8 research outputs found
Efficient time stepping for numerical integration using reinforcement learning
Many problems in science and engineering require the efficient numerical
approximation of integrals, a particularly important application being the
numerical solution of initial value problems for differential equations. For
complex systems, an equidistant discretization is often inadvisable, as it
either results in prohibitively large errors or computational effort. To this
end, adaptive schemes have been developed that rely on error estimators based
on Taylor series expansions. While these estimators a) rely on strong
smoothness assumptions and b) may still result in erroneous steps for complex
systems (and thus require step rejection mechanisms), we here propose a
data-driven time stepping scheme based on machine learning, and more
specifically on reinforcement learning (RL) and meta-learning. First, one or
several (in the case of non-smooth or hybrid systems) base learners are trained
using RL. Then, a meta-learner is trained which (depending on the system state)
selects the base learner that appears to be optimal for the current situation.
Several examples including both smooth and non-smooth problems demonstrate the
superior performance of our approach over state-of-the-art numerical schemes.
The code is available under https://github.com/lueckem/quadrature-ML
A Prototype to Support Business Model Innovation through Crowdsourcing and Artificial Intelligence
The development of new and innovative business models is a central challenge for many companies, particularly for small and medium-sized companies. Information systems could support these companies by actively guiding them through a business model development process. However, the existing business model development tools only provide passive support for their users (e.g., digital whiteboards). Therefore, we set out to develop a prototype that actively supports its users by generating business model ideas. Informed by an existing design theory, we built a prototype relying on hybrid intelligence (i.e., the combination of human and artificial knowledge). The prototype iteratively generates new business model ideas by recombining existing knowledge, posts the ideas to a crowdsourcing platform for evaluation, and learns from the crowds’ evaluation. This demonstration paper presents the prototype, the challenges we faced during its implementation, and directions for future research on machine-supported business model development
Proceedings of the DA2PL'2016 EURO Mini Conference
International audienc