Pitfalls and Best Practices in Algorithm Configuration

The Journal of Artificial Intelligence Research

Katharina Eggensperger¹ 

关键词: satisfiability;    constraint satisfaction;    heuristics;    search;   

DOI  :  10.1613/jair.1.11420

学科分类：人工智能

来源: Association for the Advancement of Artificial Intelligence

【 摘 要 】

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

【 授权许可】

CC BY