The Relationship Between Problem Features and Algorithm Evaluation Methods in Artificial Intelligence

Algorithms are the primary component of Artificial Intelligence(AI). The algorithm is the process in AI that imitates the human mind to solve problems. Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets. However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics. Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets, lacking the credibility and validity of the evaluation. Moreover, evaluation of algorithms requires repeated experiments on different data sets, reducing the attention of researchers to the research of the algorithms itself. Crucially, this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems. And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms. Because classical algorithms input is infinite numbers, whereas AI algorithms input is a data set, which is limited and multifarious. According to the AI algorithm evaluation without response to the problem solving capability, this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.