Lik Mui

Notions of reputation in multi-agents systems: a review

Reputation has recently received considerable attention within a number of disciplines such as distributed artificial intelligence, economics, evolutionary biology, among others. Most papers about reputation provide an intuitive approach to reputation which appeals to common experiences without clarifying whether their use of reputation is similar or different from those used by others. This paper argues that reputation is not a single notion but one with multiple parts. After a survey of existing works on reputation, an intuitive typology is proposed summarizing existing works on reputation across diverse disciplines. This paper then describes a simple simulation framework based on evolutionary game theory for understanding the relative strength of the different notions of reputation. Whereas these notions of reputation could only be compared qualitatively before, our simulation framework has enabled us to compare them quantitatively.

Evolution of indirect reciprocity by social information: the role of trust and reputation in evolution of altruism.

The complexity of humans cooperative behavior cannot be fully explained by theories of kin selection and group selection. If reciprocal altruism is to provide an explanation for altruistic behavior, it would have to depart from direct reciprocity, which requires dyads of individuals to interact repeatedly. For indirect reciprocity to rationalize cooperation among genetically unrelated or even culturally dissimilar individuals, information about the reputation of individuals must be assessed and propagated in a population. Here, we propose a new framework for the evolution of indirect reciprocity by social information: information selectively retrieved from and propagated through dynamically evolving networks of friends and acquaintances. We show that for indirect reciprocity to be evolutionarily stable, the differential probability of trusting and helping a reputable individual over a disreputable individual, at a point in time, must exceed the cost-to-benefit ratio of the altruistic act. In other words, the benefit received by the trustworthy must out-weigh the cost of helping the untrustworthy.

Persona: a contextualized and personalized web search

Recent advances in graph-based search techniques derived from Kleinbergs (1997) work have been impressive. This paper further improves the graph-based search algorithm in two dimensions. Firstly, variants of Kleinbergs techniques do not take into account the semantics of the query string nor of the nodes being searched. As a result, polysemy of query words cannot be resolved. This paper presents an interactive query scheme utilizing the simple Web taxonomy provided by the Open Directory Project to resolve meanings of a user query. Secondly, we extend a recently proposed personalized version of the Kleinberg algorithm (Chang et al., 2000). Simulation results are presented to illustrate the sensitivity of our technique. We outline the implementation of our algorithm in the Persona personalized web search system.

Tensor Controlled Local Structure Enhancement of CT Images for Bone Segmentation

This paper addresses the problem of segmenting bone from Computed Tomography (CT) data. In clinical practice, identification of bone is done by thresholding, a method which is simple and fast. Unfortunately, thresholding alone has significant limitations. In particular, segmentation of thin bone structures and of joint spaces is problematic. This problem is particularly severe for thin bones such as in the skull (the paranasal sinus and around the orbit). Another area where current techniques often fail is automatic, reliable and robust identification of individual bones, which requires precise separation of the joint spaces. This paper presents a novel solution to these problems based on three-dimensional filtering techniques. Improvement of the segmentation results in more accurate 3D models for the purpose of surgical planning and intraoperative navigation.

An Adaptive modular neural network with application to unconstrained character recognition

The topology and the capacity of a traditional multilayer neural system, as measured by the number of connections in the network, has surprisingly little impact on its generalization ability. This paper presents a new adaptive modular network that offers superior generalization capability. The new network provides significant fault tolerance, quick adaption to novel inputs, and high recognition accuracy. We demonstrate this paradigm on recognition of unconstrained handwritten characters.

Collaborative sanctioning: applications in restaurant recommendations based on reputation

Collaborative filtering and collaborative sanctioning need to be differentiated. Collaborative filtering concerns with the pooling together of opinions from all users of a system [2]. This pooling is done without weighing the reliability (or reputation) of the users. The weight for any given individual upon which the combined rating depends is the subject of collaborative sanctioning. This paper describes a system: the Restaurant Sanctioning Service (RSS) which provides weighting for ratings based on the reputation of the raters. We call such a system a collaborative sanctioning system.