{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

lecture2 ch3

# Artificial Intelligence: A Modern Approach

This preview shows pages 1–4. Sign up to view the full content.

1 ICS-171:Lecture 2: 1 Lecture 2: Problem Solving as Search; Uniformed Search ICS 171, Summer 2000 ICS-171:Lecture 2: 2 Outline Representing problems as search state space operators start state goal states A Search Tree is an efficient way to represent how a search algorithm explores the state space There are a variety of specific search techniques, including Depth-First Search Breadth-First Search

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 ICS-171:Lecture 2: 3 What do these problems have in common? Find the layout of chips on a circuit board which minimize the total length of interconnecting wires Schedule which airplanes and crew fly to which cities for American, United, British Airways, etc Write a program which can play chess against a human Build a system which can find human faces in an arbitrary digital image Program a tablet-driven portable computer to recognize your handwriting Decrypt data which has been encrypted but you do not have the key Answer they can all be formulated as search problems ICS-171:Lecture 2: 4 Problem-Solving Agents Intelligent agents can solve problems by searching a state-space State space the agent’s model of the world usually a set of discrete states e.g., in driving, the states in the model could be towns/cities Goal State(s) a goal is defined as a desirable state for an agent For now: all goal states have utility 1, and all non-goals have utility 0 there may be many states which satisfy the goal • e.g., drive to a town with a ski-resort or just one state which satisfies the goal • e.g., drive to Mammoth Mountain Operators operators are legal actions which the agent can take to move from one state to another
3 ICS-171:Lecture 2: 5 State Spaces and Search A State-Space Representation for Search Problems search = “journey” through a set of states start at initial state S want to get to a goal state G (utility of these states = 1) nodes represent states links represent state-transitions, may have associated costs A search algorithm specifies precisely how to explore the state space to: find any path to G find all paths to G find the lowest cost path to G We will focus on • finding any path to any goal state G • ignore path costs for now ICS-171:Lecture 2: 6 Defining Search Problems A statement of a Search problem has 4 components 1. A set of states 2. A set of “operators” which allow one to get from one state to another 3. A start state S 4. A set of possible goal states, or ways to test for goal states Search solution consists of a unique goal state G a sequence of operators which transform S into a goal state G (this is the sequence of actions the agent would take to maximize the success function) For now we are interested in any path from S to G Representing real problems in a search framework may be many ways to represent states and operators key idea: represent only the relevant aspects of the problem

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 27

lecture2 ch3 - Lecture 2 Problem Solving as Search...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online