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I asked myself the same question, and did read some papers, but could not find a recent comprehensive survey on automatic fuzzy rule generation (I admit I gave

by linschn 10y ago

I asked myself the same question, and did read some papers, but could not find a recent comprehensive survey on automatic fuzzy rule generation (I admit I gave up after ~15 minutes). What I found did not convince me that it would fare better than an off-the-shelf (somewhat) non-interpretable statistical supervised learning algorithm. It can be a nice way of bootstrapping the rule writing process, or to go the other way : to discover and analyze new expert knowledge by looking at the rules. But performance-wise, I would go the machine learning way anytime. Also, Inverse Reinforcement Learning seems to be very promising : one guesses the reward function by observing the expert acting.

boxy310 10y ago
I imagine there's probably significant regulatory constraints into the interpretability of any models generated to run combat weapons platforms, even if just in a simulation. Deviations from a norm during times of war due to needing to chase after additional data or testing hypotheses might be considered a significant demerit to the model. Alternatively, formalizing these rules may be helpful for instructing new pilots or adhering to existing rules-of-engagement.
PeterisP 10y ago
Quinlans https://en.wikipedia.org/wiki/C4.5_algorithm https://en.wikipedia.org/wiki/C4.5_algorithm is somewhat popular for similar tasks, it allows to build decision trees from data that are conceptually similar to such fuzzy rules, and the rules can be human-readable so they can be really powerful after expert review. For example, a very specific condition can either mean that this particular condition is useful, or that simply the training data happened to have those particular instances of a more general condition - and a human expert can usually easily decide which rules need to be extended for proper generalization beyond your training data, but the automated generation helps identify factors that the expert could recognize, but wouldn't think of if doing it themselves from scratch.
- linschn 10y ago
  Thanks for the ref !

by linschn 10y ago

boxy310 10y ago

I imagine there's probably significant regulatory constraints into the interpretability of any models generated to run combat weapons platforms, even if just in a simulation. Deviations from a norm during times of war due to needing to chase after additional data or testing hypotheses might be considered a significant demerit to the model. Alternatively, formalizing these rules may be helpful for instructing new pilots or adhering to existing rules-of-engagement.

PeterisP 10y ago

Quinlans https://en.wikipedia.org/wiki/C4.5_algorithm https://en.wikipedia.org/wiki/C4.5_algorithm is somewhat popular for similar tasks, it allows to build decision trees from data that are conceptually similar to such fuzzy rules, and the rules can be human-readable so they can be really powerful after expert review. For example, a very specific condition can either mean that this particular condition is useful, or that simply the training data happened to have those particular instances of a more general condition - and a human expert can usually easily decide which rules need to be extended for proper generalization beyond your training data, but the automated generation helps identify factors that the expert could recognize, but wouldn't think of if doing it themselves from scratch.

linschn 10y ago
Thanks for the ref !