neural networks research group
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Research Areas
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Evolutionary Computation
Our research in this area focuses primarily on evolving neural networks, or
Neuroevolution
, but also includes work in probabilistic model-b...
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Neuroevolution
In difficult real-world learning tasks such as controlling robots, playing games, or pursuing or evading an enemy, there are no direct targets that would specify the correct action for every situation...
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Control
Control theory and engineering study how to produce the desired behavior in a variety of dynamical systems, e.g. the electro-mechanical systems in robots and the chemical systems in manufacturing plan...
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Robotics
Our work in robotics focuses on learning hierarchies of sensorimotor schemas through unsupervised and reinforcement learning, and on developing intelligent controllers through neuroevolution.
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Game Playing
Games constitute an effective platform for developing and testing artificial intelligence techniques: they are well defined and easy to implement, yet challenging and fun. Our work in this area focuse...
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Multiobjective Optimization
Instead of finding a single optimal solution to any given problem, multiobjective methods aim at finding a Pareto-front, which represents all of the trade-offs between objectives within the domain. A ...
Cognitive Science
Our research in Cognitive Science aims to better understand human development and learning through computational modeling. We have active projects in Natural Language Processing, Schema Learning, Epis...
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Concept and Schema Learning
An agent can truly understand the meaning of its knowledge structures, and utilize them most effectively, only if that knowledge is grounded on sensorimotor interactions with the world. We aim at bu...
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Episodic Memory
Episodic memory is the record of events that the individual experiences throughout his/her/its life. Recent neurobiological results suggest that the events are stored temporarily in the hippocampus a...
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Natural Language Processing
Our research in Natural Language Processing aims at bridging the gap between subsymbolic representations and complex high-level behavior. The models are based on subsymbolic mechanisms but aim at expl...
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Self-Organization
Our work in this area includes extending the Self-Organizing Map architecture (SOM; Kohonen, 1982; 1997; von der Malsburg, 1975) with lateral connections, hierarchies, sequential inputs, and growing n...
Computational Neuroscience
A computational model is a complete description of how a neural system functions, and in that sense the ultimate specification of neuroscience theory. The models are constrained by and validated with...
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Visual Cortex
Our goal is to understand the development and function of the visual cortex through computational modeling. The main idea is that the visual cortex is a continuously-adapting structure in a dynamic eq...
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Self-Organization
Our work in this area includes extending the Self-Organizing Map architecture (SOM; Kohonen, 1982; 1997; von der Malsburg, 1975) with lateral connections, hierarchies, sequential inputs, and growing n...
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Episodic Memory
Episodic memory is the record of events that the individual experiences throughout his/her/its life. Recent neurobiological results suggest that the events are stored temporarily in the hippocampus a...
Other Areas
This category includes papers published by neural network group members that are not in our traditional research areas.
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Self-Organization
Our work in this area includes extending the Self-Organizing Map architecture (SOM; Kohonen, 1982; 1997; von der Malsburg, 1975) with lateral connections, hierarchies, sequential inputs, and growing n...
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Reinforcement Learning
Reinforcement Learning tasks are learning problems where the desired behavior is not known; only sparse feedback on how well the agent is doing is provided. Reinforcement Learning techniques include t...
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Algorithm Portfolios
Algorithm portfolio methods operate in problem domains for which there are multiple algorithms with complementary strengths. The portfolio method applies patterns learned from experience to better all...
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Unsupervised Learning and Clustering
Unsupervised learning does not require any annotation or labeling from a human teacher and learns from purely unlabeled data. The most standard unsupervised learning task is clustering, i.e. grouping...
Applications
Much of our work on applications involves using neuroevolution in real-world domains, but also includes reinforcement learning in robotics, packet routing, and satellite communication, unsupervised le...
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Control
Control theory and engineering study how to produce the desired behavior in a variety of dynamical systems, e.g. the electro-mechanical systems in robots and the chemical systems in manufacturing plan...
•
Robotics
Our work in robotics focuses on learning hierarchies of sensorimotor schemas through unsupervised and reinforcement learning, and on developing intelligent controllers through neuroevolution.
•
Game Playing
Games constitute an effective platform for developing and testing artificial intelligence techniques: they are well defined and easy to implement, yet challenging and fun. Our work in this area focuse...
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Satisfiability
The problem of propositional satisfiability (SAT) is the classic NP-complete problem. It asks whether a Boolean expression is satisfiable: whether an assignment of Boolean values to its variables exis...