Theoretical analysis on a traffic-based routing algorithm of mobile agents

© 2005 IEEE.In this paper, we propose a mobile agent-based routing algorithm in which the traffic cost is considered. We define a traffic cost function for each link based on known traffic information and find the probability distribution that mobile agents may select a neighboring node and move to. We theoretically analyze the probability distribution and provide the optimal probability distribution that makes inference on the known traffic information and approximates to a unbiased distribution.Wenyu Qu, Hong Shen, Yingwei Ji

A Verification Framework for Fictitious Play Based Learning Algorithms

Distributed optimisation techniques have gained increasing attention due to fast development of autonomous robots. Many algorithms have been proposed to make optimisation more efficient. In this paper we propose a framework, which is based on probabilistic verification techniques, in order to compare the performance of various game-theoretic algorithms, in particular, fictitious play and its variants, after a finite number of iterations. To demonstrate the effectiveness of the framework, we apply the framework to a game which is inspired by wireless communication network problems, on five variations of fictitious play algorithms

Product-Based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, webs earch and online advertising. The data in those applications is mostly categorical and contains multiple fields, a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfieldcategories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two-large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics