Should multi-unit auctions be used to sell multiple identical and indivisible units? In this paper we show that with sufficiently many bidders, the multi-unit format is more efficient than the bundle format in a broad range of situations. However, the revenueperformance depends on the fraction of demand that is met asymptotically. We also examine the absolute performance of the multi-unit aucti...

In this paper, we propose a text summarization system for a single document and multiple documents. The system for a single one extracts sentences from a document and itemizes them to generate a summary. We applied this mechanism for Task A (single document summarization). We also utilized this mechanism for multi-document summarization (Task B) except for itemization mechanism. The system for ...

This paper describes an intelligent, mixed-initiative scheduler which supports users in the organization and revision of their calendars, helping them to allocate personal and shared activities involving other collaborators. Our scheduler exploits well-known temporal reasoning techniques to suggest complete schedules, as well as to guide the user in the exploration of the possible changes to th...

To solve the problem of group task allocation with time constraints in open and dynamic network environments, this paper proposes a decentralized combinatorial auction-based approach for group task allocation. In the proposed approach, both resource providers and consumers are modeled as intelligent agents. The proposed approach is decentralized, so all the agents are limited to communicating w...

Motivating employees to contribute knowledge has been a daunting challenge in knowledge management. This study examines how job design influences employees' knowledge contribution to electronic repositories. Based on Warr's Vitamin Model, we posit that the job autonomy, skill variety, task feedback, task identity, and task significance aspects of job design influence employees' knowledge contri...

We study the problem of distributed multitask learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-e...

Stigmergy, usually used on simple problems, can be applied to more complex ones by encoding them in the agents’ environment. We show how stigmergic agents can plan over a hierarchical task network, a resource-oriented dialect of the TÆMS language. Our results reveal an important distinction among HTN’s.

Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situationa...

The modern computer vision systems usually scan the image over positions and scales to detect a predefined object, whereas the human vision system performs this task in a more intuitive and efficient manner by selecting only a few regions to fixate on. A comprehensive understanding of human search will benefit computer vision systems in search modeling. In this paper, we investigate the contrib...