Despite their many advantages, e-Businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents ’ trust and reputation scores. 1.

We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.

Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others ’ states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argument-based negotiation research is to reach its full potential.

Abstract. In this paper we develop a notion of “objective trust ” for Software Agents, that is trust of, or between, Agents based on actual experiences between those Agents. Experiential objective trust allows Agents to make decisions about how to select other Agents when a choice has to be made. We define a mechanism for such an “objective Trust-Based Agent ” (oTB-Agent), and present experimental results in a simulated trading environment based on an Intelligent Networks (IN) scenario. The trust one Agent places in another is dynamic, updated on the basis of each experience. We use this to investigate three questions related to trust in Multi-Agent Systems (MAS), first how trust affects the formation of trading partnerships, second, whether trust developed over a period can equate to “loyalty ” and third whether a less than scrupulous Agent can exploit the individual nature of trust to its advantage. 1

For agents, one of the advantages of being in a society is the satisfaction of those goals whose success depends on the abilities of other agents. In turn, societies are controlled by norms and, consequently, agents must be able first to model the society in which they exist, and then to identify the different relationships, due to norms, in which they might be involved in order to act appropriately.

We argue that modelling emotions among agents in artificial societies will further the computational study of social norms. The appraisal theory of emotions is presented as theoretical underpinning of Jon Elster's view that social norms are sustained not only by material sanctions but also by emotions such as shame and contempt. Appraisal theory suggests the following twofold relationship between social norms and emotions: First, social norms play an important role in the generation of emotions; second, emotion regulation depends heavily on the influence of social norms. Based on these insights, we present an emotion-based view on the influential study by Conte and Castelfranchi (1995); without mentioning emotions, they argue that a function of social norms is aggression control. Appraisal theory offers a principled framework for the development of TABASCO, a three-layer agent architecture incorporating social norms. At the macro level, the computational study of social norms can profit by economic and sociobiological theories, which suggest that emotions play an important role in sustaining norms of cooperation and reciprocity. We show how appraisal theory can serve as a link between the macro and micro levels, and summarize the potential benefits from the development of TABASCO.

Recent works in multi-agent systems have identified agent behaviors that can develop and sustain mutually beneficial cooperative relationships with like-minded agents and can resist exploitation from selfish agents. Researchers have proposed the use of a probabilistic reciprocity scheme that uses summary information from past interactions to decide whether or not to honor a request for help from another agent. This behavior has been found to be close to optimal in homogeneous groups and outperform exploiters in mixed groups. A major shortcoming of these experiments, however, is that the composition of the group in term of agent behaviors is fixed. We believe that real-life rational agents, on the contrary, will change their behaviors based on observed performances of di#erent behavioral traits with the goal of maximizing performance. In this paper, we present results from experiments on two distinct domains with population groups whose behavioral composition changes based on the performance of the agents. Based on the experimental results, we identify ecological niches for variants of exploitative selfish agents and robust reciprocative agents.

Since the agent paradigm emerged, agent researchers have faced the challenge of build-ing open societies in which heterogeneous and independently designed entities can work towards similar or different ends. Open societies involve agents that do not necessarily share the same interests, that do not know and might not trust each other, but that can work together and help each other. One of the key omissions in the computational rep-resentation of open societies relates to the need for norms in multi-agent systems, that help to cope with the heterogeneity, the autonomy and the diversity of interests among their members. This also requires agents that can reason about norms because their par-ticipation in a society, rather than predefined, must be voluntary. So, these agents must understand why norms should be adopted and complied with, and why the authority and the power of agents in a society must be respected. This thesis addresses both the in-troduction of norms in systems of autonomous agents, and the modelling of agents that can reason about norms. The thesis makes three main contributions. First, it develops a framework of norma-tive concepts that enables agents to reason about norms and the society in which they participate. Second, it provides the means for agents to identify situations of power, and to use these powers both for the satisfaction of their goals and to understand why the goals of other agents must be satisfied. This is required since agents in an open soci-ety must interact with other agents which are also autonomous, and power represents a means to influence them. Third, this thesis provides models for agents that adopt and comply with norms not as an end, but as the result of a deliberation process in which their goals and motivations are taken into account. This enables agents to voluntarily decide whether participating in a society is important for the achievement of their goals.

Abstract. In an unpredictable, heterogeneous world, intelligent agents depend on accurate social information; reputation, among the preeminent artifacts to transmit social evaluations, has been receiving growing attention by social scientists. A realistic description of reputation must include inaccurate information; in this paper, based on the distinction between image (agents ’ believed evaluation of a target) and reputation (circulating evaluation, without reference to the evaluation source), we model the spreading of information in a simple market with the presence of liars and the possibility of retaliation. While fear of retaliation inhibits the spreading of image, the detached character of reputation can be a cause of inaccuracy; The two forces could balance in different settings. In a set of simulations, with agents using the Repage platform for management of image and reputation, we compare the usage of image alone with the usage of both image and reputation. Reputation is shown to be preferable over image to allow for faster discover of scarce good sellers. 1

This article deals with the process of formation of norms in societies in which individuals act according to their own cognitive framework under communication. The individuals acquire information from others and interpret it. The way of individual’s action is revised through changing the source of information and reforming the method of interpretation. Through the revising mechanisms, assemblages sharing cognitive frameworks establish. At first individuals adopt the same source of information and then arrange the shared cognitive framework. The assemblage are considered as groups with common norms, since the same cognitive framework gives actions coherency. In the process of formation the two revising mechanisms function in turn. The intensity of interaction among individuals affects the period to build norms and the size of groups sharing norms. The size develops under strong interaction but the period comes to long. The dependency of average size of norms on the strength is a power.