Decentralized reputation-based trust for assessing agent reliability under aggregate feedback

Reputation mechanisms allow agents to establish trust in other agents' intentions and capabilities in the absence of direct interactions. In this paper, we are concerned with establishing trust on the basis of reputation information in open, decentralized systems of interdependent autonomous agents. We present a completely decentralized reputation mechanism to increase the accuracy of agents' assessments of other agents' capabilities and allow them to develop appropriate levels of trust in each other as providers of reliable information. Computer simulations show the reputation system's ability to track an agent's actual capabilitie

Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration

We study the inverse power index problem for weighted voting games: the problem of finding a weighted voting game in which the power of the players is as close as possible to a certain target distribution. Our goal is to find algorithms that solve this problem exactly. Thereto, we study various subclasses of simple games, and their associated representation methods. We survey algorithms and impossibility results for the synthesis problem, i.e., converting a representation of a simple game into another representation. We contribute to the synthesis problem by showing that it is impossible to compute in polynomial time the list of ceiling coalitions of a game from its list of roof coalitions, and vice versa. Then, we proceed by studying the problem of enumerating the set of weighted voting games. We present first a naive algorithm for this, running in doubly exponential time. Using our knowledge of the

Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have emph{nonlinear} preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmar

Editorial

When we look at successful sales processes occurring in practice, we find they combine two techniques which have been studied separately in the literature. Recommender systems are used to suggest additional products or accessories to include in the bundle under consideration, and multi-issue negotiation focuses on optimizing the precise configuration of the bundle and its price. In this paper, we pursue the automation of such interactive sales processes. We present some key insights about, as well as a procedure for locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences, learnt by the shop in interactions with previous customers, with current data about the ongoing negotiation process with the current customer. We present a memory- and a model-based method for online learning customer preferences and discuss their pros and cons. The performance of our system is illustrated using extensive computer experiments involving simulated customers with highly non-linear preferences which the system has no trouble learning

Negotiating over bundles and prices using aggregate knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers

Negotiating over bundles and prices using aggregate knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers

Decentralized reputation-based trust for assessing agent reliability under aggregate feedback

Reputation mechanisms allow agents to establish trust in other agents’ intentions and capabilities in the absence of direct interactions. In this paper, we are concerned with establishing trust on the basis of reputation information in open, decentralized systems of interdependent autonomous agents. We present a completely decentralized reputation mechanism to increase the accuracy of agents’ assessments of other agents’ capabilities and allow them to develop appropriate levels of trust in each other as providers of reliable information. Computer simulations show the reputation system’s ability to track an agent’s actual capabilities. The work described in this paper was performed in the context of the cim project on Cybernetic Incident Management, sponsored by the Dutch government (senter) as project number TSIT2021. See http://www.almende.com/cim/ for more information. We are grateful to Floortje Alkemade, Pınar Yolum and Pieter Jan ’t Hoen for stimulating discussions, and to three anonymous referees for helpful comments

A Versatile Approach to Combining Trust Values for Making Binary Decisions

In open multi-agent systems, agents typically need to rely on others for the provision of information or the delivery of resources. However, since different agents’ capabilities, goals and intentions do not necessarily agree with each other, trust can not be taken for granted in the sense that an agent can not always be expected to be willing and able to perform optimally from a focal agent’s point of view. Instead, the focal agent has to form and update beliefs about other agents’ capabilities and intentions. Many different approaches, models and techniques have been used for this purpose in the past, which generate trust and reputation values. In this paper, employing one particularly popular trust model, we focus on the way an agent may use such trust values in trust-based decision-making about the value of a binary variable. We use computer simulation experiments to assess the relative efficacy of a variety of decision-making methods. In doing so, we argue for systematic analysis of such methods beforehand, so that, based on an investigation of characteristics of different methods, different classes of parameter settings can be distinguished. Whether, on average across many random problem instances, a certain method performs better or worse than alternatives is not the issue, given that the agent using the method always exists in a particular setting. We find that combining trust values using our likelihood method gives performance which is relatively robust to changes in the setting an agent may find herself in