14192001
Emergence of Social Norms in Complex Networks
This paper studies the problem that how social norms emerge even though agents are selfish and attempt to only maximize their own utility. We propose a new rule for social interactions. The rule is called Highest Rewarding Neighborhood (HRN). The HRN rule allows agents to remain selfish and be able to break relationships in order to maximize their utility. Our experiment shows that when agents are able to break unrewarding relationships that a Pareto-optimum strategy arises as the social normal. In addition we conclude the rate and amount of Pareto-optimum strategy that arises is dependent on the network structure when the networks are dynamic, and the rate is independent of the network structure when the networks are static.
1229519
A decision-theoretic approach for designing proactive communication in multi-agent teamwork
Techniques that support effective communication during teamwork processes are of particular importance. Psychological study shows that an effective team often can anticipate information exchange among the team and communicate relevant information proactively. Proactive communication is crucial for understanding and sharing common goals and for cooperative actions. Communication can be valuable if it assists agents with new and timely information; it also has cost because it consumes network resources such as bandwidth. To address these issues, we present a new model that uses information production and need to capture the complex multi-agent communication process and a dynamic decision-theoretic determination of communication strategies. We also introduce a generic utility function and an algorithm, DTPC (Decision-Theoretic Proactive Communication), that focuses on representing information production and need of team members and resolving decision interactions among them for making decisions.
35439746
Role-Based and Agent-Oriental Teamwork Modeling
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
40423470
Proactive Communication in Observation-Based Agent Team Corporation
14748203
A history sensitive cascade model in diffusion networks
Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
10735385
Data-Driven Feature-Based 3D Face Synthesis
This paper presents a novel data-driven method for creating varied realistic face models by synthesizing a set of facial features according to intuitive high-level control parameters. Our method takes as examples 3D face scans in order to exploit the variations presented in the real faces of individuals. We use an automatic model fitting approach for the 3D registration problem. Once we have a common surface representation for each example, we form feature shape spaces by applying principal component analysis (PCA) to the data sets of facial feature shapes. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures. Apart from an initial tuning of feature point positions and assignment of texture attribute values, our method is fully automated.
1164569
Impact of time-selective fading on the performance of quasi-orthogonal space-time-coded OFDM systems
In this paper, we study the impact of time-selective fading on quasi-orthogonal space-time (ST) coded orthogonal frequency-division multiplexing (OFDM) systems over frequency-selective Rayleigh fading channels. OFDM is robust against frequency-selective fading, but it is more vulnerable to time-selective fading than single-carrier systems. In ST-OFDM, channel time variations cause not only intercarrier interference among different subcarriers in one OFDM symbol, but also intertransmit-antenna interference. We quantify the impact of time-selective fading on the performance of quasi-orthogonal ST-OFDM systems by deriving, via an analytical approach, the expressions of carrier-to-interference and signal-to-interference-plus-noise ratios. We observe that system error performance is insensitive to changes in vehicle speeds and the channel power-delay profile, but very sensitive to changes in the number of subcarriers. We also evaluate the performance of five different detection schemes in the presence of time-selective fading. We show that although there exist differences in their relative performances, all these detection schemes suffer from an irreducible error floor.
17359946
Optimized Cross-Layer Design for Scalable Video Transmission Over the IEEE 802.11e Networks
A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design.
36049595
The Implementation of a Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) on Imote2
Wireless Sensor networks are gaining a role of importance in the research community. In this paper, we choose Imote2 to establish our testbed according to the analysis and the comparison of the hardware capabilities and software characteristics of the current WSNs products. Moreover, we present our hardware, software platforms and "30 motes" testbed to validate and evaluate our Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) while further propose the rudiment of our general routing and data dissemination testbed. In the test stage, experimental results show that each mote in the network obtains the corresponding role and takes correct actions to handle every receiving message.
16765186
Insider DoS Attacks on Epidemic Propagation Strategies of Network Reprogramming in Wireless Sensor Networks
Network reprogramming is a crucial service in wireless sensor networks (WSNs) that relies on epidemic strategy for spreading software updates by just having a local view of the networks. Securing the process of network reprogramming is essential in some certain WSNs applications, state-of-the-art secure network reprogramming protocols for WSNs aim for the efficient source authentication and integrity verification of code image, however, due to the resource constrains of WSNs, existing secure network reprogramming protocols are vulnerable to Denial of Service (DoS) attacks when sensor nodes can be compromised (insider DoS attacks). In this paper, we identify different types of DoS attacks exploiting the epidemic propagation strategies used by Deluge and propose corresponding analysis models to attempt to quantify the cost of these attacks damage. Simulation further shows the impact of insider DoS attacks on network reprogramming in WSNs.