Artificial Intelligence and Searching CSCE 315 Programming Studio

Artificial Intelligence and Searching CSCE 315 – Programming Studio Fall 2017 Project 2, Lecture 1 Adapted from slides of Yoonsuck Choe, John Keyser

Artificial Intelligence l Long-standing computational goal l l Field of AI very diverse l l Turing test “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 l l Robotics Computer Vision

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

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

AI History (continued) l Mid-1980 s l l l Late 1980 -1990 s l l Japan: 5 th Generation Project – giant push for AI Expert systems and neural networks grew Another “gap” as earlier work did not pan out 2000 s onward : l l l Growth in interest in AI again over time AI topics applied to big data are especially popular Machine Learning, Natural Language Processing, etc.

Game Playing and Search l Game playing a long-studied topic in AI l l Seen as a proxy for how more complex reasoning can be developed Search l l 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 l e. g. in theorem proving, can be the state of things known § l By applying known theorems, can expand the state, until reaching the goal theorem Should be stored concisely

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

Operators l Transition from one state to another l l l 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 l l 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.

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

Game Playing l l Abstract AI problem Nice and challenging properties l l 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 l Limit response based on time – forces making good “decisions” l 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.

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

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