Machine learning involves developing systems that can learn from data and experience over time to improve performance on tasks. The document discusses several machine learning approaches including version space learning, explanation-based learning, evolutionary algorithms, and using a genetic algorithm to solve Boolean satisfiability problems expressed in conjunctive normal form (CNF).

2. What Is Machine Learning? “Logic is not the end of wisdom, it is just the beginning” --- Spock System time Knowledge Environment Action 1 Knowledge Environment System changed same Action 2

5. How Version Space Works + + + + + + + + − − If we consider only positives If we consider positive and negatives + + + + + + + + − − What is the role of the negative instances? to help prevent over-generalizations

6. Explanation-Based learning A C B A B C A C B C B A B A C B A C B C A C A B A C B B C A A B C A B C A B C Can we avoid making this error again? ? ? ?

7. Explanation-Based learning (2) A C B A B C C B A A C B A B C ? ? ? More sensible rule: don’t stack anything above a block, if the block has to be free in the final state Possible rule: If the initial state is this and the final state is this , don’t do that

10. Example: CNF-satisfaction A c onjunctive n ormal f orm (CNF) is a Boolean expression consisting of one or more disjunctive formulas connected by an AND symbol (  ). A disjunctive formula is a collection of one or more (positive and negative) literals connected by an OR symbol (  ). Example : (a)  ( ¬ a  ¬ b  c  d)  ( ¬ c  ¬ d)  ( ¬ d) Problem (CNF-satisfaction): Give an algorithm that receives as input a CNF form and returns Boolean assignments for each literal in form such that form is true Example (above) : a  true, b  false, c  true, d  false