2. Constructed Languages (Lojban) in AI
4. Knowledge Acquisition Through Machine Perception
5. The Affects of Priming. Prior Knowledge, and Concurrent Sensory Input on Natural Language Processing
6. Applying Foundherentist Principles to Knowledge Base Construction
7. Determine how Levels of Abstraction Affect a Knowledge Base
I'm questioning number one. The current research into an ITS is mostly determining what questions to quiz them on (I believe). I'll read up on it some more, but my latest additions to the list seem to be pointing in a different direction. For this reason, number three is also called into question. Research into the other topics would benefit one and three.
My remaining choices, except for number two, deal with knowledge bases and/or knowledge representation in some way. I've made no attempt to hide the fact that I think the existing methods of knowledge representation (that I know about) are far too limited and not very recent. With our expended understanding of neuroscience, epistemology, and computer science, we would have come up with a better method by now. Granted, I need to read up on what's being used, but I keep seeing frames and first order logic being mentioned. While we have much to learn from each, both have faults that have yet to be resolved.