Real-time strategy (RTS) games represent a mainstream genre of video games. They are also practical test-beds for intelligent agents, which have received considerable interest from Artificial Intelligence (AI) researchers, in particular game AI researchers. Terrain knowledge understanding is a fundamental issue for RTS agents and map decomposition methods can help AI agents in representing terr...

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initiali...

In this work, we explore two Monte-Carlo planning approaches: Upper Confidence Tree (UCT) and Rapidlyexploring Random Tree (RRT). These Monte-Carlo planning approaches are applied in a real-time strategy game for solving the path finding problem. The planners are evaluated using a grid-based representation of our game world. The results show that the UCT planner solves the path planning problem...

Planning in domains with temporal and numerical properties is an important research problem. One application of this is the resource production problem in real-time strategy (RTS) games, where players attempt to achieve the goal of producing a certain amount of resources as fast as possible. In this paper, we develop an online planner for resource production in the RTS game of Wargus, where the...

Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic ...

In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent’s disposition is limited to what can be observed through scouting. Information is costly, since units dedicated to scouting are unavailable for other purposes, and the enemy will resist scouting attempts. It is important to infer as much as possib...

The current game industry around the world is one of the fastest growing industries. One gaming genre that is very popular is the real-time strategy games. However, current implementations of games apply extensive usage of FSM that makes them highly predictable and provides less replayability. Thus, this paper looks at the possibility of employing case-based plan recognition for NPCs so as to m...

We study the problem of learning probabilistic models of high-level strategic behavior in the real-time strategy (RTS) game StarCraft. The models are automatically learned from sets of game logs and aim to capture the common strategic states and decision points that arise in those games. Unlike most work on behavior/strategy learning and prediction in RTS games, our data-centric approach is not...

Game tree search algorithms such as minimax have been used with enormous success in turn-based adversarial games such as Chess or Checkers. However, such algorithms cannot be directly applied to real-time strategy (RTS) games because a number of reasons. For example, minimax assumes a turn-taking game mechanics, not present in RTS games. In this paper we present RTMM, a real-time variant of the...