Artificial Intelligence and Searching CPSC 315 – Programming Studio Spring 2013 Project 2, Lecture 1 Adapted from slides of Yoonsuck Choe

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

Topics in Artificial Intelligence Problem solving Reasoning Theorem Proving Planning Learning Knowledge Representation Perception Agent Behavior Understanding brain function and development Optimizing etc.

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

Really Basic State Search Example Given a=b,b=c,c=d, prove a=d. a=b, b=c, c=d a=c a=b, b=c, c=d b=d a=b, b=c, c=d (a=c, b=d), a=d

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.

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

Game Playing Abstract AI problem Nice and challenging properties Usually state can be clearly, 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.

Types of games Deterministic vs. random factor Known state vs. hidden information Examples DeterministicChance Perfect InfoChess, Checkers, Othello, Go Monopoly, Backgammon Imperfect InfoStratego, Bridge? Poker, Scrabble Bridge?

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