||Believable autonomous agents that have the qualities people see in
traditional animated characters. Rich personality, the illusion of
life, emotion, and thought.|
||Machine translation, planning and learning.|
|Jill Fain ||Natural
language and integrated architectures, such as SOAR.|
||Architectures for agents that learn from
experience, including robot and web-based agents.
Especially interested in the role of machine learning
in these architectures.|
||Getting computers to listen to children read aloud, and help them.
Project LISTEN = "Language Instruction (or Literacy Innovation) that Speech
||Self-reliant autonomous robots that can plan, monitor their plans, and
react to contingencies. Especially interested in robot architectures that
integrate reaction and deliberation, probabilistic planning and navigation,
and selective perception. Interested in integrating machine learning and
||Scientific discovery and computers, e.g., programs to
carry out scientific reasoning at its highest levels.
Practical deployment of tools as scientist's assistants.
Implications for future organization of science.|
||Planning and learning by combining inductive and deductive
techniques, including analogical/case-based reasoning.
Minds for robots: Experience-based agents that plan, execute, and learn.
Collaborative and adversarial planning and learning: RoboSoccer and Bolo.
Machine learning for signal understanding through evolutionary computation.
Rationale capture and reuse in mixed-initiative planning.|