Computational Trust

The explorative mind-map is a dynamic framework, that emerges automatically from the input, it gets. It is unlike a verificative modeling system where existing (human) thoughts are placed and connected together. In this regard, explorative mind-maps change their size continuously, being adaptive with connectionist cells inside; mind-maps process data input incrementally and offer lots of possibilities to interact with the user through an appropriate communication interface. With respect to a cognitive-motivated situation like a conversation between partners, mind-maps become interesting as they are able to process stimulating signals whenever they occur. If these signals are close to an own understanding of the world, then the conversational partner becomes automatically more trustful than if the signals do not or less match the own knowledge scheme. In this (position) paper, we therefore motivate explorative mindmaps as a cognitive engine and propose these as a decision support engine to foster trust.

Previous work on computational trust has delivered robust aggregation engines that proved competent in estimating the trustworthiness of agents. However, most of these approaches were evaluated through simulation using simple and static agents' populations that did not consider the social account of trust. In this paper, we show by experimental analysis that these approaches tend to perform poorly when the populations of agents form social structures that evolve with the situation and the relationship existing between partners. Moreover, we present an approach to social-aware computational trust based on Social Tuner, a new component that we developed that infers the benevolence of the trustee toward the truster from the available evidence. We show by experimental analysis that this approach outperforms the social-less aggregation engines in a relevant way. The experiments are run using a model of agents' behavior grounded on the literature of social trust and benevolence that we have developed and describe in detail in this paper.

Properties of trust are becoming widely studied in several applications within the computational domain. On the contrary, negative trust attribution is less well-defined and related issues are yet to be approached and resolved. We present a natural deduction calculus for trust protocols and its negative forms, distrust and mistrust. The calculus deals efficiently with forms of trust transitivity and negative trust multiplication and we briefly illustrate some possible applications.

Trust is a construct of paramount importance in society. Accordingly, computational trust is evolving fast in order to allow trust in artificial societies. Despite the advances in this research field, most computational trust approaches evaluate trust by estimating the trustworthiness of the agents under evaluation (the trustees), without however distinguishing between the different dimensions of trustworthiness, such as ability and benevolence. In this paper, we propose different techniques to extract the ability of the trustee in the task at hand and to infer the benevolence of the trustee toward the truster when the trust judgment is made. Moreover, we propose to dynamically change the relative importance and impact of both ability and benevolence on the perceived trustworthiness of the trustee, taking into consideration the development of the relationship between the truster and the trustee and the disposition of the truster in the specific situation. Finally, we set an experimental scenario to evaluate our approach. The results obtained from these experiments show that the proposed techniques significantly improve the reliability of the estimation of the trustworthiness of agents.

Despite relevant insights from socio-economics, little research in multiagent systems has addressed the interconnections between trust and normative notions such as contracts and sanctions. Focusing our attention on scenarios of betrayal, in this paper we combine the use of trust and sanctions in a negotiation process. We describe a scenario of dyadic relationships between truster agents, which make use of trust and/or sanctions, and trustees characterized by their ability and integrity, which may influence their attitude toward betrayal. Both agent behavior models are inspired in socio-economics literature. Through simulation, we show the virtues and shortcomings of exploiting trust, sanctions and a combination of both.

Computational trust systems are getting popular in several domains such as social networks, grid computing and business-to-business systems. However, the estimation of the trustworthiness of agents is not trivial in scenarios where the existing trust evidences are scarce. We propose an online, situation-aware trust model that uses the information gain metric to dynamically extract tendencies of failure of target agents, improving the process of selection of partners in a relevant way. Experimental results presented in this paper show that our proposal outperforms other trust approaches in contextual scenarios.

In this era of digital economy, commercial relationships between business partners are increasing in flexibility, with new business binds being created whenever a business opportunity arises. Moreover, the instability in demand increases the need for enterprises to procure new partners as well as the associated risk of dealing with partners that may be unknown beforehand. Therefore, enterprises need mechanisms that allow to evaluate the confidence they have on their current and potential new, unknown, partners, and to monitor this confidence in a continuous and automatic way. This paper presents our computational trust model, which was inspired in the concept of the hysteresis of trust and betrayal and in the asymmetry principle of human psychology. Our model allows to estimate the trustworthiness of agents using different features of the dynamics of trust. Additionally, we present a study on the effect of preselecting partners based on their trustworthiness in automated negotiation processes. The study was conducted experimentally using our agent-based Electronic Institution framework for e-Contracting, which includes a normative environment and an automatic negotiation service, as well as the mentioned computational trust service. The results obtained show that, in identified conditions, business clients benefit from preselecting partners based on trust prior to the negotiation phase.

