Developed a suite of AI solutions encompassing various facets of artificial intelligence, including search algorithms, game playing strategies, and probabilistic inference systems using Python.

● Uninformed & Informed Search: Developed a state space search program to find optimal routes between cities. The program handles both uninformed and informed search based on the availability of heuristic data. Key outputs include the route length, list of cities on the route, and the number of nodes expanded and generated during the search process.

● Game Playing Problems: Built an intelligent agent to play the red-blue nim game (standard and misère versions) against a human player. The agent utilizes MinMax search with Alpha-Beta Pruning to determine optimal moves. The program alternates turns between the computer and the human player until the game concludes, then calculates and displays the winner and their final score based on the number of remaining marbles.

● Posterior Probabilities: Created a system to compute posterior probabilities of various hypotheses given prior probabilities and a sequence of observations. The system can also predict the likelihood of the next observation being of a specific type based on the given data.

● Bayesian Networks: Implemented a program to calculate and print the probability of any combination of events given any other combination of events using Bayesian networks. This involved handling complex probabilistic inferences and dependencies between events.

Skills: Python (Programming Language) · Artificial Intelligence (AI) · Probability and Statistics · Bayesian networks · Problem Solving and Analytical Skills · Data Structures