Algorithms in a Nutshell

(^212) | Chapter 7: Path Finding in AI Because we assume the opponent makes no mistakes,MAXcan stop searching a MINsubtree once ...

NegMax | 213 Path Finding in AI MINcomputes aminValuethat determines the lowest achievable score that player MINcan ensure.MINca ...

(^214) | Chapter 7: Path Finding in AI Figure 7-18. NegMax fact sheet Example 7-7. NegMax implementation public class NegMaxEval ...

NegMax | 215 Path Finding in AI Consequences NEGMAXis useful because it prepares a simple foundation on which to extend to ALPHA ...

(^216) | Chapter 7: Path Finding in AI One last observation is how NEGMAXhandles the leaf nodes in the game tree (e.g., when the ...

AlphaBeta | 217 Path Finding in AI NEGMAXstreamlines the algorithm since there is no longer a need to alternate betweenMAXandMIN ...

(^218) | Chapter 7: Path Finding in AI The game tree in Figure 7-21 shows the [α,β] values as ALPHABETAexecutes; initially they ...

AlphaBeta | 219 Path Finding in AI Recall that ALPHABETAis based on NEGMAX, the MINIMAXvariant that seeks to maximize the negati ...

(^220) | Chapter 7: Path Finding in AI At the initial nodenin the game tree, player O must consider one of six potential moves. ...

AlphaBeta | 221 Path Finding in AI Player’s turn Assume O plays in the middle of the left column and X responds by playing in th ...

(^222) | Chapter 7: Path Finding in AI Consequences Since the resulting moves will be exactly the same as if MINIMAXhad executed ...

AlphaBeta | 223 Path Finding in AI favorability (i.e., the best move first), then we still have to evaluate allbchildren for the ...

(^224) | Chapter 7: Path Finding in AI References Barr, Avron and Edward A. Feigenbaum,The Handbook of Artificial Intelligence. ...

References | 225 Path Finding in AI Russell, S. J. and P. Norvig,Artificial Intelligence: A Modern Approach. Prentice Hall, 2003 ...

226 Chapter 8. Network Flow Algorithms.......................................................................................... ...

Overview | 227 Network Flow Algorithms One way to explain how these specialized problems are solved is to describe the relations ...

(^228) | Chapter 8: Network Flow Algorithms approach outlined in FORD-FULKERSONcan be generalized to solve the more powerful Min ...

Maximum Flow | 229 Network Flow Algorithms Capacity constraint The flowf(u,v) through an edge cannot be negative and cannot exce ...

(^230) | Chapter 8: Network Flow Algorithms Input The flow network is defined by a graphG=(V,E) with designated start vertexs an ...

Maximum Flow | 231 Network Flow Algorithms Output FORD-FULKERSONcomputes for each edge (u,v)inE, an integer flowf(u,v) repre- se ...