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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.
Intelligent User Interfaces
- 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.
Pragmatics of Natural Language
- 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
- 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.