CS 470 - Spring 2020.

Final Exam: Friday May 1, 2pm

The final exam will administered remotely. Stay tuned for more information.

Lecture: AI and the New Normal

An adult is someone who looks both ways before crossing the street, and then gets hit by an airplane.

Over the past few weeks, we have had our lives disrupted. I want to take this opportunity to discuss one way to think about your experience from an AI perspective. Specifically, I want to consider the Planning Domain Definition Language (PDDL) model of planning. Here is the shopping plan we looked at earlier.

PlanningProblem(
init='At(Home) & Sells(SM, Milk) & Sells(SM, Banana) & Sells(HW, Drill)',
goals='Have(Milk) & Have(Banana) & Have(Drill)', 
actions=[Action('Buy(x, store)',
           precond='At(store) & Sells(store, x)',
           effect='Have(x)'),
         Action('Go(x, y)',
           precond='At(x)',
           effect='At(y) & ~At(x)')])

The PDDL plan would not work because its assumptions are no longer valid. This is true for much of our lives now. The goals and plans and actions that we took for granted are often no longer valid. We need to revisit our assumptions, abandon our old goals, formulate new plans.

I invite you to think about this in the coming weeks. What plans no longer work? How do you cope? How do you recompute? How would you program a robot to revise its plans? How would a robot benefit from having relationships with other agents? This strikes me as a good take home exam question.

Lecture: Learning

• Learning from Observation
• Supervised Learning
• Genetic Algorithms
• Neural Nets
• Deep Learning

• AIMA chapter 18 6up Learning from Observations
• AIMA chapter 20a 6up Statistical Learning
• AIMA chapter 20b 6up Neural Networks

• Visualization
• distance functions
• plurality learner classifier
• k-nearest neighbors classifier
• decision tree learner
• random forest learner

• naive Bayes learner.
• Perceptron Classifier
• Linear Learner
• Ensemble Learner
• Learner Evaluation
• k-Nearest Neighbor Example
• AdaBoost