Our journal paper “Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections” was accepted to the D-Lib Magazine. This is joint work with Dr. Liz Lorang and students M. Datla and S. Kulwicki.

We have a technical paper accepted to ICER’2015 @ Omaha, NE! “Exploring Changes in Computer Science Students’ Implicit Theories of Intelligence across the Semester”. This is joint work with Dr. Duane Shell, and his students Flanigan and Peteranetz.

Adam Eck will be teaching a new course Survey Informatics @ UNL in Fall 2015!

Congratulations to Adam Eck. He has been awarded the Outstanding Ph.D. Student Award by CSE @ UNL, 2015!

The IAMAS group helped host Dr. Ed Durfee of the University of Michigan here on April 23rd. Thank you, Dr. Durfee, for your colloquium talk on multiagent organization and your lecture on multiagent scheduling! Thank you too for your valuable insights and productive discussions!

Congratulations to Adam Eck for his successful Ph.D. defense on April 23rd!

We also have a new journal paper to appear in the Autonomous Agents and Multiagent Systems journal: Eck, A., Soh, L.-K., Devlin, S., & Kudenko, D. 2015. Potential-Based Reward Shaping for Finite Horizon Online POMDP Planning, Autonomous Agents and Multiagent Systems. The online version can be found at: link

We have a new full paper accepted at AAMAS’2015! Eck, A. & Soh, L.-K. 2015. To Ask, Sense, or Share: Ad Hoc Information Gathering. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’15), Bordini, Elkind, Weiss, Yolum (eds.), Istanbul, Turkey, May 4-8, 2015. We will present the paper soon at AAMAS.

A paper based on LD Miller’s Ph.D. dissertation work–”Cluster-Based Boosting”–has just been accepted to the IEEE Transactions on Knowledge and Data Engineering. Congratulations!

Congratulations to LD Miller, who successfully defended his Ph.D. dissertation on November 17, 2014! His dissertation title is “Cluster-Based Boundary of Use for Selective Improvement to Supervised Learning”.