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Ch. 5 – Adversarial Search. Supplemental slides for CSE 327 Prof. Jeff Heflin. Tic-Tac-Toe Transition Model. O to bottom-center. O to top-left. O to top-center. O to top-right. Minimax Algorithm.

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### Ch. 5 – Adversarial Search

Supplemental slides for CSE 327

Prof. Jeff Heflin

O tobottom-center

O to top-left

O totop-center

O to top-right

function Minimax-Decision(state) returns an action returnargmaxa  ACTIONS(s)C(Result(state,a))

function Max-Value(state) returns a utility valueif Terminal-Test(state) then return Utility(state)v -for each ainActions(state) dovMax(v, Min-Value(Result (s,a)))return v

function Min-Value(state) returns a utility valueif Terminal-Test(state) then return Utility(state)v +for each a in Actions(state) dovMax(v, Min-Value(Result (s,a)))return v

From Figure 5.3, p. 166

sensors

State

What the world is like now

How the world evolves

What it will be like if I do action A

What my actions do

Environment

How happy will I be in such a state

Utility

What action I should do now

Agent

actuators

function Minimax-Decision(state) returns an action returnargmaxa  ActionS(s)Min-Value(Result(state,a),0)

function Max-Value(state,depth) returns a utility valueif Cutoff-Test(state,depth) then return Eval(state)v -for each ainActions(state) dovMax(v, Min-Value(Result(s,a)), depth+1)return v

function Min-Value(state,depth) returns a utility valueif Cutoff-Test(state,depth) then return Eval(state)v +for each a in Actions(state) dovMin(v, Max-Value(Result(s,a)), depth+1)return v