Exploration and Learning in Heuristic Search

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Research in Computing Science progresses towards the goal of solving ever more complex, difficult real-world problems. Intelligent automated decision-making requires modelling an application domain, and processing a potentially huge space of possible future alternatives. With my research group and my colleagues I study efficient search algorithms for solving hard decision-making problems. The current proposal focuses on two topics: the question of efficient exploration in large search spaces, and the use of machine learning methods. These topics have emerged as big common themes that drive much of my group's recent work in a diverse set of application areas. My research area has radically changed over the last five years. Systems that combine deep reinforcement learning with Monte Carlo Tree Search have achieved super-human performance in complex games such as Go, chess and shogi. DeepMind's Alpha Zero system has learned to play such games from scratch, without any human input regarding playing strategy. A beautiful aspect of these systems is the way in which they combine learning and search. They create a virtuous cycle where machine learning improves the search process, and the search also improves the learning. Despite the impressive successes of these algorithms, a number of problems of both practical and fundamental nature currently limits their more widespread use. A major practical problem is posed by the massive resources required to train the large and deep neural networks which encode the learned knowledge. More fundamental questions include: how to control the search process? And how to generalize such approaches when we don't have a perfect and efficient model of a problem? In future work with my students and colleagues, I want to study the following topics in depth: 1. Continue the study of exploration in heuristic search 2. Extend our methods to problems beyond games, which are less well specified 3. Study learning and search in cases where we know the "true result" due to their special mathematical structure To study these research questions, I plan to continue working on concrete applications which pose significant challenges. I want to continue building complete high performance systems, and test them on standard benchmarks as well as in competitions. A deeper understanding of these methods will likely lead to further significantly improved decision-making systems, which can search and learn better and faster, and can be used for less well-defined problems.