Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing

Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games

Testing market imperfections via genetic programming

The thesis checks the validity of the efficient markets hypothesis focusing on stock markets. Technical trading rules are generated by using an evolutionary optimization algorithm (Genetic Programming) based on training samples. The trading rules are subsequently applied to data samples unknown to the algorithm beforehand. The benchmark strategy consists of a classic buy-and-hold strategy in the DAX and the Hang Seng. The trading rules generally fail at consistently beating the benchmark thus indicating that market efficiency holds.Gegenstand der Dissertation ist die Überprüfung von Markteffizienz auf Aktienmärkten. Hierzu werden technische Handelsregeln mit Hilfe eines evolutionären Optimierungsalgorithmus (Genetic Programming) anhand von Trainingsdaten erlernt und anschließend auf eine unbekannte Zeitreihe angewandt. Als Benchmark dient eine klassische buy-and-hold Strategie im DAX und Hang Seng. Es zeigt sich, dass die mittels Genetic Programming generierten Handelsstrategien den Benchmark auf risikoadjustierter Basis nicht durchgängig schlagen können und somit die These effizienter Märkte für den DAX und den Hang Seng gültig ist