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1. Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics

   Coscia, A; Chau, D H: (January 2020, Workshop on Trust and Expertise in Visual Analytics (TREX))

   null (Ed.)

   Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives they can take instead of identifying, communicating and addressing the bias as it occurs. We argue that mixed-initiative design principles can and should incorporate cognitive bias mitigation strategies directly through development of mitigation techniques embedded in the system to address cognitive biases in situ. We identify domain experts in machine learning adopting visual analytics techniques and systems that incorporate existing mixed-initiative principles and examine their potential to support bias mitigation strategies. This examination considers the unique perspective these experts bring to visual analytics and is situated in existing user-centered systems that make exemplary use of design principles informed by cognitive theory. We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field. Finally, we contribute open questions and research directions for designers seeking to adopt visual analytics techniques that incorporate bias-aware initiatives in future systems. 

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2. Mixed-Initiative Level Design with RL Brush

   https://doi.org/10.1007/978-3-030-72914-1_27  

   Delarosa, Omar; Dong, Hang; Ruan, Mindy; Khalifa, Ahmed; Togelius, Julian (December 2021, Artificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021)

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   This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment manual human level-design through the addition of AI-generated suggestions. Here, we apply RL Brush to designing levels for the classic puzzle game Sokoban. We put the tool online and tested it in 39 different sessions. The results show that users using the AI suggestions stay around longer and their created levels on average are more playable and more complex than without. 

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3. APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Natural Language Instructions

   https://doi.org/10.1109/VLHCC.2018.8506506  

   Li, Toby Jia-Jun; Labutov, Igor; Li, Xiaohan Nancy; Zhang, Xiaoyi; Shi, Wenze; Ding, Wanling; Mitchell, Tom M.; Myers, Brad A. (October 2018, 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'18))

   A key challenge for generalizing programming-by-demonstration (PBD) scripts is the data description problem - when a user demonstrates performing an action, the system needs to determine features for describing this action and the target object in a way that can reflect the user's intention for the action. However, prior approaches for creating data descriptions in PBD systems have problems with usability, applicability, feasibility, transparency and/or user control. Our APPINITE system introduces a multimodal interface with which users can specify data descriptions verbally using natural language instructions. APPINITE guides users to describe their intentions for the demonstrated actions through mixed-initiative conversations. APPINITE constructs data descriptions for these actions from the natural language instructions. Our evaluation showed that APPINITE is easy-to-use and effective in creating scripts for tasks that would otherwise be difficult to create with prior PBD systems, due to ambiguous data descriptions in demonstrations on GUIs. 

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4. Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging

   https://doi.org/10.1109/IPSN54338.2022.00041  

   Miller, John; Soltanaghai, Elahe; Duvall, Raewyn; Chen, Jeff; Bhat, Vikram; Pereira, Nuno; Rowe, Anthony (May 2022, 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN))

   Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter. 

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5. Mario Level Generation From Mechanics Using Scene Stitching

   https://doi.org/10.1109/CoG47356.2020.9231692  

   Green, Michael Cerny; Mugrai, Luvneesh; Khalifa, Ahmed; Togelius, Julian (August 2020, IEEE Conference on Games)

   null (Ed.)

   Video game tutorials allow players to gain mastery over game skills and mechanics. To hone players’ skills, it is beneficial from practicing in environments that promote individ- ual player skill sets. However, automatically generating environ- ments which are mechanically similar to one-another is a non- trivial problem. This paper presents a level generation method for Super Mario by stitching together pre-generated “scenes” that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications. Given a sequence of mechanics, the proposed system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The proposed system outputs levels that can be beaten using a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare the proposed system to a greedy method that selects scenes that maximize the number of matched mechanics. Unlike the greedy approach, the proposed system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process. 

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