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Alfa-Beta genėjimas

  • Alfa-beta genėjimas yra modifikuota minimax algoritmo versija. Tai minimalaus algoritmo optimizavimo technika.
  • Kaip matėme minimax paieškos algoritme, žaidimo būsenų, kurias jis turi ištirti, skaičius yra eksponentinis medžio gylyje. Kadangi negalime pašalinti eksponento, bet galime jį sumažinti iki pusės. Taigi yra technika, pagal kurią netikrindami kiekvieno žaidimo medžio mazgo galime apskaičiuoti teisingą minimalaus maksimalaus sprendimą, ir ši technika vadinama genėjimas . Tai apima du slenksčius alfa ir beta parametrus būsimam išplėtimui, todėl jis vadinamas alfa-beta genėjimas . Jis taip pat vadinamas kaip Alfa-beta algoritmas .
  • Alfa-beta genėjimas gali būti taikomas bet kuriame medžio gylyje, o kartais genimi ne tik medžio lapai, bet ir visas pomedis.
  • Du parametrai gali būti apibrėžti taip:
      Alfa:Geriausias (didžiausios vertės) pasirinkimas, kurį iki šiol radome bet kuriame Maximizer kelio taške. Pradinė alfa reikšmė yra -∞ .
  • Beta:Geriausias (mažiausios vertės) pasirinkimas, kurį iki šiol radome bet kuriame Minimizer kelio taške. Pradinė beta vertė yra +∞ .
  • Alfa-beta genėjimas pagal standartinį minimalaus maksimalaus algoritmą grąžina tą patį veiksmą, kaip ir standartinis algoritmas, bet pašalina visus mazgus, kurie iš tikrųjų neturi įtakos galutiniam sprendimui, bet sulėtina algoritmą. Taigi, apkarpius šiuos mazgus, algoritmas pagreitėja.

Pastaba: norėdami geriau suprasti šią temą, išstudijuokite „minimax“ algoritmą.

Alfa-beta genėjimo sąlygos:

Pagrindinė alfa-beta genėjimo sąlyga yra:

 α>=β 

Pagrindiniai dalykai apie alfa-beta genėjimą:

  • Max grotuvas atnaujins tik alfa reikšmę.
  • Min grotuvas atnaujins tik beta versijos vertę.
  • Atšaukiant medį, mazgo reikšmės bus perduodamos į viršutinius mazgus, o ne alfa ir beta vertes.
  • Į antrinius mazgus perduosime tik alfa ir beta vertes.

Alfa-beta genėjimo pseudokodas:

 function minimax(node, depth, alpha, beta, maximizingPlayer) is if depth ==0 or node is a terminal node then return static evaluation of node if MaximizingPlayer then // for Maximizer Player maxEva= -infinity for each child of node do eva= minimax(child, depth-1, alpha, beta, False) maxEva= max(maxEva, eva) alpha= max(alpha, maxEva) if beta<=alpha break return maxeva else for minimizer player mineva="+infinity" each child of node do eva="minimax(child," depth-1, alpha, beta, true) eva) beta="min(beta," if beta<="alpha" < pre> <h2>Working of Alpha-Beta Pruning:</h2> <p>Let&apos;s take an example of two-player search tree to understand the working of Alpha-beta pruning</p> <p> <strong>Step 1:</strong> At the first step the, Max player will start first move from node A where &#x3B1;= -&#x221E; and &#x3B2;= +&#x221E;, these value of alpha and beta passed down to node B where again &#x3B1;= -&#x221E; and &#x3B2;= +&#x221E;, and Node B passes the same value to its child D.</p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning.webp" alt="Alpha-Beta Pruning"> <p> <strong>Step 2:</strong> At Node D, the value of &#x3B1; will be calculated as its turn for Max. The value of &#x3B1; is compared with firstly 2 and then 3, and the max (2, 3) = 3 will be the value of &#x3B1; at node D and node value will also 3.</p> <p> <strong>Step 3:</strong> Now algorithm backtrack to node B, where the value of &#x3B2; will change as this is a turn of Min, Now &#x3B2;= +&#x221E;, will compare with the available subsequent nodes value, i.e. min (&#x221E;, 3) = 3, hence at node B now &#x3B1;= -&#x221E;, and &#x3B2;= 3.</p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning-2.webp" alt="Alpha-Beta Pruning"> <p>In the next step, algorithm traverse the next successor of Node B which is node E, and the values of &#x3B1;= -&#x221E;, and &#x3B2;= 3 will also be passed.</p> <p> <strong>Step 4:</strong> At node E, Max will take its turn, and the value of alpha will change. The current value of alpha will be compared with 5, so max (-&#x221E;, 5) = 5, hence at node E &#x3B1;= 5 and &#x3B2;= 3, where &#x3B1;&gt;=&#x3B2;, so the right successor of E will be pruned, and algorithm will not traverse it, and the value at node E will be 5. </p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning-3.webp" alt="Alpha-Beta Pruning"> <p> <strong>Step 5:</strong> At next step, algorithm again backtrack the tree, from node B to node A. At node A, the value of alpha will be changed the maximum available value is 3 as max (-&#x221E;, 3)= 3, and &#x3B2;= +&#x221E;, these two values now passes to right successor of A which is Node C.</p> <p>At node C, &#x3B1;=3 and &#x3B2;= +&#x221E;, and the same values will be passed on to node F.</p> <p> <strong>Step 6:</strong> At node F, again the value of &#x3B1; will be compared with left child which is 0, and max(3,0)= 3, and then compared with right child which is 1, and max(3,1)= 3 still &#x3B1; remains 3, but the node value of F will become 1. </p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning-4.webp" alt="Alpha-Beta Pruning"> <p> <strong>Step 7:</strong> Node F returns the node value 1 to node C, at C &#x3B1;= 3 and &#x3B2;= +&#x221E;, here the value of beta will be changed, it will compare with 1 so min (&#x221E;, 1) = 1. Now at C, &#x3B1;=3 and &#x3B2;= 1, and again it satisfies the condition &#x3B1;&gt;=&#x3B2;, so the next child of C which is G will be pruned, and the algorithm will not compute the entire sub-tree G.</p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning-5.webp" alt="Alpha-Beta Pruning"> <p> <strong>Step 8:</strong> C now returns the value of 1 to A here the best value for A is max (3, 1) = 3. Following is the final game tree which is the showing the nodes which are computed and nodes which has never computed. Hence the optimal value for the maximizer is 3 for this example. </p> <img src="//techcodeview.com/img/artificial-intelligence/75/alpha-beta-pruning-6.webp" alt="Alpha-Beta Pruning"> <h2>Move Ordering in Alpha-Beta pruning: </h2> <p>The effectiveness of alpha-beta pruning is highly dependent on the order in which each node is examined. Move order is an important aspect of alpha-beta pruning.</p> <p>It can be of two types:</p> <ul> <tr><td>Worst ordering:</td> In some cases, alpha-beta pruning algorithm does not prune any of the leaves of the tree, and works exactly as minimax algorithm. In this case, it also consumes more time because of alpha-beta factors, such a move of pruning is called worst ordering. In this case, the best move occurs on the right side of the tree. The time complexity for such an order is O(b<sup>m</sup>). </tr><tr><td>Ideal ordering:</td> The ideal ordering for alpha-beta pruning occurs when lots of pruning happens in the tree, and best moves occur at the left side of the tree. We apply DFS hence it first search left of the tree and go deep twice as minimax algorithm in the same amount of time. Complexity in ideal ordering is O(b<sup>m/2</sup>). </tr></ul> <h2>Rules to find good ordering: </h2> <p>Following are some rules to find good ordering in alpha-beta pruning:</p> <ul> <li>Occur the best move from the shallowest node.</li> <li>Order the nodes in the tree such that the best nodes are checked first. </li> <li>Use domain knowledge while finding the best move. Ex: for Chess, try order: captures first, then threats, then forward moves, backward moves.</li> <li>We can bookkeep the states, as there is a possibility that states may repeat.</li> </ul> <hr></=alpha>