An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information

We study the problem of agent-based negotiation in combinatorial domains. It is difficult to reach optimal agreements in bilateral or multi-lateral negotiations when the agents' preferences for the possible alternatives are not common knowledge. Self-interested agents often end up negotiating inefficient agreements in such situations. In this paper, we present a protocol for negotiation in combinatorial domains which can lead rational agents to reach optimal agreements under incomplete information setting. Our proposed protocol enables the negotiating agents to identify efficient solutions using distributed search that visits only a small subspace of the whole outcome space. Moreover, the proposed protocol is sufficiently general that it is applicable to most preference representation models in combinatorial domains. We also present results of experiments that demonstrate the feasibility and computational efficiency of our approach

On Efficient Mediation Approach to Multi-issue Negotiation with Optimal and Fair Outcomes

ABSTRACT Empirical evidence suggests that self-interested agents often fail to reach optimal agreements in multi-issue negotiations. Most existing negotiation approaches either do not address fairness issues; or do not consider computational concerns. To address these problems, the aim of this research is to investigate negotiation techniques, introducing efficient mediation approaches to support multi-issue, multi-agent negotiations with optimal and fair outcomes under incomplete information setting

Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points

Wi-Fi channel state information (CSI) provides adequate information for recognizing and analyzing human activities. Because of the short distance and low transmit power of Wi-Fi communications, people usually deploy multiple access points (APs) in a small area. Traditional Wi-Fi CSI-based human activity recognition methods adopt Wi-Fi CSI from a single AP, which is not very appropriate for a high-density Wi-Fi environment. In this article, we propose a learning method that analyzes the CSI of multiple APs in a small area to detect and recognize human activities. We introduce a deep learning model to process complex and large CSI from multiple APs. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi APs exist

Mobile Crowdsensing in Software Defined Opportunistic Networks

Mobile crowdsensing is a new paradigm that sharing sensing data collected by mobile devices such as smartphones and tablets. As mobile devices are usually connected by an opportunistic network for data transferring, it is hard to acknowledge the contribution of each mobile user in network forwarding then find a sustainable incentive mechanism. In this paper, we propose a software defined opportunistic network (SDON) scheme for mobile crowdsensing. We design a centralized control structure to manage the opportunistic network and mobile crowdsensing. By the centralized structure, we also design an incentive mechanism for data forwarding and collection in an SDON and solve the optimal decision of mobile devices and the sensing service provider. From the extensive simulation results, our incentive mechanism performs better than original solutions

An Adaptive Context-Aware Transaction Model for Mobile and Ubiquitous Computing

Transaction management for mobile and ubiquitous computing (MUC)aims at providing mobile users with reliable and transparent services anytime anywhere. Traditional mobile transaction models built on client-proxy-server architecture cannot make this vision a reality because (1) in these models, base stations (proxy) are the prerequisite for mobile hosts (client) to connect with databases (server), and 2)few models consider context-based transaction management. In this paper, we propose a new network architecture for MUC transactions, with the goal that people can get online network access and transaction even while moving around; and design a context-aware transaction model and a context-driven coordination algorithm adaptive to dynamically changing MUC transaction context. The simulation results have demonstrated that our model and algorithm can significantly improve the successful ratio of MUC transactions

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology