Using tags to bootstrap stereotypes and trust

Agents joining multi-agent systems (MAS) face two significant problems: they do not know who to trust and others do not know if they are trustworthy. Our contribution extends trust and stereotype approaches to use a comparison of agents’ observable features, called tags, as an initial indication of expected behaviour. The results show an improvement in agents’ rewards in the early stages of their lifetimes, prior to having sufficient information to use trust or stereotype methods

Bootstrapping trust and stereotypes with tags

In real-world environments, cooperation often emerges amongst agents who are observably similar. Estimating the expected behaviour of another agent is a challenging problem, particularly for new agents who have little or no experience of others. In this paper, we show how observable features can be used to find similar, and hence cooperative, partners. Our contribution extends trust and stereotype approaches, to include comparisons and learning of observable features, called tags. In environments where no reciprocity exists (or where there have been insuf- ficient interactions for reciprocity to take effect) tags have been used to encourage cooperation. The only information available to an agent early in its life is knowledge of its own tags and behaviour. We assume that agents who are observably similar will be behaviourally similar too. Agents use reinforcement learning to take advantage of as much available information as possible, until sufficient experience has been gathered for more established trust and stereotype models to be built. Our results show that using tags improves agents’ rewards in the early stages of their lifetime when used prior to established stereotype and trust algorithms. We demonstrate that tags are successful in supporting cooperation, even when agent behaviour is independent of the partner, because the approach correctly identifies similar agents. Good agents are able to select partners who will act as they do, while bad agents avoid those who are observably similar

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Trust assessment in the context of unrepresentative information

Trust and reputation algorithms are social methods, complementary to security protocols, that guide agents in multi-agent systems (MAS) in identifying trustworthy partners to communicate with. Agents need to interact to complete tasks, which requires delegating to an agent who has the time, resources or information to achieve it. Existing trust and reputation assessment methods can be accurate when they are learning from representative information, however, representative information rarely exists for all agents at all times. Improving trust mechanisms can benefit many open and distributed multi-agent applications. For example, distributing subtasks to trustworthy agents in pervasive computing or choosing who to share safe and high quality files with in a peer-to-peer network. Trust and reputation algorithms use the outcomes from past interaction experiences with agents to assess their behaviour. Stereotype models supplement trust and reputation methods when there is a lack of direct interaction experiences by inferring the target will behave the same as agents who are observably similar. These mechanisms can be effective in MAS where behaviours and agents do not change, or change in a simplistic way, for example, if agents changed their behaviour at the same rate. In real-world networks, agents experience fluctuations in their location, resources, knowledge, availability, time and priorities. Existing work does not account for the resulting dynamic dynamic populations and dynamic agent behaviours. Additionally, trust, reputation and stereotype models encourage repeat interactions with the same subset of agents which increase the uncertainty about the behaviour of the rest of the agent population. In the long term, having a biased view of the population hinders the discovery of new and better interaction partners. The diversity of agents and environments across MAS means that rigid approaches of maintaining and using data keep outdated information in some situations and not enough data in others. A logical improvement is for agents to manage information flexibly and adapt to their situation. In this thesis we present the following contributions. We propose a method to improve partner selection by making agents aware of a lack of diversity in their own knowledge and how to then make alternative behavioural assessments. We present methods for detecting dynamic behaviour in groups of agents, and give agents the statistical tools to decide which data are relevant. We introduce a data-free stereotype method to be used when there are no representative data for a data-driven behaviour assessment. Finally, we consider how agents can summarise agent behaviours to learn and exploit in depth behavioural patterns. The work presented in this thesis is evaluated in a synthetic environment designed to mimic characteristics of real-world networks and are comparable to evaluation environments from prominent trust and stereotype literature. The results show our work improves agents’ average reward from interactions by selecting better partners. We show that the efficacy of our work is most noticeable in environments where agents have sparse data, because it improve agents’ trust assessments under uncertainty

Threat Modelling Guided Trust-based Task Offloading for Resource-constrained Internet of Things

There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource-constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes. Which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this paper, we present a novel trust model-driven system architecture, based on behavioural evidence, that is suitable for resource-constrained IoT devices that supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviours. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behaviour. Hence, we perform a threat modelling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used

