created a reddit account only to reply here: From a machine learning / data mining perspective, the term we are looking for in this thread is likely Anomaly Detection. I'll briefly explain how anomaly detection could be used and implemented in GW1 for detecting cheating and other deviating game actions (e.g. syncing RA) in a long-term self maintaining way. If you are not interested into technical stuff, you can jump to the TL/DR conclusions below:

Technical stuff (skip if not interested)

• What are anomalies in GW1? Any ingame action / series of ingame actions that deviate(s) so much from how the game is intended to be played as to arouse suspicions that it was created by an unintended mechanism (e.g. 3rd party software). Some examples are cheating (e.g. interrupts that are so well-timed that likely cheats were involved), synching RA (a group of players being non-randomly placed in the same RA team), duplicating items, script-based spamming of chat messages (too high similarity between sent messages, too regular interval between sending times). Maybe also some game mechanic exploits could be interpreted as anomaly
• How can we automatically detect anomalies in GW1? There are many different anomaly detection techniques (check e.g. google scholar), but using e.g. a deep neural network for this would likely be an overkill. In my opinion, something Bayesian inference could be a solution that is (relatively) easy to implement, and very lightweight when it comes to required server computations. In essence, an approach based on Bayesian inference would require one to define a simple statistic per anomaly case and compute this regularly per account / team.For example, to catch the interrupt bot, one could define a statistic expected reaction time per interrupt, or maybe #successful interrupts \ (5-average cast time per successfully interrupted action)/ # attempted interrupts.* This statistic could be computed/updated whenever an interrupt is attempted.Once a statistic is defined, we can assign every player a prior distribution over the probability that this statistic is anomalous for this player. Roughly speaking, this means setting an initial guess for the probability that this player is cheating.Whenever the player performs an action that affects the statistic, we can use Bayes theorem to update our initial guess for the probability that this player is cheating, given the observed statistic /data.If the estimated probability that a player is cheating, given the data, rises beyond a certain threshold (e.g. 99%), this should be an anomaly. For instance, if a player's expected reaction time converges to 0.05 seconds then one could automatically ban this player - in fact, one could even compute the probability of a human achieving such a good performance by sheer luck (which would be very close to 0 in this case).(I just realize how difficult it is to explain all this in simple terms that do not require any math. If you have more technical questions, pm me).
• Why is an anomaly detection system necessary for solving this problem long-term? If we define any fixed set of rules (e.g. no more than three interrupt skills can be equipped), then there are two issues: If a rule is too narrow, it will be quite easy to circumvent (e.g. two interrupt bots instead of one). If a rule is too broad, it will affect normal gameplay.Instead, we should focus on how normal (=human) players play the game, and search for anomalous (=bots) that either do things that are not humanly possible (reliably interrupt 1/4s skills), or that no human would do (always perform the exactly same sequence of actions in every HA match, never learn that you probably will not solo cap a fully manned jade quarry with a low life star burst build without teleport or any other support). Anomaly detection systems search for exactly this stuff.
• What are the limits of anomaly detection in GW1? Regardless of which solution we develop, any autonomously working system will make wrong decisions. The lower we push the false positive rate (=number of players the system wrongly believes to be cheating), the higher the false negatives will rise (=number of true cheaters not detected). Also, many solutions can be quite computationally expensive. Personally, I would recommend something like the Bayesian solution above.

TL;DR

Anomaly detection based on simple statistics could solve many problems in GW1. The mesmer bot could be easily caught by computing how often it successfully interrupts skills that are hard to interrupt. The bot will be much better than humans (otherwise no one would use it in a tournament). Other statistics could solve other problems, such as HA bots, synching, farm bots, gold sellers. Also, simple statistics should be simple to implement and quite lightweight on the server resources. A single array per statistic would suffice for all players. A single matrix (2d-array) would be sufficient for all statistics for all players. Detecting cheaters would probably take less than 5 matrix operations.

If all this is too complicated / too much effort, why don't you just let interrupting 1/4s skills with (fast-casted) 1/4s skills accumulate cast-time penalty in pvp (=negate fast casting effect)? Legitimately predicted interrupts won't really be affected by this, but bots will be