Shared understanding is commonly seen as essential to the success of coalition operations, and cu... more Shared understanding is commonly seen as essential to the success of coalition operations, and current research efforts are attempting to develop techniques and technologies to improve shared understanding in military coalition contexts. In spite of this, our understanding of what the term 'shared understanding' actually means is surprisingly poor. In part, this problem is attributable to the difficulty in comprehending the true nature of understanding itself, although confusions also arise about the precise nature of the differences between shared understanding and ostensibly similar constructs, such as shared mental models and shared situation awareness. In this paper, we attempt to improve our understanding of shared understanding by exploring the nature of understanding, situation awareness and mental models. Following Wittgenstein, we suggest that understanding is best conceived of as something akin to an ability, and shared understanding is, we suggest, best conceived of as the sharing of individual forms of understanding by multiple agents. We further suggest that mental models may provide a mechanistic realization for some of the performances that manifest understanding, and that situation awareness should best be seen as a particular kind of understanding, namely a dynamic form of situational understanding. In addition to discussing the nature of understanding and shared understanding, we also discuss their potential relevance to military coalition operations. We propose that shared understanding is important to coalition operations because it contributes to improvements in coalition performance, the optimal use of limited communication assets, and an improved sense of group cohesion, group solidarity and mutual trust.
Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06, 2006
Current computational trust models are usually built either on an agent's direct experience of an... more Current computational trust models are usually built either on an agent's direct experience of an interaction partner (interaction trust) or reports provided by third parties about their experiences with a partner (witness reputation). However, both of these approaches have their limitations. Models using direct experience often result in poor performance until an agent has had a sufficient number of interactions to build up a reliable picture of a particular partner and witness reports rely on self-interested agents being willing to freely share their experience. To this end, this paper presents Certified Reputation (CR), a novel model of trust that can overcome these limitations. Specifically, CR works by allowing agents to actively provide third-party references about their previous performance as a means of building up the trust in them of their potential interaction partners. By so doing, trust relationships can quickly be established with very little cost to the involved parties. Here we empirically evaluate CR and show that it helps agents pick better interaction partners more quickly than models that do not incorporate this form of trust.
An antiserum was raised against an amino acid sequence predicted from the DNA sequence of amyloid... more An antiserum was raised against an amino acid sequence predicted from the DNA sequence of amyloid β-protein precursor (ABPP), and it was then affinity-purified. This affinity-purified antibody (anti-GID) intensely stained neurons and dystrophic neurites in plaques of Alzheimer's disease (AD) patients, but marginally stained neurons of age-matched normal individuals. Anti-GID antibody detected a series of protein bands with a molecular weight centered at 100,000 and a second band at 55,000 on a blot of the human brain particulate fraction. It also stained a set of bands with a molecular weight around 95,000 and a doublet of Mr 16,000 in the soluble fraction. A band at Mr 35,000 was detected in the soluble fraction prepared from brain tissue of AD patients but not from control brain tissue. A strong immunostaining of AD sections with anti-GID and the presence of a Mr 35,000 band unique to AD might reflect an altered processing of ABPP in AD brains
Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in w... more Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent's likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent's environment.
Modern warfare's situation awareness and operation planning requires analyzing a vast amount of i... more Modern warfare's situation awareness and operation planning requires analyzing a vast amount of information, ranging broadly from intelligence reports to data from autonomous sensors. Correctly assessing the credibility of such information in a timely manner is as crucial to decision making as being able to obtain the information in the first place. However, manual information quality assessment is time-consuming and laborious, especially considering the amount of information that modern automated knowledge solutions can deliver.
The number of computational trust models has been increasing rapidly in recent years, yet their a... more The number of computational trust models has been increasing rapidly in recent years, yet their applications for automating trust evaluation are still limited. The main obstacle is the difficulty of selecting a suitable trust model and adapting it for particular trust modeling requirements, which vary greatly due to the subjectivity of human trust. The Personalized Trust Framework (PTF) presented in this paper aims to address this problem by providing a mechanism for human users to capture their trust evaluation process in order for it to be replicated by computers. In more details, a user can specify how he selects a trust model based on information about the subject whose trustworthiness he needs to evaluate and how that trust model is configured. This trust evaluation process is then automated by the PTF making use of the trust models flexibly plugged into the PTF by the user. By so doing, the PTF enable users reuse and personalize existing trust models to suit their requirements without having to reprogram those models.
Proceedings of the 5th Annual ACM Web Science Conference on - WebSci '13, 2013
ABSTRACT In this paper, we present a software tool to help emergency planners at Hampshire County... more ABSTRACT In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities.... more Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
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