Thursday, December 11, 2003
Case Base Reasoning (CBR) overview
AICom paper
My thinking about CBR is:
1) CBR has some ideas
2) As a whole CBR is too old (~15 years) and produced very limited results.
3) CBR is applicable to certain tasks. For example some Narrow Artificial Intelligence Systems may benefit from CBR.
Question (by Jiri Jelinek): What do you think is the main reason for the "very limited results"?
1) Because problems that we are solving now never happened before -- only similar cases happened.
2) Even if we were able to find solution in our memory -- such memory would have virtually infinite size (which means virtually zero performance).
3) When we try to solve #1 and #2 -- we need to apply interpolation/extrapolation techniques, which in essence is not a CBR anymore.
Interpolation/extrapolation would require calculating weights for and against set of potential solutions.
Such weights can be stored in Cause-Effect relations
My thinking about CBR is:
1) CBR has some ideas
2) As a whole CBR is too old (~15 years) and produced very limited results.
3) CBR is applicable to certain tasks. For example some Narrow Artificial Intelligence Systems may benefit from CBR.
Question (by Jiri Jelinek): What do you think is the main reason for the "very limited results"?
1) Because problems that we are solving now never happened before -- only similar cases happened.
2) Even if we were able to find solution in our memory -- such memory would have virtually infinite size (which means virtually zero performance).
3) When we try to solve #1 and #2 -- we need to apply interpolation/extrapolation techniques, which in essence is not a CBR anymore.
Interpolation/extrapolation would require calculating weights for and against set of potential solutions.
Such weights can be stored in Cause-Effect relations
Labels: Case Base Reasoning, CBR, Problem Solving, Relation, Relations
