1 / 5

Ch. 5 – Adversarial Search

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.

haig
Download Presentation

Ch. 5 – Adversarial Search

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Ch. 5 – Adversarial Search Supplemental slides for CSE 327 Prof. Jeff Heflin

  2. Tic-Tac-Toe Transition Model O tobottom-center O to top-left O totop-center O to top-right

  3. Minimax Algorithm 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

  4. Utility-Based Agent 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

  5. Minimax with Cutoff Limit 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

More Related