5,796 research outputs found
Exploring personality-targeted UI design in online social participation systems
We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extroversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation. Copyright © 2013 ACM
Rage Against the Machines: How Subjects Learn to Play Against Computers
We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment.learning, fictitious play, imitation, reinforcement, trial & error, strategic teaching, Cournot duopoly, experiments, internet
CASOG: Conservative Actor-critic with SmOoth Gradient for Skill Learning in Robot-Assisted Intervention
Robot-assisted intervention has shown reduced radiation exposure to
physicians and improved precision in clinical trials. However, existing
vascular robotic systems follow master-slave control mode and entirely rely on
manual commands. This paper proposes a novel offline reinforcement learning
algorithm, Conservative Actor-critic with SmOoth Gradient (CASOG), to learn
manipulation skills from human demonstrations on vascular robotic systems. The
proposed algorithm conservatively estimates Q-function and smooths gradients of
convolution layers to deal with distribution shift and overfitting issues.
Furthermore, to focus on complex manipulations, transitions with larger
temporal-difference error are sampled with higher probability. Comparative
experiments in a pre-clinical environment demonstrate that CASOG can deliver
guidewire to the target at a success rate of 94.00\% and mean backward steps of
14.07, performing closer to humans and better than prior offline reinforcement
learning methods. These results indicate that the proposed algorithm is
promising to improve the autonomy of vascular robotic systems.Comment: 13 pages, 5 figure, preprin
Constraint-aware Policy for Compliant Manipulation
Robot manipulation in a physically-constrained environment requires compliant
manipulation. Compliant manipulation is a manipulation skill to adjust hand
motion based on the force imposed by the environment. Recently, reinforcement
learning (RL) has been applied to solve household operations involving
compliant manipulation. However, previous RL methods have primarily focused on
designing a policy for a specific operation that limits their applicability and
requires separate training for every new operation. We propose a
constraint-aware policy that is applicable to various unseen manipulations by
grouping several manipulations together based on the type of physical
constraint involved. The type of physical constraint determines the
characteristic of the imposed force direction; thus, a generalized policy is
trained in the environment and reward designed on the basis of this
characteristic. This paper focuses on two types of physical constraints:
prismatic and revolute joints. Experiments demonstrated that the same policy
could successfully execute various compliant-manipulation operations, both in
the simulation and reality. We believe this study is the first step toward
realizing a generalized household-robot
Trust among the Avatars: A Virtual World Experiment, with and without Textual and Visual Cues
We invited “residents” of a virtual world who vary in real-world age and occupation to play a trust game with stakes comparable to “in world” wages. In different treatments, the lab wall was adorned with an emotively suggestive photograph, a suggestive text was added to the instructions, or both a photo and text were added. We find high levels of trust and reciprocity that appear still higher for non-student and older subjects. Variation of results by treatment suggests that both photographic and textual cues influenced the level of trust but not that of trustworthiness.trust; experiment; internet; virtual world; priming
Collective Intelligence for Object Manipulation with Mobile Robots
While natural systems often present collective intelligence that allows them
to self-organize and adapt to changes, the equivalent is missing in most
artificial systems. We explore the possibility of such a system in the context
of cooperative object manipulation using mobile robots. Although conventional
works demonstrate potential solutions for the problem in restricted settings,
they have computational and learning difficulties. More importantly, these
systems do not possess the ability to adapt when facing environmental changes.
In this work, we show that by distilling a planner derived from a
gradient-based soft-body physics simulator into an attention-based neural
network, our multi-robot manipulation system can achieve better performance
than baselines. In addition, our system also generalizes to unseen
configurations during training and is able to adapt toward task completions
when external turbulence and environmental changes are applied
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