Research

Decision-theoretic Planning and Learning

Design of algorithms to optimize a sequence of actions in an uncertain environment. The emphasis is on probabilistic and decision-theoretic techniques such as (fully and partially observable) Markov decision processes as well as reinforcement learning. Applications include assistive technology and spoken-dialog systems.

Integrating natural language processing models and user models for the purpose of producing more effective human-computer interfaces. This includes designing interfaces which allow for mixed-initiative interaction. Application areas include interface agents, electronic commerce, recommender systems and personalization systems.

Studying how computational limitations influence strategic behavior in multi-agent systems, as well as developing approaches to overcome computational issues which arise in practical applications of mechanism design and game theory.
Designing systems of collaborative problem solving agents, with an emphasis on issues of communication and coordination, applications of multi-agent systems to the design of effective electronic marketplaces and adjustable autonomy systems and modeling trust and reputation in multi-agent systems.

Classification of citations: Automated classification of citations in scientific articles, including digital libraries.
HealthDoc: Automated generation of individually tailored health-education materials.

Constraint propagators for global constraints: Speeding up constraint programming by designing algorithms for propagating commonly occurring constraints.
Branching strategies:The effects of branching strategy on backtracking search.
Learning variable ordering heuristics: Applying machine learning techniques to devise heuristics for speeding up a backtracking search problem.
Instruction scheduling: Applying constraint programming to a scheduling problem that arises in compilers.

Machine learning is a fast growing topic of both academic research and commercial applications. It addresses the issue of how can computers "learn", that is, how can processes drawing useful conclusions from massive data sets be automated. Machine learning plays a central role in a wide range of important applications emerging from need to process data sets whose sizes and complexities are beyond the ability of humans to handle.