Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

Mixed-initiative Variable Autonomy for Remotely Operated Mobile Robots

This article presents an Expert-guided Mixed-initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster-response-inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: (a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; (b) in the context of the simulated robot experiment, Mixed-initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Last, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: (a) the design of context-aware MI control systems; and (b) the conflict for control between the robot’s MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed.

A Large-scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search

Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this article, we help to position it with respect to other research areas within conversational artificial intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To this end, we perform a large-scale dialogue analysis of more than 150K transcripts from 16 publicly available dialogue datasets. These datasets were collected to inform different dialogue-based tasks including conversational search. We extract different patterns of mixed initiative from these dialogue transcripts and use them to compare dialogues of different types. Moreover, we contrast the patterns found in information-seeking dialogues that are being used for research purposes with the patterns found in virtual reference interviews that were conducted by professional librarians. The insights we provide (1) establish close relations between conversational search and other conversational AI tasks and (2) uncover limitations of existing conversational datasets to inform future data collection tasks.

Procedural Dungeon Generation: A Survey

Procedural content generation (PCG) is a method of content creation entirely or partially done by computers. PCG is popularly employed in game development to produce game content, such as maps and levels. Representative examples of games using PCG are Rogue (1998), which introduced the rogue­like genre, and No Man’s Sky (2016), which generated whole worlds with fauna and flora. PCG may generate final contents, ready to be added to a game, or intermediate contents, which might be polished by human designers or work as an input level sketch to be interpreted by a level translator. In this paper, we survey the current state of procedural dungeon generation (PDG) research, a PCG subarea, applied in the context of games. For each work we selected in this survey, we examined and compared how they created game features, what type of level structure and representation they propose, which content generation strategy they applied, and, finally, we classify them according to the taxonomy of procedural content generation proposed by Togelius et al. (2016). The most relevant findings of our survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which temporarily blocks the player progression, and; (3) mixed-initiative approaches, i.e., software that helps human designers by making suggestions to the levels being created, are little explored.