273,329 research outputs found
Unrolled Graph Learning for Multi-Agent Collaboration
Multi-agent learning has gained increasing attention to tackle distributed
machine learning scenarios under constrictions of data exchanging. However,
existing multi-agent learning models usually consider data fusion under fixed
and compulsory collaborative relations among agents, which is not as flexible
and autonomous as human collaboration. To fill this gap, we propose a
distributed multi-agent learning model inspired by human collaboration, in
which the agents can autonomously detect suitable collaborators and refer to
collaborators' model for better performance. To implement such adaptive
collaboration, we use a collaboration graph to indicate the pairwise
collaborative relation. The collaboration graph can be obtained by graph
learning techniques based on model similarity between different agents. Since
model similarity can not be formulated by a fixed graphical optimization, we
design a graph learning network by unrolling, which can learn underlying
similar features among potential collaborators. By testing on both regression
and classification tasks, we validate that our proposed collaboration model can
figure out accurate collaborative relationship and greatly improve agents'
learning performance
Merging Two Worlds: Agent-Based Simulation Methods for Autonomous Systems
This chapter recommends the increased use of agent-based simulation methods to support the design, development, testing, and operational use of autonomous systems. This recommendation is motivated by deriving taxonomies for intelligent software agents and autonomous robotic systems from the public literature, which shows their similarity: intelligent software agents can be interpreted as the virtual counterparts of autonomous robotic systems. This leads to examples of how simulation can be used to significantly improve autonomous system research and development in selected use cases. The chapter closes with observations on the operational effects of possible emergent behaviour and the need to align the research agenda with other relevant organisations facing similar challenges
Benzimidazole-based derivatives as privileged scaffold developed for the treatment of the RSV infection: a computational study exploring the potency and cytotoxicity profiles
Respiratory syncytial virus (RSV) has been identified as a main cause of hospitalisation in infants and children. To date, the current therapeutic arsenal is limited to ribavirin and palivizumab with variable efficacy. In this work, starting from a number of in-house series of previously described anti-RSV agents based on the benzimidazole scaffold, with the aim at gaining a better understanding of the related chemical features involved in potency and safety profiles, we applied a computational study including two focussed comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results allowed us to derive useful suggestions for the design of derivatives and also to set up statistical models predicting the potency and selectivity index (SI1/4CC50/EC50) of any new analogue prior to synthesis. Accordingly, here, we discuss preliminary results obtained through the applied exhaustive QSAR analyses, leading to design and synthesise more effective anti-RSV agents
Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents
This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms
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Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
Cultural dialects of real and synthetic emotional facial expressions
In this article we discuss the aspects of designing facial expressions for virtual humans (VHs) with a specific culture. First we explore the notion of cultures and its relevance for applications with a VH. Then we give a general scheme of designing emotional facial expressions, and identify the stages where a human is involved, either as a real person with some specific role, or as a VH displaying facial expressions. We discuss how the display and the emotional meaning of facial expressions may be measured in objective ways, and how the culture of displayers and the judges may influence the process of analyzing human facial expressions and evaluating synthesized ones. We review psychological experiments on cross-cultural perception of emotional facial expressions. By identifying the culturally critical issues of data collection and interpretation with both real and VHs, we aim at providing a methodological reference and inspiration for further research
Exploring cultural factors in human-robot interaction: A matter of personality?
This paper proposes an experimental study to investigate task-dependence and cultural-background dependence of the personality trait attribution on humanoid robots. In Human-Robot Interaction, as well as in Human-Agent Interaction research, the attribution of personality traits towards intelligent agents has already been researched intensively in terms of the social similarity or complementary rule. These two rules imply that humans either tend to like others with similar personality traits or complementary personality traits more. Even though state of the art literature suggests that similarity attraction happens for virtual agents, and complementary attraction for robots, there are many contradictions in the findings. We assume that searching the explanation for personality trait attribution in the similarity and complementary rule does not take into account important contextual factors. Just like people equate certain personality types to certain professions, we expect that people may have certain personality expectations depending on the context of the task the robot carries out. Because professions have different social meaning in different national culture, we also expect that these task-dependent personality preferences differ across cultures. Therefore suggest an experiment that considers the task-context and the cultural background of users
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