Explaining how to play real-time strategy games

Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior

Materiality in information environments: Objects, spaces, and bodies in three outpatient hemodialysis facilities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/1/asi24277.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152032/2/asi24277_am.pd

Animating Humans Dynamically Simulated Characters in Virtual Environments

Animated characters can play the role of teachers or guides, teammates or competitors, or just provide a source of interesting motion in virtual environments. Characters in a compelling virtual environment must have a variety of complex and interesting behaviors, and be responsive to the user’s actions. The difficulty of constructing such synthetic characters currently hinders the development of these environments, particularly when realism is required. In this article, we present one approach to populating virtual environments—using dynamic simulation to generate the motion of characters. We explore this approach’s effectiveness Border collie and Olympic with two virtual environments: the Border collie environment, in which bicycle race environments the user acts as a Border collie to herd robots into a corral, and the test one approach to Olympic bicycle race environment, in which the user participates in a populating virtual worlds bicycle race with synthetic competitors (see Figure 1). using dynamic simulation to Motion for characters in virtual environments can be generated generate characters’ with keyframing, motion capture, or dynamic simulation. All three motions. approaches require a tradeoff between the level of control given to the animator and the automatic nature of the process. Animators require detailed control when creating subtle movements that are unique or highly stylized. Generating expressive facial animations usually requires this low level of control. Automatic methods are beneficial because they can interactively produce motion for characters based on the continuously changing state of the user and other characters in the virtual environment. Keyframing requires that the animator specify critical, or key, positions for the animated objects. The computer then fills in the missing frames by smoothl

How Experts Explain Strategic Behavior During Real-Time Strategy Games

Real-time strategy games, such as Wargus, are examples of complex learning and planning domains that present unique challenges to AI and machine learning. With the drive to acquire planning knowledge from ever fewer examples, learning and planning in this complex, dynamic environment is challenging. Some headway could be made by providing notations in which an expert can annotate examples to help derive additional knowledge, but describing behavior can be problematic if there is a significant mismatch between the notation and the user's conceptualization of their behavior. We conducted a formative study with expert players of a real-time strategy game to determine the structure of the language used by the experts to describe strategy