Research

The Big Picture

I'm interested in many things, including artificial intelligence, coginitive science, natural language understanding, programming languages, computer science education, and pretty much any good old-fashioned computer science.

Current Projects

Modular Reinforcement Learning
	Real-world agents (and agents in interesting artificial worlds) must pursue multiple goals in parallel nearly all of the time. Thus, to make real-world partial programming feasible, we must be able to represent the multiple goals of realistic agents and have a learning system that handles them acceptably well in terms of computation time, optimality, and expressiveness. We are developing a theoretically grounded and practical algorithm to encode the multiple goals of an agent in a way that facilitates true modularity, enabling a true discipline of agent software engineering.
FABLE (Friendly ABL Extensions): An Agent Modeling Language and Tools for Non-Programmers
	My Ph.D. focuses on developing a language and tools for agent modeling as part of CABAL's larger vision to create advanced artificial intelligence for interactive computer games and simulations. Such languages are not new; my contribution will be to bring the power of advanced agent modeling to people who are not experts in computer science or artificial intelligence, and to simplify the task for those who are. We call our envisioned language FABLE, for "Friendly ABL Extensions." Read more. Publications Towards Adaptive Programming: Integrating Reinforcement Learning into a Programming Language. Christopher Simpkins, Sooraj Bhat, Charles Isbell, Jr., and Michael Mateas. To appear in OOPSLA '08: ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, Onward! Track, Nashville ,TN USA, October 2008

Past Projects

Mutual Information-Maximizing Input Clustering (MIMIC) for Antenna Design
	Seth Markle, Denis Bueno, and I applied Charles Isbell's MIMIC algorithm to the design of planar array antennas. This work compared MIMIC to a previously published and very successful genetic algorithm approach employed by GTRI's Signature Technology Lab. We found MIMIC to be far superior in terms of execution time due to the computationally expensive evaluation function of real-world antennas. Software and publication to be published soon.
Deriving Scientometric Information by Text Mining Scientific Publications
	This work was in service of the research of Dr. Chiara Franzoni, an economist specializing in scientometrics. I applied text mining methods to a database of publication abstracts and biographical information for members of the American Physical Society. Our initial publication focused on finding patterns of specialization (in topic or research method) over time. Publications Using Content Analysis to Investigate The Research Paths Chosen by Scientists Over Time. Chiara Franzoni, Christopher Simpkins, Li Baoli and Ashwin Ram. 11th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2007)
Using Optimization Algorithms to Design Neural Networks
	This work was part of a larger project led by Dr. Paul Kemper in GTRI's Signature Technology Lab. The project sought effective machine learning algorithms for identifying RF emitters. In an effort to build on previously successful neural network approaches, Denis Bueno and I applied genetic and MIMIC optimization techniques to the design of neural network architectures.
MAT (MAT Analysis Tool): A GUI Analysis Platform for Materials Characterization Experiments
	The MAT software package is a modular GUI platform for storing and alayzing materials experiments. I designed a data management system, a flexible user interface for displaying multiple experiments, and a modular plug-in architecture that enables the straightforward addition of new analysis modules, thereby leveraging the data storage and allowing analysts to easily compare experiment data in multiple ways in a single desktop application.
SACRE BLEU: Software-Assisted Content Review Based on Lanuage (English) Understanding
	The SACRE BLEU project sought to develop software that identified information of interest in arbitrary human-language text, where information of interest is determined by both policy and precedent. The system is intended for application to the problem of content review for classified information where release officers must aprove the sharing of vast quantities of information on often very short deadlines. My role was to investigate user-adaptive methods and help manage the project, in particular the overall architecture and evaluation of technology components.
Casebook: An Adaptive Problem-Based Learning Environment
	Casebook is Dr. Ashwin Ram's web-based platform for applying Problem-Based Learning in the classroom. By providing a suite of collaboration and research tools, and guiding students through the process of PBL, Casebook solves the primary obstacle to adoption of PBL in real-world classroms: the scalability of limited teacher-facilitator resources. Traditional PBL requires a great deal of involvement from the teacher to facilitate the process. Casebook takes care of a great deal of the details so that PBL can be effectively applied in real-world classrooms. I implemented software engineering practices, deployed and maintained the system for classroom research studies, and helped design Casebook 2, which is currently in development.