1. Artificial Intelligence and Searching
CSCE 315 – Programming Studio
Spring 2019
Project 2, Lecture 1
Robert Lightfoot
Adapted from slides of
Yoonsuck Choe, John Keyser

2. Artificial Intelligence
Long-standing computational goal
Turing test
Field of AI very diverse
“Strong” AI – trying to simulate thought itself
“Weak” AI – trying to make things that behave intelligently
Several different approaches used, topics studied
Sometimes grouped with other fields
Robotics
Computer Vision

3. Topics in Artificial Intelligence
Problem solving
Reasoning
Theorem Proving
Planning
Learning
Knowledge Representation
Perception
Agent Behavior
Understanding Brain Function and Development
Optimizing
etc.

4. AI History AI has gone through “high” and “low” points
Like many other areas…
Cycle of inflated expectations, promising early results, tough problems leading to collapse in confidence, long-term productivity
Early stages: 1950s through mid-1970s
Early work on reasoning, language (conversation – Turing-test oriented), games
Late 1970s – early 1980s
Hit limitations/roadblocks

5. AI History (continued)
Mid-1980s
Japan: 5th Generation Project – giant push for AI
Expert systems and neural networks grew
Late s
Another “gap” as earlier work did not pan out
2000s onward :
Growth in interest in AI again over time
AI topics applied to big data are especially popular
Machine Learning, Natural Language Processing, etc.

6. Game Playing and Search
Game playing a long-studied topic in AI
Seen as a proxy for how more complex reasoning can be developed
Search
Understanding the set of possible states, and finding the “best” state or the best path to a goal state, or some path to the goal state, etc.
“State” is the condition of the environment
e.g. in theorem proving, can be the state of things known
By applying known theorems, can expand the state, until reaching the goal theorem
Should be stored concisely

7. Really Basic State Search Example
Given a=b, b=c, c=d, prove a=d.
Knowledge: a=b,b=c,c=d
Knowledge:a=b,b=c,c=d
Infer: a=c
Knowledge: a=b,b=c,c=d
Infer: b=d
Knowledge:a=b,b=c,c=d,a=c,b=d
Infer:a=d

8. Operators Transition from one state to another
Fly from one city to another
Apply a theorem
Move a piece in a game
Add person to a meeting schedule
Operators and states are both usually limited by various rules
Can only fly certain routes
Only certain theorems can be applied
Only valid moves in game
Meetings can have capacity, requirements for/against grouping people, etc.

9. Search Examine possible states, transitions to find goal state
Interesting problems are those too large to explore exhaustively
Uninformed search
Systematic strategy to explore options
Informed search
Use domain knowledge to limit search

10. Search Examine possible states, transitions to find goal state
Interesting problems are those too large to explore exhaustively
Uninformed search
Systematic strategy to explore options
Informed search
Use domain knowledge to limit search
Come up with a Informed search and Uninformed search example per table. Be ready to explain.

11. Game Playing Abstract AI problem Nice and challenging properties
Usually states can be clearly and concisely represented
Limited number of operations (but can still be large)
Unknown factor – account for opponent
Search space can be huge
Limit response based on time – forces making good “decisions”
e.g. Chess averages about 35 possible moves per turn, about 50 moves per player per game, or possible games. But, “only” possible board states.

12. Types of games Examples Deterministic vs. random factor
Known state vs. hidden information
Examples
Deterministic
Chance
Perfect Info
Chess, Checkers, Othello, Go, Mancala
Monopoly, Backgammon
Imperfect Info
Stratego, Poker? Bridge?
Poker?, Scrabble
Bridge?

13. Game Playing
In upcoming lectures, we will discuss some of the basic methods for performing search
Our next Project will focus on a deterministic game with perfect information