Artificial Intelligence and Searching

1. Artificial Intelligence and Searching CSCE 315 – Programming Studio Spring 2019 Project 2, Lecture 1 Robert Lightfoot Adapted from slides of YoonsuckChoe, 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 1980-1990s • 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 BasicState 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 35100 possible games. But, “only” 1040 possible board states.

12. Types of games • Deterministic vs. random factor • Known state vs. hidden information

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