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Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages...
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications...
This paper presents MP-Draughts (MultiPhase-Draughts): a multiagent environment for Draughts, where one agent - named IIGA- is built and trained such as to be specialized for the initial and the intermediate phases of the games and the remaining ones for the final phases of them. Each agent of MP-Draughts is a neural network which learns almost without human supervision (distinctly from the world...
Behaviors, in which the characters such as conciliatory (Con), neutral (Neu), or aggressive (Agg) define a 'psychological' aspect of the human personality, play an important role for negotiation agent. Elsewhere, learning in negotiation is fundamental for understanding human behaviors as well as for developing new solution concepts. In this paper, a brief description of SISINE (Integrated System of...
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