Another view on the SSS* algorithm

A new version of the SSS* algorithm for searching game trees is presented. This algorithm is built around two recursive procedures. It finds the minimax value of a game tree by first establishing an upper bound to this value and then successively trying in a top down fashion to tighten this bound until the minimax value has been obtained. This approach has several advantages, most notably that the algorithm is more perspicuous. Correctness and several other properties of SSS* can now more easily be proven. As an example we prove Pearl's characterization of the nodes visited by SSS*. Finally the new algorithm is transformed into a practical version, which allows an efficient use of memory

MODIFIKASI ALGORITMA MINIMAX DENGAN ALPHA BETA PRUNING PADA PROTOTYPE PERMAINAN BERTIPE TURN BASED

Algoritma Minimax merupakan salah satu algoritma yang sering diterapkan pada permainan bertipe giliran atau biasa disebut turn based game. Namun permasalahan muncul saat algoritma Minimax digunakan pada permainan yang kompleks. Penggunaan algoritma Minimax pada permainan yang kompleks akan membuat waktu pencarian solusi menjadi lebih lama. Oleh karena itu, Alpha Beta pruning menjadi salah satu solusi untuk mengurangi waktu penelusuran pada pencarian solusi. Untuk melihat perbedaan waktu pencarian solusi antara algoritma Minimax dengan algoritma Minimax Aplha Beta pruning maka dibuatlah sebuah prototype permainan bertipe turn based. Hasil pengujian dengan menggunakan prototype permainan yang dibuat diketahui bahwa Alpha Beta pruning mampu memangkas waktu penelusuran sebanyak 90 persen lebih cepat daripada algoritma Minimax tanpa pruning. Hal ini terjadi karena simpul-simpul yang bukan merupakan solusi tidak perlu ditelusuri. Adapun salah satu cara untuk memangkas simpul lebih banyak pada algoritma Minimax Alpha Beta pruning diperlukan pembuatan tree yang baik, sehingga pada saat proses pencarian dengan algoritma Minimax Alpha Beta pruning bisa lebih cepat dengan melakukan pruning pada bagian-bagian tree yang sudah disusun atau disortir, Kata Kunci: permainan, turn based game, prototype, tree, Minimax, Alpha Beta pruning.  Minimax algorithm is one algorithm that is often applied in turn-type game or commonly called turn based game. But the problem arises when the Minimax algorithm is used in complex games. The use of Minimax algorithms in complex games will make the solution search time longer. Therefore, Alpha Beta pruning becomes one of the solutions to reduce search time on solution search. To see the difference of the time searching solution between Minimax algorithm with Minimax algorithm Aplha Beta pruning a turn based game prototype is made. The test results using the game prototype made a finding that Alpha Beta pruning able to cut search time as much as 90 percent faster than the Minimax algorithm without pruning. This happens because nodes that are not a solution are already pruned by Alpha Beta pruning method. As for one way to cut more nodes on the algorithm Minimax Alpha Beta pruning process required good tree creation, so the Minimax Alpha Beta pruning algorithm can be faster by pruning the parts of the tree that has been prepared or sorted. Keywords: games, turn based game, prototype, tree, Minimax, Alpha Beta prunin

GAMES: A new Scenario for Software and Knowledge Reuse

Games are a well-known test bed for testing search algorithms and learning methods, and many authors have presented numerous reasons for the research in this area. Nevertheless, they have not received the attention they deserve as software projects. In this paper, we analyze the applicability of software and knowledge reuse in the games domain. In spite of the need to find a good evaluation function, search algorithms and interface design can be said to be the primary concerns. In addition, we will discuss the current state of the main statistical learning methods and how they can be addressed from a software engineering point of view. So, this paper proposes a reliable environment and adequate tools, necessary in order to achieve high levels of reuse in the games domain

Search versus Knowledge: An Empirical Study of Minimax on KRK

This article presents the results of an empirical experiment designed to gain insight into what is the effect of the minimax algorithm on the evaluation function. The experiment’s simulations were performed upon the KRK chess endgame. Our results show that dependencies between evaluations of sibling nodes in a game tree and an abundance of possibilities to commit blunders present in the KRK endgame are not sufficient to explain the success of the minimax principle in practical game-playing as was previously believed. The article shows that minimax in combination with a noisy evaluation function introduces a bias into the backed-up evaluations and argues that this bias is what masked the effectiveness of the minimax in previous studies