Some rules can be derived from instrumenting humans as they perform the maneuvers, and generalizing from their behavior? We used to instrument motorcycle riders at Harley Davidson and create fuzzy-logic models of expert riders as they performed certain acts on a track (dodging road hazard; emergency stop; hairpin turn). Our goal was also a fuzzy-logic driver model, which they used to help design new motorcycle suspensions/steering that would feel 'natural' to an expert rider e.g. mesh well with the model they had for an expert.
Wow that's very innovative for HD. Why don't they put some of that innovation effort into their actual drivetrain? I loved my Buell's look and ride, but I mistrusted it's horrid 50-year-old Baker transmission which went on to completely fail at ~6,000 miles, necessitating me to disassemble the entire engine to split the crankcase so I could repair it. After seeing its guts, I no longer wanted it. It ignores a half century of innovation in motorcycle design producing a machine that I vote most likely to unexpectedly leave me on the side of the road.
I'll throw out a guess that part of it is the character of the bike being tied to the drivetrain.
Even outside Buell, which was a bit of a neither-fish-nor-foul anomaly, they've had the odd innovative model here and there--the V-Rod comes to mind. And I've seen some interesting things in their ABS systems and some other components. But I think the most successful models have been very conservative about their drivetrain as it's part of their signature sound/feel.
My hope is that Polaris' recent critical success with the Indian Scout (which I bought over HD--far better bang for buck than a Sportster) gives the segment a kick in the ass.
The continued anomaly of the Buell is partly the V-Rod's fault. The V-Rod engine was originally supposed to be for the Buell line to address the issue until the mothership got interested in it. They added too much weight and too high a deck height for Buell chassis, so the project was wrestled away from Buell to make a bike that, in the end, HD couldn't really sell anyway.
Interesting--I had no idea that the V-Rod engine was originally destined for Buell. That makes lots of sense.
Buell had some great innovative designs too, but you could tell that was all Erik and not the motor company. I wasn't surprised when the split happened.
What many don't know about the shuttering of Buell is that it happened while HD was in the process of receiving over a billion in TARP money. This information was kept secret for over a year. Harley had something like ~6000 employees at the time. Buell operated on ~100. It seemed like a ludicrous move; like it had to be political.
If you're looking for innovative motorcycling, gotta look at a sportbike. HDs are for people who love cruisers, a genre defined by rat bikes and self-made bikes, but for people with money. (Not knocking on rat bikes, though, those things are awesome)
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.
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.
Quinlans 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.
It's an approach to AI that allows you to generate rules based on probabilistic logic (0.0-1.0) rather than strictly boolean true/false (1 or 0). The applications are used in tons of different systems from medical diagnostics tools to washing machines.
The plus side is that it allows you to make systems that can be tweaked using trial and error to handle cases that would require very complex logic otherwise. The down side is that alot of the time the systems aren't provable the way other types of logic are, and they can be a pain to debug.
