Technology and Research on Artificial Intelligence Laboratory
@mobile and distributed systems chair, LMU Munich
Recent advances in Artificial Intelligence (AI) have enabled exciting applications, which are now playing important roles in everyday life ranging from language translation and image processing to recommender systems and autonomous driving. Most applications are based on Machine Learning (ML), which achieved great successes due to increasingly available computational resources and data. The current trend of AI offers numerous opportunities to contribute within areas of research, theory, technology, and application.
In TRAIL, we investigate different directions of AI and ML to provide novel methods and insights to pave the way for future applications and technologies.
Here is some of our recent research:
CROP
Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
DIRECT
Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
Social Neural Network Soups with Surprise Minimization
What happens when concepts from artificial chemistry and neural networks intersect? They become social (or try to).
We focus our research on intelligent, autonomous systems and agents. In TRAIL, we regard the term intelligence with respect to the behaviour of an entity. For simplification we assume an entity to be intelligent if it is able to learn from past experience, to think about future events or actions, and to act according to its knowledge, thoughts, and interaction with other entities.
Our research in TRAIL focuses on three main aspects: Think, Act and Learn