動作型電腦遊戲設計因素探討

Abstract

本研究探討動作型電腦遊戲的設計因素。研究分成兩個部分來進行：（一）以遊戲者為對象，探討在購買過程(buying process)時，吸引遊戲者購買之電腦遊戲設計特徵。（二）以遊戲開發設計者為對象，提出在設計過程(design process)時，電腦遊戲之最佳化設計整合方法。

在購買過程，吸引遊戲者之遊戲設計特徵研究部份，首先由16位經常購買電腦遊戲的玩家，對照不同版本的PacMan（小精靈）動作型電腦遊戲後，確認出39個吸引購買玩家的設計特徵。接著由45位受試者（27位高中男生與18位高中女生）根據此確認的39個設計特徵來評估28種版本的PacMan電腦遊戲。然後經Qnet2000類神經網路軟體計算確認此39個設計特徵的相對重要性。結果顯示前十項最重要的設計特徵對「感覺好玩」的累積貢獻程度超過50%。而在購買過程對玩家有重要性的化身(avatar)特徵，過去在以遊戲過程(play process)為對象的研究中並未提及。此化身特徵顯示個人化展示遊戲的重要性。同時，透過因素分析方法，將此39個設計特徵總共歸類為「新奇性與具威力性」、「吸引的呈現」、「互動性」、「挑戰感」、「控制感」與「獎勵」六個設計因素，這六個設計因素可以解釋54%的總變異量。在這六個因素中，「吸引的呈現」在過去的其他研究中也未被強調。對於不能直接接觸到遊戲的購買玩家，「吸引的呈現」這個因素在展示的環境中變得非常重要。

在設計過程，電腦遊戲最佳化設計整合方法研究部份，因為電腦遊戲的設計特徵非常多，而且每一設計特徵皆有不同表現水準的要求，也相對的形成不同的設計成本。所以在短時間產品必須上市的市場環境中，電腦遊戲的開發設計者可能無法將所有的設計特徵皆以最佳之表現水準納入遊戲設計中。因此如何權衡決定每一設計特徵之表現水準，就變得非常重要，但少有研究提到。本研究提出一個方法以解決此權衡決定的問題。本方法包括利用類神經網路與基因演算法以選擇設計方案。其中，類神經網路乃是用來建立評估設計方案之「感覺好玩」程度的機制；而基因演算法則用來發展出確認最佳化的設計方案。根據本方法所得到近於最佳化的設計選擇方案中，要求達到最佳表現水準的設計特徵包括「對手厲害」、「音效要反應事件的發生」、「音效要時常變化」、「開頭幾關容易過關」及「後幾關不容易得高分」等五個設計特徵。本研究所發展出來的方法可在遊戲風行的時機，設計團隊就能夠即時決定出電腦遊戲近於最佳化的設計選擇方案，並將資源與心力配置於不同的電玩設計特徵上。

This study attempts to explore the design factors for action computer games. There are two issues in this study: (1) identifying design features for action games that would appeal to game players during buying process, rather than play process. (2) presenting an approach to solve the trade-off decision problem efficiently for game design.

The first issue aims to identify design features for action games that would appeal to game players during buying process. Sixteen frequent-buyers of computer games identified 39 design features that appeal to players during buying process by contrasting different versions of Pacman games. Twenty-eight versions of Pacman were then evaluated in terms of the identified design features by 45 participants（27 male and 18 female college students）. Qnet2000 neural network software was used to determine the relative importance of these design features. The results indicated that the top 10 most important design features could account for more than 50% of “perceived fun” among these 39 design features. The feature of avatar is important to game players during buying process, yet not revealed in previous play process oriented studies. Moreover, six design factors underlying the 39 features were identified through factor analysis. These factors included “novelty and powerfulness”, “appealing presentation”, “interactivity”, “challenging”, “sense of control”, and “rewarding” and could account for 54% of total variance. Among these six factors, appealing presentation has not been emphasized by play process oriented research. Implications of the findings were discussed.

The second issue aims to present an integrated approach to solve the trade-off decision problem efficiently for game design. In a time-to-market environment, designers may not be able to incorporate all the design features with the best performance in a computer game. For each feature, there are several levels of implementation, which is corresponded to different levels of benefit as well as cost. Therefore, a trade-off decision for determining appropriate levels of implementation is very important, yet has been rarely studied in literature. This issue presents an approach to solve this trade-off selection problem. This approach applies the neural network technique and develops a genetic algorithm to optimize the design of computer games. By this study, a near-optimal design alternative can be identified in a timely fashion. In this study, five design features included “opponent is competitive”, “sound effect varies with events”, “sound effect is varying”, “beginning levels are easy” and “final levels are difficult” and could be required up to the best implementation level. Then, computer game designers can properly allocate design resources in different design features of a game.