6,085 research outputs found
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of âstrongâ artificial intelligence in robotics are brought forward
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
A New Constructivist AI: From Manual Methods to Self-Constructive Systems
The development of artificial intelligence (AI) systems has to date been largely one of manual labor. This constructionist approach to AI has resulted in systems with limited-domain application and severe performance brittleness. No AI architecture to date incorporates, in a single system, the many features that make natural intelligence general-purpose, including system-wide attention, analogy-making, system-wide learning, and various other complex transversal functions. Going beyond current AI systems will require significantly more complex system architecture than attempted to date. The heavy reliance on direct human specification and intervention in constructionist AI brings severe theoretical and practical limitations to any system built that way.
One way to address the challenge of artificial general intelligence (AGI) is replacing a top-down architectural design approach with methods that allow the system to manage its own growth. This calls for a fundamental shift from hand-crafting to self-organizing architectures and self-generated code â what we call a constructivist AI approach, in reference to the self-constructive principles on which it must be based. Methodologies employed for constructivist AI will be very different from todayâs software development methods; instead of relying on direct design of mental functions and their implementation in a cog- nitive architecture, they must address the principles â the âseedsâ â from which a cognitive architecture can automatically grow. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift
06031 Abstracts Collection -- Organic Computing -- Controlled Emergence
Organic Computing has emerged recently as a challenging vision for
future information processing systems, based on the insight that we
will soon be surrounded by large collections of autonomous systems
equipped with sensors and actuators to be aware of their environment,
to communicate freely, and to organize themselves in order to perform
the actions and services required. Organic Computing Systems will
adapt dynamically to the current conditions of its environment, they
will be self-organizing, self-configuring, self-healing,
self-protecting, self-explaining, and context-aware.
From 15.01.06 to 20.01.06, the Dagstuhl Seminar 06031 ``Organic
Computing -- Controlled Emergence\u27\u27 was held in the International
Conference and Research Center (IBFI), Schloss Dagstuhl.
The seminar was characterized by the very constructive search for
common ground between engineering and natural sciences, between
informatics on the one hand and biology, neuroscience, and chemistry
on the other. The common denominator was the objective to build
practically usable self-organizing and emergent systems or their
components.
An indicator for the practical orientation of the seminar was the
large number of OC application systems, envisioned or already under
implementation, such as the Internet, robotics, wireless sensor
networks, traffic control, computer vision, organic systems on chip,
an adaptive and self-organizing room with intelligent sensors or
reconfigurable guiding systems for smart office buildings. The
application orientation was also apparent by the large number of
methods and tools presented during the seminar, which might be used as
building blocks for OC systems, such as an evolutionary design
methodology, OC architectures, especially several implementations of
observer/controller structures, measures and measurement tools for
emergence and complexity, assertion-based methods to control
self-organization, wrappings, a software methodology to build
reflective systems, and components for OC middleware.
Organic Computing is clearly oriented towards applications but is
augmented at the same time by more theoretical bio-inspired and
nature-inspired work, such as chemical computing, theory of complex
systems and non-linear dynamics, control mechanisms in insect swarms,
homeostatic mechanisms in the brain, a quantitative approach to
robustness, abstraction and instantiation as a central metaphor for
understanding complex systems.
Compared to its beginnings, Organic Computing is coming of age. The OC
vision is increasingly padded with meaningful applications and usable
tools, but the path towards full OC systems is still complex. There is
progress in a more scientific understanding of emergent processes. In
the future, we must understand more clearly how to open the
configuration space of technical systems for on-line
modification. Finally, we must make sure that the human user remains
in full control while allowing the systems to optimize
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Continual Lifelong Learning with Neural Networks: A Review
Humans and animals have the ability to continually acquire, fine-tune, and
transfer knowledge and skills throughout their lifespan. This ability, referred
to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
that together contribute to the development and specialization of our
sensorimotor skills as well as to long-term memory consolidation and retrieval.
Consequently, lifelong learning capabilities are crucial for autonomous agents
interacting in the real world and processing continuous streams of information.
However, lifelong learning remains a long-standing challenge for machine
learning and neural network models since the continual acquisition of
incrementally available information from non-stationary data distributions
generally leads to catastrophic forgetting or interference. This limitation
represents a major drawback for state-of-the-art deep neural network models
that typically learn representations from stationary batches of training data,
thus without accounting for situations in which information becomes
incrementally available over time. In this review, we critically summarize the
main challenges linked to lifelong learning for artificial learning systems and
compare existing neural network approaches that alleviate, to different
extents, catastrophic forgetting. We discuss well-established and emerging
research motivated by lifelong learning factors in biological systems such as
structural plasticity, memory replay, curriculum and transfer learning,
intrinsic motivation, and multisensory integration
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