27,940 research outputs found
Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors
In this work we investigate knowledge acquisition as performed by multiple
agents interacting as they infer, under the presence of observation errors,
respective models of a complex system. We focus the specific case in which, at
each time step, each agent takes into account its current observation as well
as the average of the models of its neighbors. The agents are connected by a
network of interaction of Erd\H{o}s-Renyi or Barabasi-Albert type. First we
investigate situations in which one of the agents has a different probability
of observation error (higher or lower). It is shown that the influence of this
special agent over the quality of the models inferred by the rest of the
network can be substantial, varying linearly with the respective degree of the
agent with different estimation error. In case the degree of this agent is
taken as a respective fitness parameter, the effect of the different estimation
error is even more pronounced, becoming superlinear. To complement our
analysis, we provide the analytical solution of the overall behavior of the
system. We also investigate the knowledge acquisition dynamic when the agents
are grouped into communities. We verify that the inclusion of edges between
agents (within a community) having higher probability of observation error
promotes the loss of quality in the estimation of the agents in the other
communities.Comment: 10 pages, 7 figures. A working manuscrip
Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields
Research into several aspects of robot-enabled reconnaissance of random
fields is reported. The work has two major components: the underlying theory of
information acquisition in the exploration of unknown fields and the results of
experiments on how humans use sensor-equipped robots to perform a simulated
reconnaissance exercise.
The theoretical framework reported herein extends work on robotic exploration
that has been reported by ourselves and others. Several new figures of merit
for evaluating exploration strategies are proposed and compared. Using concepts
from differential topology and information theory, we develop the theoretical
foundation of search strategies aimed at rapid discovery of topological
features (locations of critical points and critical level sets) of a priori
unknown differentiable random fields. The theory enables study of efficient
reconnaissance strategies in which the tradeoff between speed and accuracy can
be understood. The proposed approach to rapid discovery of topological features
has led in a natural way to to the creation of parsimonious reconnaissance
routines that do not rely on any prior knowledge of the environment. The design
of topology-guided search protocols uses a mathematical framework that
quantifies the relationship between what is discovered and what remains to be
discovered. The quantification rests on an information theory inspired model
whose properties allow us to treat search as a problem in optimal information
acquisition. A central theme in this approach is that "conservative" and
"aggressive" search strategies can be precisely defined, and search decisions
regarding "exploration" vs. "exploitation" choices are informed by the rate at
which the information metric is changing.Comment: 34 pages, 20 figure
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Network constraints on learnability of probabilistic motor sequences
Human learners are adept at grasping the complex relationships underlying
incoming sequential input. In the present work, we formalize complex
relationships as graph structures derived from temporal associations in motor
sequences. Next, we explore the extent to which learners are sensitive to key
variations in the topological properties inherent to those graph structures.
Participants performed a probabilistic motor sequence task in which the order
of button presses was determined by the traversal of graphs with modular,
lattice-like, or random organization. Graph nodes each represented a unique
button press and edges represented a transition between button presses. Results
indicate that learning, indexed here by participants' response times, was
strongly mediated by the graph's meso-scale organization, with modular graphs
being associated with shorter response times than random and lattice graphs.
Moreover, variations in a node's number of connections (degree) and a node's
role in mediating long-distance communication (betweenness centrality) impacted
graph learning, even after accounting for level of practice on that node. These
results demonstrate that the graph architecture underlying temporal sequences
of stimuli fundamentally constrains learning, and moreover that tools from
network science provide a valuable framework for assessing how learners encode
complex, temporally structured information.Comment: 29 pages, 4 figure
Parametric Surfaces for Augmented Architecture representation
Augmented Reality (AR) represents a growing communication channel, responding to the need to expand reality with additional information, offering easy and engaging access to digital data. AR for architectural representation allows a simple interaction with 3D models, facilitating spatial understanding of complex volumes and topological relationships between parts, overcoming some limitations related to Virtual Reality. In the last decade different developments in the pipeline process have seen a significant advancement in technological and algorithmic aspects, paying less attention to 3D modeling generation. For this, the article explores the construction of basic geometries for 3D modelās generation, highlighting the relationship between geometry and topology, basic for a consistent normal distribution. Moreover, a critical evaluation about corrective paths of existing 3D models is presented, analysing a complex architectural case study, the virtual model of Villa del Verginese, an emblematic example for topological emerged problems. The final aim of the paper is to refocus attention on 3D model construction, suggesting some "good practices" useful for preventing, minimizing or correcting topological problems, extending the accessibility of AR to people engaged in architectural representation
Collaboration in an Open Data eScience: A Case Study of Sloan Digital Sky Survey
Current science and technology has produced more and more publically
accessible scientific data. However, little is known about how the open data
trend impacts a scientific community, specifically in terms of its
collaboration behaviors. This paper aims to enhance our understanding of the
dynamics of scientific collaboration in the open data eScience environment via
a case study of co-author networks of an active and highly cited open data
project, called Sloan Digital Sky Survey. We visualized the co-authoring
networks and measured their properties over time at three levels: author,
institution, and country levels. We compared these measurements to a random
network model and also compared results across the three levels. The study
found that 1) the collaboration networks of the SDSS community transformed from
random networks to small-world networks; 2) the number of author-level
collaboration instances has not changed much over time, while the number of
collaboration instances at the other two levels has increased over time; 3)
pairwise institutional collaboration become common in recent years. The open
data trend may have both positive and negative impacts on scientific
collaboration.Comment: iConference 201
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