In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, i...

For now over a decade, real-time strategy (RTS) games have been challenging intelligence, human and artificial (AI) alike, as one of the top genre in terms of overall complexity. RTS is a prime example problem featuring multiple interacting imperfect decision makers. Elaborate dynamics, partial observability, as well as a rapidly diverging action space render rational decision making somehow el...

Real-Time Strategy (RTS) games can be abstracted to resource allocation applicable in many fields and industries. We consider a simplified custom RTS game focused on mid-level combat using reinforcement learning (RL) algorithms. There are a number of contributions to game playing with RL in this paper. First, we combine hierarchical RL with a multi-layer perceptron (MLP) that receives higher-or...

Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches [8, 3]. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive. In this paper we introduce the concept of a situation ...

Design is an essential part of all games and narratives, yet designing and implementing believable game strategies can be time consuming and error-prone. This motivates the development and application of systems AI methodologies. Here we demonstrate for the first time the iterative development of agent behaviour for a real-time strategy game (here StarCraft) utilising Behaviour Oriented Design ...

Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal ...

Real-Time Strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer as one player monitors opponents’ (human or other computers) strategies and then updates its own policy using RL methods. In this paper, we firstly examine the suitability of applying the online RL in various computer games. RL application depe...

This thesis is focused on the design of a new path planning algorithm to solve path planning problems in dynamic, partially observable and real-time environments such as Real-Time Strategy (RTS) games. The emphasis is put on fast action selection motivating the use of Monte-Carlo planning techniques. Three main contributions are presented in this thesis. The first contribution is a Monte-Carlo ...

We present a real-time strategy (RTS) game AI agent that integrates multiple specialist components to play a complete game. Based on an analysis of how skilled human players conceptualize RTS gameplay, we partition the problem space into domains of competence seen in expert human play. This partitioning helps us to manage and take advantage of the large amount of sophisticated domain knowledge ...

This poster motivates AI research in the area of real-time strategy (RTS) games and describes the current status of a project whose goals are to implement an RTS game programming environment and to build AIs that eventually can outperform human experts in this challenging and popular domain. 1 Real-Time Strategy Games Commercial computer games are a growing part of the entertainment industry an...