Yet another generalized algorithmic engine has been added to the XIAS library. Multi-state analysis (abbreviated MSE for "multi-state engine") is way of describing the intrinsic "value" of certain state combinations based on the partial analysis of relationships between individual states. The engine is, more or less, a creative implementation of an inference algorithm. But here's the best part - the engine takes categorical states as an input and transforms them into an arbitrary quantitative measurement.

It's easier to explain how the algorithm works in practice. Suppose the user wishes for the machine to know that eating ice-cream on a warm school day is "good." In particular, it's better than eating ice-cream on a cold school day. Still, a non-school day is better than either of these situations, regardless of temperature and presence of ice-cream. The following statements might be made to the engine:

```mse_SetStateValue("icecream,warm,schoolday",10) mse_SetStateValue("icecream,cold,schoolday",5) mse_SetStateValue("icecream,warm,!schoolday",50) mse_SetStateValue("no_icecream,cold,!schoolday",45)```

From these statements, we might ask the engine to infer the intrinsic "value" of ice-cream, the weather, or a school day vs. a non-school day. More interestingly, we might ask the engine to infer the value of the situation in which we have no ice-cream, but it is a warm non-school day ("no_icecream,warm,!schoolday). The second inference would be much more interesting, because the engine would base its response on not just the sum of predicted individual state values, but also the predicted values of the partial relationships between the states (no_icecream to warm, warm to !schoolday, and no_icecream to !schoolday). Notice that we can use any syntax for the categorical inputs - the engine is not concerned with symbols such as ! and _, it treats each state uniquely. The names of states are for user convenience only (and a great convenience it is to have this flexibility!)

For this particular situation, the ability to analyze first- and second-order relationships between states doesn't contribute much to the quality of the overall analysis. However, thinking of this engine as applicable to an analysis of music, it is easy to see how useful the ability to analyze n-th order relationships would be. What is the contribution to tension of a seventh chord being played on the third beat of the measure? How pleasing is it to have [y] follow [x] in a melody given that the root is [z]? These kinds of questions are difficult to answer without highly-specialized analysis engines. But the XIAS MSE can do just that - place quantitative values on multiple-state situations based on partial relationships between the states inferred from given situational values.

I am already in the process of evaluating the abilities of the multi-state analysis engine in predicting the tension of chords based on individual note offsets. With any luck, MSE will prove to be a valuable addition to XIAS.