Games have been the perfect test-beds for artificial intelligence research for the
characteristics that widely exist in real-world scenarios. Learning and optimisation, decision …

Games have been the perfect test-beds for artificial intelligence research for the
characteristics that widely exist in real-world scenarios. Learning and optimisation, decision …

Recent progress in Game AI has demonstrated that given enough data from human
gameplay, or experience gained via simulations, machines can rival or surpass the most …

Search algorithms based on combinatorial multi-armed bandits (CMABs) are promising for
dealing with state-space sequential decision problems. However, current CMAB-based …

This paper introduces Local Learner (2L), an algorithm for providing a set of reference
strategies to guide the search for programmatic strategies in two-player zero-sum games …

Games have played crucial role in advancing research in Artificial Intelligence and tracking
its progress. In this article, a new proposal for game AI competition is presented. The goal is …

A key challenge for planning systems in real-time multiagent domains is to search in large
action spaces to decide an agent's next action. Previous works showed that handcrafted …

Professional StarCraft game players are likely to focus on the management of the most
important group of units (called the main force) during gameplay. Although macro-level skills …

Search-based systems have shown to be effective for planning in zero-sum games.
However, search-based approaches have important disadvantages. First, the decisions of …

Real-time strategy (RTS) games are a challenging application for artificial intelligence (AI)
methods. This is because they involve simultaneous play and adversarial reasoning that is …