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Wrap-up First Unit Motivating Logic in AI

Wrap-up First Unit Motivating Logic in AI. Exam #1. My general impression as I graded the exams: You understood the general idea(s) but not the specifics/details You were able to say things that made sense, but you couldn’t identify the key ideas that show that you GET IT. Exam #1.

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Wrap-up First Unit Motivating Logic in AI

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  1. Wrap-up First Unit Motivating Logic in AI

  2. Exam #1 • My general impression as I graded the exams: • You understood the general idea(s) but not the specifics/details • You were able to say things that made sense, but you couldn’t identify the key ideas that show that you GET IT.

  3. Exam #1 • Examples: • 1c – to explain why it is better than DFS you have to mention that it won’t fall into infinite loops • 2c – “They have info about the problem” What KIND of info? (This is important). • 3b – You have to talk about the children • 3e – you better define what alpha and beta are

  4. Exam #1 • What is in the grade book. • Add up all your points • Add 2.5 • Multiply by 4/3 • This gives you an adjusted score out of 100

  5. HW#2 • Write the (recursive) minimax algorithm that reports the best next move for a modified version of tic-tac-toe • When it is your move you may put an X or an O in any empty spot on the board. • First player to get three like pieces in a row wins. • It is still possible to tie.

  6. HW#2 • How many opening moves? • What is the fewest number of moves needed for someone to win?

  7. HW#2 - requirements • Takes in a length nine string to show the current board: “o-xxxoo--"

  8. HW#2 - requirements • Returns the piece and the position of the best next move:

  9. HW#2 - requirements • Returns the piece and the position of the best next move: “Put an o in cell 1”

  10. HW#2 - requirements • The fitness function of a leaf node (an end game) is: 0=tie Otherwise : (Empty Cells +1)* [1 or -1]

  11. HW#2 - requirements • Even numbers are negative • Odd numbers are positive

  12. HW#2 • My suggestions: • This is a recursive algorithm. It’s easier if you recognize and embrace this. • You don’t need any “special” info about whose turn it is. You can calculate that from the board. • Check your code early with simple problems • “xx-oxooox” better take an x in cell 2 • “--xxoooxx“ better take an o in cell 0 (could also be cell 1, but I would expect 0.

  13. Let’s play a game of Wumpus

  14. A Typical Wumpus World

  15. Wumpus World Description

  16. Let’s Play Wumpus! • You are in Cell (1,1) facing “North” • None of the sensors register anything. • Find the gold….

  17. Exploring the Wumpus World

  18. Exploring the Wumpus World

  19. Exploring the Wumpus World

  20. Exploring the Wumpus World

  21. Exploring the Wumpus World

  22. Exploring the Wumpus World

  23. Exploring the Wumpus World

  24. Exploring the Wumpus World

  25. Motivating KB Agents • Reflex agents keep the world clean, in large part, by dumb luck • Searching algorithm can find the shortest path from Arad to Bucharest, but can’t easily adjust when the road from Rimnicu Vilcea to Pitesti is closed for maintenance. • Logic (Knowledge-Based) agents combine general knowledge with current percepts to infer hidden aspects of current state prior to selecting actions

  26. Knowledge Bases

  27. Knowledge Base • A knowledge base (KB) is a set of representations (sentences) of facts about the world. • TELL and ASK - two basic operations • to add new knowledge to the KB • to query what is known to the KB • Infer - what should follow after the KB has been TELLed. • A generic KB agent (Fig 7.1)

  28. Generic KB-Based Agent

  29. Simple Knowledge-Based Agent

  30. Representing Knowledge Problems: PAGE Formulation • Percepts • What can agent observe? • What can sensors tell it? • Actions • What actuators does agent have? • In what context are they applicable? • Goals • What are agents goals? Preferences (utilities)? • How does agent evaluate them (check environment, deliberate, etc.)? • Environment • What are “rules of the world”? • How can these be represented, simulated?

  31. Wumpus World PAGE Description

  32. Wumpus World: PEAS Description

  33. Tight spots (0.86 vs. 0.31)

  34. Tight spots (0.86 vs. 0.31)

  35. Tight spots (0.86 vs. 0.31)

  36. Tight spots (0.86 vs. 0.31)

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