Application of Stereotypes in Marketing: Gender Cues and Brand Perception

Stereotypes and their applications and implications have emerged as an important area of investigation in the field of marketing. Yet, despite the growing interest and relevance of stereotypes among marketing scholars, the marketing literature lacks a cohesive understanding of their concept and application. In particular, little research in the field of consumer behavior has examined the effects of stereotypes on brand perception and the subsequent influence on consumer purchase intention. The goal of this dissertation is to create a deeper and more cohesive understanding of stereotypes and their implications for marketing practitioners, with a particular focus on gender stereotypes and brand perception. This dissertation consists of three projects. The first project outlines a conceptual framework of stereotypes and their application in the field of marketing by integrating knowledge from social psychology literature with knowledge from consumer behavior and marketing. Based on this framework, gaps in the marketing literature are identified and suggestions for future research are provided. Building upon project one, project two seeks to identify applications of stereotypes in the field of marketing. In particular, project two explores whether the use of gender stereotypes via gender cues (e.g., colors such as blue or pink) in a product description can influence brand perception along the warmth and competence dimensions and, consequently, impact upon consumer purchase intention. Further, the effect of gender cues is tested dependent upon the gender type of the product. The results indicate that feminine cues enhance perceived warmth which, in turn, increases purchase likelihood for masculine products. Perceived warmth serves as a mediator between the gender cue and purchasing likelihood, yet competence does not. Project three builds upon and integrates the findings from project two by investigating the effect of gender cues in the presence of warmth and competence cues. Thereby, a theoretical framework is proposed which takes multiple warmth and competence cues into account. Subsequently, this theoretical framework is used to assess the effects of gender cues, as well as warmth and competence cues, on consumer perception of brands and their purchase intention. Consistent with the theoretical framework, this project explores that feminine cues enhance perceived brand warmth and purchase intention when paired with a high competence cue, yet backfires when paired with a low competence cue. In contrast, implicit masculine gender cues enhance perceived brand competence and purchase intention when paired with low competence cues yet backfire when combined with high competence cues. Overall, this dissertation proposes recommendations on how practitioners are able to effectively use gender cues (i.e., butterflies, bears, circles, squares, colors such as pink and blue) as part of gender stereotypes within a firm’s marketing efforts (i.e., background of print ads or on the product packaging) to achieve desired consumer brand perception and improve purchase likelihood

Trust assessment in 32 KiB of RAM : multi-application trust-based task offloading for resource-constrained IoT nodes

There is an increasing demand for Internet of Things (IoT) systems comprised of resource-constrained sensor and actuator nodes executing increasingly complex applications, possibly simultaneously. IoT devices will not be able to execute computationally expensive tasks and will require more powerful computing nodes, called edge nodes, for such execution, in a process called computation offloading. When multiple powerful nodes are available, a selection problem arises: which edge node should a task be submitted to? This problem is even more acute when the system is subjected to attacks, such as DoS, or network perturbations such as system overload. In this paper, we present a trust model-based system architecture for computation offloading, based on behavioural evidence. The system architecture provides confidentiality, authentication and non-repudiation of messages in required scenarios and will operate within the resource constraints of embedded IoT nodes. We demonstrate the viability of the architecture with an example deployment of Beta Reputation System trust model on real hardware

Collect, measure, repeat: Reliability factors for responsible AI data collection

The rapid entry of machine learning approaches in our dailyactivities and high-stakes domains demands transparency andscrutiny of their fairness and reliability. To help gauge ma-chine learning models’ robustness, research typically focuseson the massive datasets used for their deployment,e.g., cre-ating and maintaining documentation to understand theirorigin, process of development, and ethical considerations.However, data collection for AI is still typically a one-offpractice, and oftentimes datasets collected for a certain pur-pose or application are reused for a different problem. Addi-tionally, dataset annotations may not be representative overtime, contain ambiguous or erroneous annotations, or be un-able to generalize across domains. Recent research has shownthese practices might lead to unfair, biased, or inaccurate out-comes. We argue that data collection for AI should be per-formed in a responsible manner where the quality of the datais thoroughly scrutinized and measured through a systematicset of appropriate metrics. In this paper, we propose a Re-sponsible AI (RAI) methodology designed to guide the datacollection with a set of metrics for an iterative in-depth analy-sis of thefactors influencing the quality and reliabilityof thegenerated data. We propose a granular set of measurements toinform on theinternal reliabilityof a dataset and itsexternalstabilityover time. We validate our approach across nine ex-isting datasets and annotation tasks and four input modalities.This approach impacts theassessment of data robustnessusedin real world AI applications, where diversity of users andcontent is eminent. Furthermore, it deals with fairness andaccountability aspects in data collection by providing system-atic and transparent quality analysis for data collections