Games are an ideal domain for exploring the capabilities of artificial intelligence (AI) within a constrained environment and a fixed set of rules, where problemsolving techniques can be developed and evaluated before being applied to more complex real-world problems (Schaeffer 2001). AI has notably been applied to board games, such as chess, Scrabble, and backgammon, creating competition that has sped the development of many heuristicbased search techniques (Schaeffer 2001). Over the past decade, there has been increasing interest in research based on video game AI, which was initiated by Laird and van Lent (2001) in their call for the use of video games as a test bed for AI research. They saw video games as a potential area for iterative advancement in increasingly sophisticated scenarios, eventually leading to the development of human-level AI. Buro (2003) later called for increased research in real-time strategy (RTS) games as they provide a sandbox for exploring various complex challenges that are central to game AI and many other problems.Video games are an attractive alternative to robotics for AI research because they increasingly provide a complex and realistic environment for simulation, with few of the messy properties (and cost) of real-world equipment (Buro 2004;Laird and van Lent 2001). They also present a number of challenges that set them apart from the simpler board games that AI has famously been applied to in the past. Video games often have real-time constraints that prevent players from thinking extensively about each action, randomness that prevents players from completely planning future events, and hidden information that prevents players from
In this paper, we present an agent which uses case-based reasoning to play the real-time strategy game StarCraft. Cases are gathered through observation of human actions in particular situations, which are extracted from game log files. Cases are then used by a domain-independent casebased reasoning framework to make in-game actions based on human actions in similar situations. This work aims to demonstrate a method for more easily creating better agents in real-time strategy games.
Abstract:Commercial 3D modeling applications tend to be difficult and time consuming to use due to complex interfaces and functionality. In this paper we present a simple and intuitive interface for modeling "blobby" 3D objects using touch input. Objects are defined by sketching and modifying contours of cross-sectional slices. Two-touch interactions are used to zoom, rotate and slice the object. The resulting application allows rapid creation of 3D models and looks promising for medical imaging applications. A drawback is that intuitiveness depends on a user's mental abilities such as 3D vision and the ability to develop a mental model and compare it with visual data.
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