67,513 research outputs found
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Resource Sharing and Coevolution in Evolving Cellular Automata
Evolving one-dimensional cellular automata (CAs) with genetic algorithms has
provided insight into how improved performance on a task requiring global
coordination emerges when only local interactions are possible. Two approaches
that can affect the search efficiency of the genetic algorithm are coevolution,
in which a population of problems---in our case, initial configurations of the
CA lattice---evolves along with the population of CAs; and resource sharing, in
which a greater proportion of a limited fitness resource is assigned to those
CAs which correctly solve problems that fewer other CAs in the population can
solve. Here we present evidence that, in contrast to what has been suggested
elsewhere, the improvements observed when both techniques are used together
depend largely on resource sharing alone.Comment: 8 pages, 1 figure; http://www.santafe.edu/~evca/rsc.ps.g
Deferred Action: Theoretical model of process architecture design for emergent business processes
E-Business modelling and ebusiness systems development assumes fixed company resources,
structures, and business processes. Empirical and theoretical evidence suggests that company resources
and structures are emergent rather than fixed. Planning business activity in emergent contexts requires
flexible ebusiness models based on better management theories and models . This paper builds and
proposes a theoretical model of ebusiness systems capable of catering for emergent factors that affect
business processes. Drawing on development of theories of the ‘action and design’class the Theory of
Deferred Action is invoked as the base theory for the theoretical model. A theoretical model of flexible
process architecture is presented by identifying its core components and their relationships, and then
illustrated with exemplar flexible process architectures capable of responding to emergent factors.
Managerial implications of the model are considered and the model’s generic applicability is discussed
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
The emergence of Deep Neural Networks (DNNs) has revolutionized various
domains, enabling the resolution of complex tasks spanning image recognition,
natural language processing, and scientific problem-solving. However, this
progress has also exposed a concerning vulnerability: adversarial examples.
These crafted inputs, imperceptible to humans, can manipulate machine learning
models into making erroneous predictions, raising concerns for safety-critical
applications. An intriguing property of this phenomenon is the transferability
of adversarial examples, where perturbations crafted for one model can deceive
another, often with a different architecture. This intriguing property enables
"black-box" attacks, circumventing the need for detailed knowledge of the
target model. This survey explores the landscape of the adversarial
transferability of adversarial examples. We categorize existing methodologies
to enhance adversarial transferability and discuss the fundamental principles
guiding each approach. While the predominant body of research primarily
concentrates on image classification, we also extend our discussion to
encompass other vision tasks and beyond. Challenges and future prospects are
discussed, highlighting the importance of fortifying DNNs against adversarial
vulnerabilities in an evolving landscape
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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