27,518 research outputs found
The Problem of Mental Action
In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network
To ensure that a robot is able to accomplish an extensive range of tasks, it
is necessary to achieve a flexible combination of multiple behaviors. This is
because the design of task motions suited to each situation would become
increasingly difficult as the number of situations and the types of tasks
performed by them increase. To handle the switching and combination of multiple
behaviors, we propose a method to design dynamical systems based on point
attractors that accept (i) "instruction signals" for instruction-driven
switching. We incorporate the (ii) "instruction phase" to form a point
attractor and divide the target task into multiple subtasks. By forming an
instruction phase that consists of point attractors, the model embeds a subtask
in the form of trajectory dynamics that can be manipulated using sensory and
instruction signals. Our model comprises two deep neural networks: a
convolutional autoencoder and a multiple time-scale recurrent neural network.
In this study, we apply the proposed method to manipulate soft materials. To
evaluate our model, we design a cloth-folding task that consists of four
subtasks and three patterns of instruction signals, which indicate the
direction of motion. The results depict that the robot can perform the required
task by combining subtasks based on sensory and instruction signals. And, our
model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied
video is available at this https://youtu.be/a73KFtOOB5
- …