbing algorithm, a simple yet powerful technique for solving optimization problems. You'll implement the algorithm and apply it to a specific problem, analyzing its strengths and weaknesses, and exploring how it can be enhanced to avoid local maxima.
Files:
2021CS10915XX
Description:
In this assignment, you will develop an AI bot for the Rollerball game. The bot will use basic AI techniques to navigate the game environment, make decisions, and aim to maximize its score. This is the first of two assignments focused on game AI, with a particular emphasis on state-based decision-making.
Files:
2021CS10915XX
Description:
This assignment involves implementing a SAT solver, a fundamental tool in AI for solving Boolean satisfiability problems. You'll learn how to encode problems into CNF (Conjunctive Normal Form) and apply techniques such as DPLL (Davis-Putnam-Logemann-Loveland) to solve them.
Files:
2021CS10915XX
Description:
This assignment covers the theory and practice of learning Bayesian networks, a powerful tool for representing and reasoning under uncertainty. You'll work on learning the structure of Bayesian networks from data and learning missing parameters, gaining insight into probabilistic graphical models.
Files:
2021CS10915XX
Description:
This is a continuation of the Rollerball AI Bot assignment, where you'll enhance your previous implementation with more advanced AI techniques. The focus will be on improving the bot's performance through optimization and the incorporation of machine learning methods.
Files:
2021CS10915XX