Retrieval systems have greatly improved over the last half century, estimating relevance to a latent user need in a wide variety of areas. One common task in e-commerce and science that has not enjoyed such advancements is searching through a catalog of items. Finding a desirable item in such a catalog requires that the user specify desirable item properties, specifically desirable attribute values. Existing item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple numeric attributes. Such systems avoid inferring query intent, instead requiring the user to precisely specify what matches the query. A contrasting approach would be to estimate how well items match the user's latent desires and return items ranked by this estimation. Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes. In two user studies (choosing airline tickets and meal plans) using Amazon Mechanical Turk, we evaluate our novel approach against the de facto standard of Boolean retrieval and several models proposed in the literature. We use a novel competitive game to motivate test subjects and compare methods based on the results of the subjects' initial query and their success in the game. In our experiments, our new method significantly outperformed the others, whereas the Boolean approaches had the worst performance.

Demand and capacity imbalances in the US national airspace are resolved using traffic management initiatives designed, in current operations, with little collaboration with the airspace users. NASA and its partners have developed a new collaborative concept of operations that requires the users and airspace service provider to work together to choose initiatives that better satisfy the business needs of the users while also ensuring safety to the same standard as today. In this paper, we describe an approach to implementing this concept through a software negotiation framework underpinned by technology developed in the artificial intelligence community. We describe our exploration of peer-to-peer negotiation and how the number of conversation threads and the time sensitivity of offer acceptance led us to a centralized approach. The centralized approach uses hill climbing to evaluate airport slot allocations from a user perspective and a linear programming solver to seek solutions compatible across the user community. Our experiments with full sized problems identify the potential operational benefits as well as limitations, and where future research needs to be focused.

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.

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. KEYWO...