Recognize problems that can use AI methods
from business, medicine, robotics, gaming, and machine learning
Identify the component elements of the problem
Does it require basic control?
Does it involve uncertain reasoning?
Does it have a simple goal, sophisticated utility, or multiple attributes?
Does it require sequential choice or planning?
How may decision makers are involved?
Formalize the problem in a way that is amenable to a solutio
What is the internal representation?
What performance criteria are relevant?
What is the nature of the environment?
What are the actuators?
What are the sensors?
What are the agent's utilities?
How is uncertainty represented?
Be able to analyze, implement, and experiment with several A
Uninformed, informed, constraint-satisfaction, hill-climbing, and stochastic (e.g., genetic algorithms) search
Markov provesses, Bayes rule, Bayes nets, Hidden Markov Models, Grid Filters, and Kalman Filters
Expected utility theory
Multi-attribute utility theory
Sequential choice under uncertainty: value and policy iteration
Minimax solutions from game theory: alpha-beta pruning
Analyze and communicate solution quality
Determine whether your solution is correct
Express the quality of your solution
Value the need to communicate your solution to others
