Designing agents that are able to achieve different play-styles while maintaining a
competitive level of play is a difficult task, especially for games for which the research …

In this paper we review several planning algorithms developed for zero-sum games with
exponential action spaces, ie, spaces that grow exponentially with the number of game …

Decision-making in large and dynamic environments has always been a challenge for AI
agents. Given the multitude of available sensors in robotics and the rising complexity of …

Portfolio methods represent a simple but efficient type of action abstraction which has shown
to improve the performance of search-based agents in a range of strategy games. We first …

Online planning is an important research area focusing on the problem of real-time decision
making, using information extracted from the environment. The aim is to compute, at each …

Action abstractions restrict the number of legal actions available for real-time planning in
zero-sum extensive-form games, thus allowing algorithms to focus their search on a set of …

In sequential decision making, heuristic search algorithms allow exploiting both the initial
situation and an admissible heuristic to efficiently search for an optimal solution, often for …

This project is concerned with learning to take decisions in complex domains, in games in
particular. Previous work assumes that massive data resources are available for training, but …

This research explored the viability and effectiveness of hyper-agent approaches to general
game playing within the Ludii game system, a general game framework that defines games …

Artificial Intelligence (AI) is slowly integrating into our everyday lives. Some of these AI
applications must cooperate with humans and each other. Such cooperation is essential for …