Artificial Intelligence and Searching CPSC 315 – Programming Studio Spring 2009 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 etc. Common theme: reason about domain knowledge

Domain Knowledge To perform a task, systems need a representation of the domain Symbolic Explicit representation of domain objects, concepts, and attributes E.g. Rules, frames, schemas Sub-symbolic Distributed representation of objects, concepts, and attributes in the world E.g. neural nets There are also representations that blend these two depending on their use

Frames & Rules Frames Represents declarative and behavioral information Like objects in E-R diagram or OO code Reasoning through inheritance of attributes and behaviors Single and multiple inheritance Class-instance and prototype inheritance Rules Often of the form “If A then B” (A → B) Reasoning through associative property A → B and B → C means A → C Often combined with other representation of objects

Planning Actions often represented as preconditions and post-conditions CookFood pre: haveRawFood AND haveCookingDevice post: haveCookedFood AND NOT haveRawFood BuyRawFood pre: atGroceryStore AND money>=5 post: haveRawFood AND money = money – 5 IncreaseRetirement (n) pre: money>=n post: retirement = retirement + n AND money = money – n Assume features not mentioned in post-condition are not modified by action

Planning Forward chaining From current state to decision (data driven) Often used in open-ended domains (e.g. design) and domains where new data becomes available over time Identifies potential action sequences A utility (“goodness”) function used to select among possible paths (could be lowest cost in design) Backward chaining From goal to actions (goal driven) Used in domains with fixed number of outcomes (e.g. diagnosis) Hypothesis/test method identifies possible diagnoses Tests to discriminate between diagnoses are then identified

Planning How to select among actions when more than one is available Priority Order Often times implemented implicitly through order of actions in list Could have priority ranks, but then again have to choose when more than one in the same rank are available Precision of Context Number of preconditions often used to infer more specialized action More specialized is assumed better

Schemas & Case-based Reasoning Schemas Represents normal sequences of actions/events Case-based reasoning: reuse solution from prior case for current context Identify appropriate schema requires similarity assessment Revise/adapt case to match current context Perhaps save new case as schema for future action Our legal system includes case-based reasoning because rule-based reasoning is fragile many unanticipated exceptions too many potential exceptions to be encoded

Schemas Consider when entering a new restaurant Restaurant schema 1 Enter restaurant Get in line Order at counter Pay for food Wait for food Take food to table Eat food Take trash to trashcan Leave restaurant

Schemas Consider when entering a new restaurant Restaurant schema 1 Enter restaurant Get in line Order at counter Pay for food Wait for food Take food to table Eat food Take trash to trashcan Leave restaurant Restaurant schema 2 Enter restaurant Ask for table Wait to be seated Order food from waiter Wait for food Eat food Get bill Pay bill & leave tip Leave restaurant

Determining Similarity How to identify similar contexts Similar situation Number of attributes in common Can be weighted to indicate relative importance of attributes  Does it look like McDonald’s or Christopher’s? Similar process Number of common actions preceding current state Can be weighted as a function of time to emphasize recent actions  Did you just give your car to the valet?

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 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 valid moves in game

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 focus on a deterministic game with perfect information