Foundations of A.I.: Search Algorithms

We explore where artificial intelligence is embedded in daily life, from self-driving cars and Alexa to face recognition, chatbots, recommender systems, and deepfakes.

Explore four schools of AI definitions—thinking rationally, thinking like humans, acting rationally, and acting like humans—with examples like Google Maps, Siri, and Roomba, to reveal what AI truly is.

Identify the factors of rationality for an agent in a running race by defining a performance measure and percept sequence; an ideal agent maximizes performance using prior knowledge and actions.

Explore how goal based agents perceive the environment with sensors, build a state model, and choose actions that move them closer to a goal.

Learn about data types and how to classify values, focusing on six data types, including numerical lists, strings, set, tuples and dictionary, and the operations they support.

Guides using if else statements in a Jupyter notebook, reading two numbers, testing positivity or negativity, and practicing divisibility and even-odd checks with three activities.

Explain how artificial intelligence solves problems by contrasting reflex and planned agents, and show how a goal-based agent selects an optimal sequence to bridge the gap to the goal.

Implement depth-first search in Python using a stack, an adjacency list, and a recursive approach to trace a path from a start node to a goal.

Explore iterative deepening depth-first search, gradually increasing depth limits to find a goal with finite branching factor and optimality, while noting repeated state visits and depth limits.

Uniform cost search explores a weighted graph by expanding the least-cost node using a priority queue, yielding a path that is optimal in terms of cost.

Learn how the hill climbing algorithm maximizes an objective function by moving to better neighbors until reaching a peak, with random start and no memory of past moves.

Explore stochastic hill climbing, first choice hill climbing, and random restart hill climbing, using random neighbor evaluation and first better value to avoid local maxima; simulated annealing offers an alternative.