30,184 research outputs found
Robot Swarms in an Uncertain World: Controllable Adaptability
There is a belief that complexity and chaos are essential for adaptability.
But life deals with complexity every moment, without the chaos that engineers
fear so, by invoking goal-directed behaviour. Goals can be programmed. That is
why living organisms give us hope to achieve adaptability in robots. In this
paper a method for the description of a goal-directed, or programmed,
behaviour, interacting with uncertainty of environment, is described. We
suggest reducing the structural (goals, intentions) and stochastic components
(probability to realise the goal) of individual behaviour to random variables
with nominal values to apply probabilistic approach. This allowed us to use a
Normalized Entropy Index to detect the system state by estimating the
contribution of each agent to the group behaviour. The number of possible group
states is 27. We argue that adaptation has a limited number of possible paths
between these 27 states. Paths and states can be programmed so that after
adjustment to any particular case of task and conditions, adaptability will
never involve chaos. We suggest the application of the model to operation of
robots or other devices in remote and/or dangerous places.Comment: Journal web page & a lot of robotic related papers
www.ars-journal.co
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
Stability and Diversity in Collective Adaptation
We derive a class of macroscopic differential equations that describe
collective adaptation, starting from a discrete-time stochastic microscopic
model. The behavior of each agent is a dynamic balance between adaptation that
locally achieves the best action and memory loss that leads to randomized
behavior. We show that, although individual agents interact with their
environment and other agents in a purely self-interested way, macroscopic
behavior can be interpreted as game dynamics. Application to several familiar,
explicit game interactions shows that the adaptation dynamics exhibits a
diversity of collective behaviors. The simplicity of the assumptions underlying
the macroscopic equations suggests that these behaviors should be expected
broadly in collective adaptation. We also analyze the adaptation dynamics from
an information-theoretic viewpoint and discuss self-organization induced by
information flux between agents, giving a novel view of collective adaptation.Comment: 22 pages, 23 figures; updated references, corrected typos, changed
conten
Constrained speaker linking
In this paper we study speaker linking (a.k.a.\ partitioning) given
constraints of the distribution of speaker identities over speech recordings.
Specifically, we show that the intractable partitioning problem becomes
tractable when the constraints pre-partition the data in smaller cliques with
non-overlapping speakers. The surprisingly common case where speakers in
telephone conversations are known, but the assignment of channels to identities
is unspecified, is treated in a Bayesian way. We show that for the Dutch CGN
database, where this channel assignment task is at hand, a lightweight speaker
recognition system can quite effectively solve the channel assignment problem,
with 93% of the cliques solved. We further show that the posterior distribution
over channel assignment configurations is well calibrated.Comment: Submitted to Interspeech 2014, some typos fixe
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