Real-time Heuristic Search
Real-time heuristic search (RTHS) allows Artificial Intelligence (AI) agents to make decisions in real time, with incomplete information. Since 2003 our research group has produced a series of state-of-the-art RTHS algorithms. We accomplished that by introducing several key ideas to the field. However, even contemporary heuristic search methods still face several challenges. First, RTHS algorithms and their parameters are typically manually designed/optimized for each type of search problem. Thus applying RTHS techniques to a new search problem usually requires an RTHS expert, restricting applicability of RTHS. Second, while our recent work showed that RTHS algorithms can sometimes be automatically formed from a set of building blocks, the blocks themselves are manually engineered limiting their variety and injecting human bias. Third, an RTHS agent typically learns only from its own individual experience. Fourth, an agent's reasoning can be difficult to express in a compact human-comprehensible way. This is detrimental for AI agents embedded in human society where the ability to explain one's actions is key to trust and collaboration.
Our research program is addressing these shortcomings by extending our recent work on automated search in the space of RTHS algorithms as well as automated per-problem algorithm selection and using A-life techniques.
Deep Learning for Sound
This project covers a wide variety of research, all revolving around deep learning and interacting with sound, such as video game accents, bird song, and musical instrumentation.