Many problems of planning and control require making decisions based on information that is imperfect, incomplete or unreliable. The theory of Markov decision processes provides a semantic foundation for problems involving planning under uncertainty. One focus of our research is the development of techniques for solving these computationally-difficult problems more efficiently by exploiting problem structure, abstraction, and intelligent search. The objective of the research in language understanding is to explore the automation of the extraction of information from documents written in English. Tools and techniques developed in this work were adapted for use in the AIMS project, which involved the automatic generation of indexes for journal articles.
Current research involves the automatic classification of Web documents.
Color compression using a Kohonen map. Automated identification of roads in satellite imagery using neural nets and genetic algorithms. Use of artificial neural networks, alone and with Markov models, in tagging the part of speech of words in a large, technical corpus. Genetic Algorithms Task scheduling, using genetic algorithms and hybrid systems. Automated identification of roads in satellite imagery using genetic algorithms.