Cooperative competition: A new way of solving computationally challenging problems in AI and beyond

Progress in solving challenging problems in artificial intelligence, computer science at large and beyond, is driven to a significant extent by competition - regular algorithm competitions as well as comparative performance evaluation against state-of-the-art methods from the literature. A prominent example for this is the satisfiability problem in propositional logic (SAT), an NP-hard problem that not only lies at the foundations of computer science, but also plays a key role in many real-world applications, notably in ensuring the correctness of hard- and software. In this presentation, Holger Hoos argues that it is time to rethink the way we assess the state of the art in solving problems such as SAT and the incentives for improving it. Hoos demonstrates how automated algorithm selection and configuration techniques based on sophisticated machine learning and optimisation methods have fundamentally changed not only the state of the art in solving SAT and many other NP-hard problems, but also provide a natural basis for cooperative competition - a new approach for achieving and assessing progress not merely in solving these problems, but also in the way we approach them as a scientific community.