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Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...
Dealing with uncertainties and lack of knowledge about problems and situations, there is a perpetual difficulty to evaluate the situations and action values in all time steps. On the other hand, the design of critics which delicately guide the agent even w ...
In this paper we describe a new computational model of switching between path-planning and cue-guided navigation strategies. It is based on three main assumptions: (i) the strategies are mediated by separate memory systems that learn independently and in p ...
Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the follow ...
A novel sequential approach is proposed for the multiperiod synthesis of Heat Exchanger Networks (HEN) and Utility Systems of chemical processes and energy systems. The framework can be used also for single period problems. Given the set of hot and cold pr ...
Bidirectional action-effect associations play a fundamental role in intentional action control and the development of the mirror neuron system. However, it has been questioned if infants are able to acquire bidirectional action-effect associations (i.e., a ...
We examine the behavior of multiagent networks where information-sharing is subject to a positive communications cost over the edges linking the agents. We consider a general mean-square-error formulation, where all agents are interested in estimating the ...
In chess, a series of moves is made until a delayed sparse feedback (win, loss) is issued, which makes it impossible to evaluate the value of a single move. There are powerful reinforcement learning (RL) algorithms, which can cope with these sequential dec ...
We address the problem of learning a classifier from distributed data over a number of arbitrarily connected machines without exchange of the datapoints. Our purpose is to train a neural network at each machine as if the entire dataset was locally availabl ...
Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise ...