1. Design of a Learning Classifier System for … … Distributed Max-Flow Algorithm Fault Detection

2. Overview Why only Design? XCS Framework S-classifiers Training the LCS The Reward Mechanism Rule Discovery Conclusion/Questions

3. Why only Design? Complexity and volatility of the design phase The design took a while to become stable Injected fault runs are not yet available Mike is working on this – I didn’t want to rush him Reluctance to implement half-baked ideas I like to feel confident before investing lots of time Other obligations and personal issues Thesis – it’s almost done, I hope Mid-life crisis – let’s not go into this, ok?

4. XCS Framework Michigan Model: population = rule set Individual = Rule Evaluation Cycle Update this, update that Rule Discovery Cycle Recombine and/or mutate (condition:action) pairs

5. S-classifiers Use s-expressions in the rules Parse tree to represent a condition which detects a fault with AND and NOT operators. Action = the diagnosis TRUE at first. Later, try to classify various types of faults. Default rule diagnosing FALSE (no fault detected) if no other rule matches the state.

6. Training the LCS Each run of the Max-Flow algorithm produces a history file collected by each processor. Input to the LCS for Rule Evaluation = state of the environment = sliding window of message history from one processor’s perspective So, I am trying to see if one processor can learn to detect faults based on its perspective

7. The Reward Mechanism Current plan: Run an entire history file (about 1500 events) through the LCS. For each file, monitor whether the LCS ever detects a fault. If so, the Diagnosis is TRUE. Allocate a reward at the end of the file based on whether the Diagnosis was accurate. Repeat for many history files, and stop training when a certain level of accuracy is obtained.

8. Rule Discovery Since all the rules have actions which are positive (TRUE) diagnoses, just alter the conditions by normal parse tree operations. There is some room for experimentation in this phase of the LCS, but I have some ideas Use more recombination than mutation. Initialize with simple depth 1 trees. Mutate by replacing subtrees with terminals.

9. Conclusion/Questions LCS is a powerful but complicated concept. Probably requires more expert knowledge than ordinary EA, also more prone to failure. Most significant contribution is probably what I did not do rather than what I did. Did not try to process the actual partial-order of causally-related events – this is not my expertise. Wanted to work with one history file at a time. Did not implement something that had little chance of being successful.