An Electronic Institution includes a normative environment with rules and norms for agents' interoperability, and is also a service providing platform that assists agents in the task of establishing and conducting normative relationships (contracts). Using this platform, agents representing organizations willing to engage in a collective contractual activity select partners according to different factors, including their capabilities, current business needs and information on past business experiences that may be used as inputs to trust building. In our framework we have designed a tightly coupled connection between electronic contract monitoring and a computational trust model. In this paper, we explain the rationale behind this connection and detail how it is materialized. In particular, we explain how our situation-aware trust model relies on past contractual behavior to dynamically build up a trustworthiness image of each agent that can be helpful for future encounters. Experiments with simplified scenarios show the effectiveness of our approach.

Computational trust systems are getting popular in several domains such as social networks, grid computing and business-to-business systems. However, the estimation of the trustworthiness of agents is not trivial in scenarios where the existing trust evidences are scarce. We propose an online, situation-aware trust model that uses the information gain metric to dynamically extract tendencies of failure of target agents, improving the process of selection of partners in a relevant way. Experimental results presented in this paper show that our proposal outperforms other trust approaches in contextual scenarios.

Computational trust systems are currently considered enabler tools for the automation and the general acceptance of global electronic business-tobusiness processes, such as the sourcing and the selection of business partners outside the sphere of relationships of the selector. However, most of the existing trust models use simple statistical techniques to aggregate trust evidences into trustworthiness scores, and do not take context into consideration. In this paper we propose a situation-aware trust model composed of two components: Sinalpha, an aggregator engine that embeds properties of the dynamics of trust; and CF, a technique that extracts failure tendencies of agents from the history of their past events, complementing the value derived from Sinalpha with contextual information. We experimentally compared our trust model with and without the CF technique. The results obtained allow us to conclude that the consideration of context is of vital importance in order to perform more accurate selection decisions.

An Electronic Institution includes a normative environment with rules and norms for agents' interoperability, and is also a service providing platform that assists agents in the task of establishing and conducting normative relationships (contracts). Using this platform, agents representing organizations willing to engage in a collective contractual activity select partners according to different factors, including their capabilities, current business needs and information on past business experiences that may be used as inputs to trust building. In our framework we have designed a tightly coupled connection between electronic contract monitoring and a computational trust model. In this paper, we explain the rationale behind this connection and detail how it is materialized. In particular, we explain how our situation-aware trust model relies on past contractual behavior to dynamically build up a trustworthiness image of each agent that can be helpful for future encounters. Experiments with simplified scenarios show the effectiveness of our approach.

The explorative mind-map is a dynamic framework, that emerges automatically from the input, it gets. It is unlike a verificative modeling system where existing (human) thoughts are placed and connected together. In this regard, explorative mind-maps change their size continuously, being adaptive with connectionist cells inside; mind-maps process data input incrementally and offer lots of possibilities to interact with the user through an appropriate communication interface. With respect to a cognitive-motivated situation like a conversation between partners, mind-maps become interesting as they are able to process stimulating signals whenever they occur. If these signals are close to an own understanding of the world, then the conversational partner becomes automatically more trustful than if the signals do not or less match the own knowledge scheme. In this (position) paper, we therefore motivate explorative mindmaps as a cognitive engine and propose these as a decision support engine to foster trust.

Online dating is one domain, which would benefit from the application of computational trust. One of the problems with the application of traditional computational trust models, as identified in our previous work, is authenticity of information provided by parties which helps other users ascertain whether they want to go on dates. In this position paper, we suggest a solution: a concordance based framework for a game to be built into a online dating system. The game provokes users to provide spontaneous and thus more genuine data about themselves. The key feature of the game is the respect for privacy that supports and encourages the user to provide authentic information. Future work will comprise of implementation of the game and its user testing.

Urbano, J. et al Trust and Normative Control in Multi-Agent Systems 43 KEYWORD ABSTRACT Multi-agent systems Computational Trust Normative Control Betrayal Despite relevant insights from socio-economics, little research in multi-agent systems has addressed the interconnections between trust and normative notions such as contracts and sanctions. Focusing our attention on scenarios of betrayal, in this paper we combine the use of trust and sanctions in a negotiation process. We describe a scenario of dyadic relationships between truster agents, which make use of trust and/or sanctions, and trustee agents, characterized by their ability and integrity, which may influence their attitude toward betrayal. Both agent behavior models are inspired in socio-economics literature. Through simulation, we show the virtues and shortcomings of using trust, sanctions, and a combination of both in processes of selection of partners.

In its list of top ten smartphone risks, the European Union Agency for Network and Information Security ranks Network Spoofing Attacks as number 6. In this paper, we present how we have validated different computational trust management techniques by means of implemented prototypes in real devices to mitigate malicious legacy Wi- Fi hotspots including spoofing attacks. Then we explain how some of these techniques could be more easily deployed on a large scale thanks to simply using the available extensions of Hotspot 2.0, which could potentially lead to a new standard to improve Wi-Fi networks trustworthiness.