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This paper considers frequency-division spatial spectrum sharing (FD-SSS) based cognitive radio (CR) networks. In this network, the spectrum of primary users (PU) can be divided into a number of sub-bands each of which can only be accessed by one secondary user (SU). This setting combines both advantages of spatial spectrum sharing and temporal spectrum sharing methods. However, there are three major...
Probability Collective (PC) is an extension of conventional game theory for distributed optimization by sampling an explicitly parameterized probability distributions over the space of solutions. This parameterization introduces more effective computational models to solve complex systems-level optimization problems. In this paper we present a study of using this collective learning model for adaptive...
Probability Collective (PC) is a methodology for distributed optimization by sampling an explicitly parameterized probability distribution over the space of solutions. This parameterization effectively utilizes granules of probability distributions to construct computational models for solving complex systems-level optimization problems. In this paper we present a study of using this probabilistic...
Formation flying (FF) is a critical element in NASA's future deep-space missions. Terrestrial Planet Finder (TPF), NASA's first space-based mission to directly observe planets outside our own solar system, will rely on FF to achieve the functionality and benefits of a large instrument using multiple lower cost smaller spacecraft. Many key network design problems for such FF missions can be formulated...
A hierarchical reinforcement learning method based on heuristic reward function is proposed to solve the problem of “curse of dimensionality”, that is the states space will grow exponentially in the number of features, and low convergence speed. The method can reduce state spaces greatly and can enhance the speed of the study. Choose actions with favorable purpose and efficiency so as to optimize...
In this paper, we first describe the basic principle and the method of the A* algorithm. And we analyze the reason that the A* algorithm influences speed when it is searching for the optimum route in network game map. Then we give the optimization scheme from the aspects of node data structure to the maintenance of the open queue. At the same time, the optimization scheme is evaluated and tested by...
In the context of multiple emergencies occurring simultaneously, the optimal allocation of relief resources to multiple emergency locations is a challenging issue in emergency management. This work presents a noncooperative complete information game model for resource allocation and an algorithm for calculating Nash equilibrium (NE). In this model, the players represent the multiple emergency locations,...
This paper studies the definition, classification, model and algorithm of vehicle routing optimization which is considered the major task in logistic distribution and it is also a series of widely used NP difficult issues. Whatpsilas more, the research applies game theory, which is often taken as a branch of applied mathematics and economics, to VRP simulation and evaluates the efficiency of game...
In this paper, we propose an AI tool for generating plausible paths of racers based on the A* algorithm. User can define the race by providing a race course of 3D model and weights of the devised turn and heuristic functions in our system. The search space for path-finding is represented by a grid. Then, the proposed cost map generator automatically generates necessary information of the race course...
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