Over the weekend, I began looking into the storytelling aspects of Skald. I consider this to be the most ambitious aspect of the project, or at least the most risky. What we want to do involves Natural Language Processing (NLP) used in a way, that as far as I can tell from my limited research, hasn’t been done before. And I have no idea how any of it works.

Time to start learning.

Before that, though, I needed to decide where I would be doing the processing. Obviously, if we wanted to do it all on the player’s machine, we’d need some way to interface with Unreal Engine (since that’s what we’re using), which means C/C++, or something that can pretend to be C/C++. However, we had already decided that we would need a server to support sharing stories between players, so could we also use that server to evaluate them? This would open up the possibility space of languages (and therefore, libraries) that we could use.

It seems like the common languages used by the machine learning (or is that Machine Learning?) community are Java and Python. I’m reluctant to touch Java with a barge pole, but Python is something I’m fairly comfortable with.

On the other hand, I have been led to believe that NLP is a fairly computationally intensive process, and combining processing time with network latency (and the requirement for an always on internet connection) would mean the game flow would need to hide that delay from the player, and given we want to give “instant” feedback on their writing, that seems like a hard problem to solve. There’s also the matter of the cost of running such a server when we could just be using our players’ computers to do the heavy lifting for free.

In the end, I let my inexperience guide me. Given that Python seems to be one of the de facto language for this kind of thing (and not C++), and so has a lot of support, I decided to prototype a Python server. That way I could work out how to even do the thing using appropriate libraries, tutorials, etc., and then port it over to something client-side if necessary in the future when I understand it all a bit better.

So after picking Gensim as the library to work with, based on some keywords that I’d come across already appeared in its docs (information retrieval, Latent Semantic Analysis, Latent Dirichlet Allocation - did I mention I don’t know what I’m doing?), I built a quick Flask app and set to work learning about NLP.

It turns out that it’s sorcery.

After spending a few hours following Gensim’s tutorials, I had less than 100 (verbose) lines of code that would tell me how similar a line of text is to a bunch of other lines of text (documents). Using a thing called Latent Semantic Analysis, we can do this by looking at the semantic similarity between the two. In other words, we can look at the “meaning” of the documents (or some statistical approximation of it - I have yet to look behind the curtain) to work out if the two are similar. And with the example data, this even found that the most similar document was one that had no common words with the test document. Sorcery!

I quickly modified this to find one of the most similar documents in the data set (randomly selected from those above a certain threshold) and return that, and so, we have a simple server we can use to simulate the flow of an exchange of related stories, which is half of what we want. What we really want to do is the reverse of this; we want to compare a story from our collection (that we had previously shown to the player) with one they wrote in response to see how similar the two are, and whether the player’s writing would feel like a relevant response or not. But this is a step in the right direction, and might even be useful itself if we decide to make the “conversation” longer than two steps.

I still need to put some real data into the thing and see how well it works. I’m probably going to borrow a bunch of books from Project Gutenberg to use as the data set for now, run some of my own prose through it, and see how that goes. Then I think I should probably get the game communicating with the server and hook up some UI to interact with the thing to see how if feels. But I might keep poking around with the NLP instead. We’ll see. Sorcery…