1. CSC 110 - Intro. to Computing Lecture 22: Artificial Intelligence

2. Knowledge Representation As video hinted at, AI has big problem representing knowledge  Must also be precise enough to capture information  But must be able to handle ambiguity  Consider the following sentence: Buffalo buffalo buffalo Buffalo buffalo

3. Semantic Network One method of representing knowledge Focuses on relationship between items Typically shown as a directed multi-graph  Vertices (points) represent the data items Each data item should be a simple noun  Edges (lines) are relationships between items Each edge is usually a verb Relationships go in one direction (shown as arrow) Items can have multiple relationships

4. Semantic Network Example Nosipho is a student. Nosiphostudent instance of

5. Semantic Network Example Students are people. Nosiphostudent Students are people. person is a instance of

6. Semantic Network Example Students go to school. Nosiphostudent Students go to school. person is a school go to instance of

7. Semantic Network Example Nosiphostudent person is a school go to Each person has a gender. gender has a instance of

8. Semantic Network Example Nosiphostudent person is a school go to Nosipho is female gender has a female instance of

9. Semantic Network Example Nosiphostudent person is a school go to Hillary Clinton is also female gender has a female Hillary Clinton instance of

10. Semantic Network Example Nosiphostudent person is a school go to Females are people. gender has a female Hillary Clinton type of instance of

11. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Dog is not a Nosipho is a person is not a type of

12. instance of Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Nosipho is a person Dog is not a type of

13. instance of Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Nosipho is a person Dog is not a type of

14. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Hillary Clinton is not a dog Dog is not a type of

15. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Hillary Clinton is not a dog Dog is not a type of

16. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Dog is not a type of

17. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Dog is not a What is Nosipho’s gender? is not a type of

18. Semantic Network Example Nosiphostudent person is a school go to gender has a female Hillary Clinton instance of Dog is not a What is Nosipho’s gender? is not a type of

19. Semantic Networks Good for problems asking relationships between data Bad for playing checkers or chess  Checkers has 10 9 different states to express Difficult, but doable  Chess has 10 43 different possible states Lot of relationships to express  Big jump from knowing how horsey moves to winning a chess match

20. Search Trees Consider game of Nim  Players take turns place 1 – 3 marks on board  Whomever marks last slot, wins “Best” move can be hard  Need to know what your opponent will do  Then depends upon how you move and your opponents response to the move, and so on

21. Search Trees Following a line from one state to another is like making a move Shows all possible games of Nim

22. Search Tree Limits Search trees really used to play games  Provides easy method of solving problem But evaluating entire tree is difficult  Checkers: 10 9 different possible game states  Chess: 10 43 different game states  Evaluating entire tree could require years… Need to re-evaluate tree for each move.  Cannot even store entire tree on a computer!

23. Tree Searches Depth-first: Could I possibly win/lose if I make this move? Breadth-first: Which move will put me in the best position in 3 rounds?

24. Search Trees

25. Computer vs. Human Humans are big and slow  Have 100 billion neurons  Neurons have many inputs  Neurons “run” at 100 Hz Computers are small and fast  Have 100 million gates  Gates combine 1 or 2 inputs  Gates run at 1,000,000,000 Hz

26. Neural Networks Simulate how a biological neural network learns  How it ultimately makes decisions often does not make sense to people Each artificial neuron assigns numerical weight to each input  “Learns” by adjusting these weights

27. Artificial Neuron Operation Effective weight of an input is the input value multiplied by input’s weight If sum of effective weights is above threshold neuron outputs 1  When sum of effective weights is less than the threshold, neuron outputs 0

28. Artificial Neuron Example Consider the following neuron: Value to compare against threshold: ValueWeightEffective Weight 10.3 1-3 02 14

29. Artificial Neural Network Train neural network on variety of input values  When output is correct, share reward (increase weight of active inputs)  When output is wrong, “punish” network (decrease weight of inputs)  Apply occasional “reverse brain damage”