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Wang F.-Y.,State Key Laboratory of Management and Control for Complex Systems
IEEE Intelligent Systems | Year: 2012

Some observers raise the spectre of a world after a "singularity" in which machine intelligence exceeds human intelligence. That may be unlikely, but the threat of technological capacity still exists. Perhaps the idea of an "open society" combined with cyberspace and intelligent systems could produce a "computational society" that is open, impartial, and fair. © 2012 IEEE. Source


Wang F.-Y.,State Key Laboratory of Management and Control for Complex Systems
IEEE Intelligent Systems | Year: 2012

The flood of big data in cyberspace will require immediate actions from the AI and intelligent systems community to address how we manage knowledge. Besides new methods and systems, we need a total knowledge-management approach that willl require a new perspective on AI. We need Merton's systems in which machine intelligence and human intelligence work in tandem. This should become a normal mode of operation for the next generation of AI and intelligent systems. © 2001-2011 IEEE. Source


Li L.,State Key Laboratory of Management and Control for Complex Systems | Li L.,Harbin Institute of Technology | Li L.,University of Chinese Academy of Sciences | Zeng D.,State Key Laboratory of Management and Control for Complex Systems | And 3 more authors.
Journal of the Association of Information Systems | Year: 2012

Despite the tremendous commercial success of generalized second-price (GSP) keyword auctions, it still remains a big challenge for an advertiser to formulate an effective bidding strategy. In this paper, we strive to bridge this gap by proposing a framework for studying pure-strategy Nash equilibria in GSP auctions. We first analyze the equilibrium bidding behaviors by investigating the properties and distribution of all pure-strategy Nash equilibria. Our analysis shows that the set of all pure-strategy Nash equilibria of a GSP auction can be partitioned into separate convex polyhedra based on the order of bids if the valuations of all advertisers are distinct. We further show that only the polyhedron that allocates slots efficiently is weakly stable, thus allowing all inefficient equilibria to be weeded out. We then propose a novel refinement method for identifying a set of equilibria named the stable Nash equilibrium set (STNE) and prove that STNE is either the same as or a proper subset of the set of the well-known symmetrical Nash equilibria. These findings free both auctioneers and advertisers from complicated strategic thinking. The revenue of a GSP auction on STNE is at least the same as that of the classical Vickrey-Clarke-Groves mechanism and can be used as a benchmark for evaluating other mechanisms. At the same time, STNE provides advertisers a simple yet effective and stable bidding strategy. Source


Xu X.,National University of Defense Technology | Shen D.,CAS Institute of Automation | Shen D.,State Key Laboratory of Management and Control for Complex Systems | Gao Y.-Q.,State Key Laboratory of Management and Control for Complex Systems | And 3 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2012

Learning control of dynamical systems based on Markov decision processes (MDPs) is an interdisciplinary research area of machine learning, control theory, and operations research. The main objective in this research area is to realize data-driven multi-stage optimal control for complex or uncertain dynamical systems. This paper presents a comprehensive survey on the theory, algorithms, and applications of MDP-based learning control of dynamical systems. Emphases are put on recent advances in the theory and methods of reinforcement learning (RL) and adaptive/approximate dynamic programming (ADP), including temporal-difference learning theory, value function approximation for continuous state and action spaces, direct policy search, approximate policy iteration, and adaptive critic designs. Applications and the trends for future research and developments in related fields are also discussed. Copyright © 2012 Acta Automatica Sinica. All rights reserved. Source


Zhang Z.,State Key Laboratory of Management and Control for Complex Systems | Zheng X.,State Key Laboratory of Management and Control for Complex Systems | Zeng D.D.,State Key Laboratory of Management and Control for Complex Systems | Zeng D.D.,University of Arizona | Leischow S.J.,Mayo Medical School
PLoS ONE | Year: 2015

This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms "tobacco" and "lung cancer" from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use. © 2015 Zhang et al. Source

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