摘要
随着人工智能的飞速发展，其在各个领域得到了更多的应用，但是它在决策方面的缺陷也日益凸显。本文拟从特征选择、决策树修剪和神经网络等优化算法入手，对各种算法的优劣进行分析。在此基础上，本项目将探索该方法的可扩展性与推广性能，提高人工智能的决策水平。本项目的研究目标是寻求一种能够最大限度地利用人工智能的决策能力，使其能够更好地利用其在实际应用中的潜能。
关键词： AI；决策；才能
Abstract
Along with the development of AI technology, it has been widely used in many fields, but its lack of decision making is also becoming more and more obvious. In this paper, the causes of AI decision making are analyzed, and the optimization methods, such as feature selection, decision tree pruning and neural network optimization, are discussed. Furthermore, this paper also discusses the scalability and generalization capability of AI to improve AI decision making. Ultimately, the goal is to find ways to optimize the decision making of artificial intelligence, so that it can be used more effectively in applications.
Key words: AI; policy decision-making; capability
目录
摘要 1
Abstract 1
引言 3
1智能系统中决策力不足的成因剖析 3
1.1智能决策程序的难点与限制 3
1.2人工智能决策中的误差与偏差 3
2人工智能决策能力的优化方法 3
2.1人工智能决策能力的优化方法 3
2.2优化算法在提高人工智能决策水平中的作用 4
3种最优方案的对比和分析 4
3.1比较几种优化算法的优劣 4
3.1具有可扩展性与推广性能的最优算法 5
结论 5
参考文献 6
