Scott Buffett

A Bayesian classifier for learning opponents' preferences in multi-object automated negotiation

We present a classification method for learning an opponents preferences during a bilateral multi-issue negotiation. Similar candidate preference relations over the set of offers are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponents true preference relation lies in that class. Evidence used for classification decision-making is obtained by observing the opponents sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponents true preferences.

NEGOTIATING EXCHANGES OF P3P-LABELED INFORMATION FOR COMPENSATION

We consider private information a commodity, of value to both the information holder and the information seeker. Hence, a customer can be enticed to trade his/her private information with a business in exchange for compensation. In this article, we propose to apply utility theory to allow each participant to express the value they place on each private datum and, separately, on combinations of data. The PrivacyPact protocol transmits messages that comprise possible exchanges. Each participant is prevented from making offers that necessarily have lower utility for the other partner than previous ones. The protocol is complete in that if an exchange exists that is acceptable to both, it will be found as long as neither partner exits the negotiation early. While the space of possible offers grows exponentially on the number of negotiable items, experimentation with simple strategies indicates that negotiations can converge relatively quickly.

Reasoning with Conditional Preferences Across Attributes

Before an autonomous agent can perform automated negotiation on behalf of a user in an electronic commerce transaction, the users preferences over the set of outcomes must be learned as accurately as possible. This paper presents a structure, a Conditional Outcome Preference Network (COP-network), for modeling preferences directly elicited from a user. The COP-network then expands to indicate all preferences that can be inferred as a result. The network can be easily checked for consistency and redundancy, and can be used to determine quickly whether one outcome is preferred over another. An important feature of the COP-network is that conditional preferences, where a users preference over outcomes depends on whether particular attribute values are included, can be modeled and inferred as well. If the agent also knows the users utilities for some of the possible outcomes, then these can be considered in the COP-network as well. Three techniques for estimating utilities based on the specified preferences and utilities are described. One such technique, which works by first estimating utilities for long chains of outcomes for which preferences are known, is shown to be the most effective.

A Decision Procedure for Bundle Purchasing with Incomplete Information on Future Prices

This paper introduces the on-line bundle-purchasing problem (OBPP) as a new computational challenge induced by e-commerce technology. The task of the OBPP is to decide which of many satisfactory combinations (bundles) of items should be purchased, from whom, and when, to maximize the buyers overall satisfaction. Satisfaction, formalized as multiattribute utility, includes attitudes toward quality, reputation, and risk. The Prequote-Quote-Rescind (PQR) protocol communicates probabilistic and temporal information on the future prices and availabilities of items. A comparison set, defined as a set of bundles in which all items are available for a fixed interval and their prices are known, determines future intervals when purchase decisions will be fully informed. A decision procedure is provided that makes effective use of comparison sets and improves a buyers expected utility compared with a naive decision procedure.

Discovering Structured Event Logs from Unstructured Audit Trails for Workflow Mining

Workflow mining aims to find graph-based process models based on activities, emails, and various event logs recorded in computer systems. Current workflow mining techniques mainly deal with well-structured and -symbolized event logs. In most real applications where workflow management software tools are not installed, these structured and symbolized logs are not available. Instead, the artifacts of daily computer operations may be readily available. In this paper, we propose a method to map these artifacts and content-based logs to structured logs so as to bridge the gap between the unstructured logs of real life situations and the status quo of workflow mining techniques. Our method consists of two tasks: discovering workflow instances and activity types. We use a clustering method to tackle the first task and a classification method to tackle the second. We propose a method to combine these two tasks to improve the performance of two as a whole. Experimental results on simulated data show the effectiveness of our method.

Bayesian Classification of Events for Task Labeling Using Workflow Models

We investigate a method designed to improve accuracy of workflow mining in the case that the identification of task labels for log events are uncertain. Here we consider how the accuracy of an independent task identifier, such as a classification or clustering engine, can be improved by examining workflow. After briefly introducing the notion of iterative workflow mining, where the mined workflow is used to help improve the true task labelings which, when re-mined, will produce a more accurate workflow model, we demonstrate a Bayesian updating approach to determining posterior probabilities for each label for a given event, by considering the probabilities from the previous step as well as information as to the beliefs of the labels that can be gained by examining the workflow model. Experiments show that labeling accuracy can be increased significantly, resulting in more accurate workflow models.

Private data management in collaborative environments

Organizations are under increasing pressures to manage all of the personal data concerning their customers and employees in a responsible manner. With the advancement of information and communication technologies, improved collaboration, and the pressures of marketing, it is very difficult to locate personal data is, let alone manage its use. in this paper, we outline the challenges of managing personally identifiable information in a collaborative environment, and describe a software prototype we call SNAP (Social Networking Applied to Privacy). SNAP uses automated workflow discovery and analysis, in combination with various text mining techniques, to support automated enterprise management of personally identifiable information.

Detecting opponent concessions in multi-issue automated negotiation

An agent engaged in multi-issue automated negotiation can benefit greatly from learning about its opponents preferences. Knowledge of the opponents preferences can help the agent not only to find mutually acceptable agreements more quickly, but also to negotiate deals that are better for the agent in question. In this paper, we describe a new technique for learning about an opponents preferences by observing its history of offers in a negotiation. Patterns in the similarity between the opponents offers and our own agents offers are used to determine the likelihood that the opponent is making a concession at each stage in the negotiation. These probabilities of concession are then used to determine the opponents most likely preference relation over all offers. Experimental results show that our technique significantly outperforms a previous method that assumes that a negotiation agent will always make concessions during the course of a negotiation.

Private Data Discovery for Privacy Compliance in Collaborative Environments

With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). This problem becomes even more evident in collaborative computing environments. PII may be hidden anywhere within the file system of a computer. As well, in the course of different activities, via collaboration or not, personally identifiable information may migrate from computer to computer. This makes meeting the organizational privacy requirements all the more complex. Our particular interest is to develop technology that would automatically discover workflow across organizational collaborators that would include private data. Since in this context, it is important to understand where and when the private data is discovered, in this paper, we focus on PII discovery, i.e. automatically identifying private data existant in semi-structured and unstructured (free text) documents. The first part of the process involves identifying PII via named entity recognition. The second part determines relationships between those entities based upon a supervised machine learning method. We present test results of our methods using publicly-available data generated from different collaborative activities to provide an assessment of scalability in cooperative computing environment.

The International Journal of Information Security Special Issue on privacy, security and trust technologies and E-business services: Guest Editors’ Introduction

With recent advances in Web and e-service technologies and associated infrastructures, there are increasing demands for ubiquitous access to e-business services for supporting business processes. With the advent of e-business and supply chain management concepts, increasing demands for interoperable applications exist, which allow for the real-time exchange of data across enterprise borders, across different applications and across different IT-platforms. However, these demands cannot be realized without suitable privacy, security, and trust technologies to ensure that business data is appropriately protected and business partners can inter-work with confidence. In principle, an e-business service refers to an autonomous unit of functionality that provides either some e-business application or information to accomplish enterprise purposes at anytime and anywhere through wired and wireless network infrastructure and Web technologies. The goal of this special issue is to crystallize the emerging privacy security and trust technologies and trends into positive efforts to focus on the most promising solutions in e-business services computing. The papers provide clear proof that privacy security and trust technologies are playing