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« Autores » DEMASI, Pedro; CRUZ, Adriano J. de O.

« Local e data » International Journal of Intelligent Games and Simulation N. 2, Vol. 2, ISSN 1477-2043, novembro de 2003.

« Abstract » Coevolutionary algorithms (CEAs) have been widely explored in the last years. Cooperative and competitive methods were proposed and evaluated, and many theoretical studies have been made about them and important results have been achieved, however few works have been published about a real-time approach to CEAs, with online agent evolution. The goal of this work is to explore this field of application of CEAs, proposing some methods and strategies for online evolution in an action (real-time) game. In this game, a human player interacts with computer-controlled agents, which begin with very naive or random behaviour and gradually get "smarter", resulting in improved difficulty levels of gameplay. We present four different methods to do online evolution of the agents: using game specific information; merging offline-evolved data with online evolution; using online data only; and using them together. We will, finally, resent some results and a brief discussion of the advantages and disadvantages of each one of the methods proposed, based upon these results.



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  • 1 decade ago
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    Autores 德馬西,佩德羅,克魯茲,阿德里亞諾 J.德澳

    本土電子商務數據 國際期刊智能遊戲和模擬 N. 2卷。 2,ISSN 1477至2043年,2003年11月。

    摘要協同進化算法(CEA平均)已廣泛地探討在近幾年。合作和競爭的方法和評估提出了,許多理論研究作出了重要的他們,已經取得成果,但一些工程已發表過關於實時 CEA平均的方法,通過在線代理的演變。在這項工作的目的是探討這一領域的應用 CEA平均,提出一些方法和策略,在網上演變行動(實時)遊戲。在這個遊戲中,一人與計算機交互的球員控制劑,它首先非常幼稚或者是隨機的行為,並逐步得到“聰明”,從而改進難度等級的遊戲。我們目前的4種不同的方法,以在網上演變劑:使用遊戲的具體資料;合併離線進化的數據網絡的發展;使用在線數據只;和使用在一起。我們將最後,反感一些結果,並簡要討論的優點和缺點的每一個建議的方法,基於這些結果。

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