Shawn R. Wolfe

A bookmarking service for organizing and sharing URLs

Abstract Web browser bookmarking facilities predominate as the method of choice for managing URLs. In this paper, we describe some deficiencies of current bookmarking schemes, and examine an alternative to current approaches. We present WebTaggerTM, an implemented prototype of a personal bookmarking service that provides both individuals and groups with a customizable means of organizing and accessing Web-based information resources. In addition, the service enables users to supply feedback on the utility of these resources relative to their information needs, and provides dynamically-updated ranking of resources based on incremental user feedback. Individuals may access the service from anywhere on the Internet, and require no special software. This service greatly simplifies the process of sharing URLs within groups, in comparison with manual methods involving email. The underlying bookmark organization scheme is more natural and flexible than current hierarchical schemes supported by the major Web browsers, and enables rapid access to stored bookmarks.

A distributed multi-agent system for collaborative information management and sharing

In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search-to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web.

Semanticorganizer: a customizable semantic repository for distributed NASA project teams

Semantic Organizer is a collaborative knowledge management system designed to support distributed NASA projects, including multidisciplinary teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. Semantic Organizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in varying contexts ranging from the investigation of Space Shuttle Columbias accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes Semantic Organizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with disparate users.

Comparing Route Selection Strategies in Collaborative Traffic Flow Management

In the United States, air traffic flow management (TFM) is concerned with directing traffic through the national airspace system (NAS), such that unsafe conditions are avoided. Unlike airport tower and en-route controllers, who focus on aircraft operating in their immediate airspace and deal directly with pilots, TFM focuses on balancing overall traffic demand with airport and airspace capacity. NASAs Next Generation Air Transportation System (NGATS) project seeks to address these issues in several ways; our aim is to explore and test their new concept of operations for collaborative traffic flow management (CTFM) through a multi-agent simulation.

User-centric multi-criteria information retrieval

Information retrieval models usually represent content only, and not other considerations, such as authority, cost, and recency. How could multiple criteria be utilized in information retrieval, and how would it effect the results? In our experiments, using multiple user-centric criteria always produced better results than a single criteria.

Collaborative information agents on the World Wide Web

In this paper, we present DIAMS, a system of distributed, collaborative information agents which help users access, collect, organize, and exchange information on the World Wide Web. Personal agents provide their owners dynamic displays of well organized information collections, as well as friendly information management utilities. Personal agents exchange information with one another. They also work with other types of information agents such as matchmakers and knowledge experts to facilitate collaboration and communication.

OperA and Brahms: A Symphony?

The organizational view on work systems focuses on the desired outcomes of work (i.e., the work process) while the emergent view focuses on how the work actually gets done (i.e., the work practice). Often a gap exists between these two, because workers pursue individual objectives in addition to the organizational objectives. Agent-based modeling and simulation can be used to improve work systems and thereby organizational performance. Current modeling and simulation frameworks only represent either one of the two views. In order to model both views, we propose an integration of two modeling and simulation frameworks, OperA and Brahms. Using the integrated model, we are able to run simulations that show to what degree work practice differs from work processes.

Interaction and personalization of criteria in recommender systems

A users informational need and preferences can be modeled by criteria, which in turn can be used to prioritize candidate results and produce a ranked list We examine the use of such a criteria-based user model separately in two representative recommendation tasks: news article recommendations and product recommendations We ask the following: are there nonlinear interactions among the criteria; and should the models be personalized? We assume that that user ratings on each criterion are available, and use machine learning to infer a user model that combines these multiple ratings into a single overall rating We found that the ratings of different criteria have a nonlinear interaction in some cases, for example, article novelty and subject relevance often interact We also found that these interactions vary from user to user.

Agent-Based Modeling and Simulation of Collaborative Air Traffic Flow Management using Brahms

The air traffic demand on the US national airspace frequently exceeds its available capacity. In current operations, the Air Traffic Service Provider designs and implements air traffic management initiatives with minimal interaction with the airlines. NASA and its partners have developed a new collaborative air traffic flow management concept of operations that involves the users of the airspace to a greater degree. In this paper, we describe an agent-based simulation of the new concept of operations and our planned experimentation to determine if the new concept of operations will lead to better utilization of the national airspace.

Using machine learning to synthesize search programs

This paper describes how machine learning techniques are used in the MULTI-TAC system to specialize generic algorithm schemas for particular problem classes. MULTI-TAC is a program synthesis system that generates Lisp code to solve combinatorial integer constraint satisfaction problems. The use of algorithm schemas enables machine learning techniques to be applied in a very focused manner. These learning techniques enable the system to be sensitive to the distribution of instances that the system is expected to encounter. We describe two applications of machine learning in MULTI-TAC. The system learns domain specific heuristics, and then learns the most effective combination of heuristics on the training instances. We also describe empirical results that reinforce the viability of our approach.<<ETX>>