The State of Game Development in Rust
Game Development is one of the fields in which Rust can gain a lot of traction. As a modern compiled language with performances comparable to C++, Rust can finally free us from the tyranny of C++ bloated feature set, hard-to-link dependencies, and header/implementation file double-madness (I am obviously exaggerating, btw).
However, if this freedom arrive, it will be a very slow process. To make it slower, the feature of memory safety in videogames is not a huge priority compared to the ability to quickly prototype. The borrow-checker and the strict compiler are an obstacle in this regard. On the other hand, memory safety also means easier multi-threading. And this is sweet!
Fortunately, the annoyances of borrow-checker will get less in the way while people becomes more confident with the language, and while tooling gets better and better. I am confident we may see Rust carve out its space in this domain.
But this is the future. What about now?
Choosing between Behavior Tree and GOAP (Planning)
I would like to expand the answer I gave on /r/gamedesign some days ago. The main point of the question was: how can I decide if it “better” to implement the decision-making layer of our game AI with Behavior Trees (BTs) or with more advanced plan-based techniques such as Goal Oriented Action Planning (GOAP) or Simple Hierarchical Ordered Planner (SHOP). First consideration: this is not a technical problem The first thing to know is that writing game AI is not a race for the best technology.
GameDesign Math: RPG Level-based Progression
Level-based progression is an extremely popular way of providing the player with the feeling of “getting stronger.” The system is born with Role-Playing Games (RPG), but it is nowadays embedded in practically every game; some more, some less. Even if it is entirely possible to provide progression feeling without levels and experience points, level-based progression is natural, direct, and linear, and it fits well in many (too many?) game genres.
Procedural Calendar Generation & Lunar Phases
Here we are again! This is Part 3 of a small series on how to randomly generate a physically accurate calendar starting from planet’s orbital parameters. You can find the general motivation here, part one here and part two here. Said that, here we go with the next part: lunar phases. Why lunar phases Lunar phases are incredibly important in a calendar. So important, that many of the early humanity calendars are, in fact, lunar calendars or lunisolar calendars.
Seasons Generation from Orbital Parameters
Welcome back to part 3 of the Procedural Calendar Generation series. In the first part we looked on how to compute celestial body position in a simple two-body system. The second part, instead is crash course on ellipse geometry. In this part, instead, we will tackle a fascinating consequence of the cosmic dance of our planet around its sun: seasons. Seasons are a strange beast because their behavior depends on a huge amount of factors.
Cuphead is not "hard"
During the last few weeks, I’ve seen over and over people saying that Cuphead is hard. That it is brutal. That is the “dark souls of the side shooter”. For this reason, before this trend goes too far, it is time to make things clear: Cuphead is not hard. Can a game that can easily beaten in a couple of hours be hard? No. It is challenging”, yes. It requires multiple tries, for sure.
Playerunknown's Battlegrounds did everything wrong. And doing so, it won.
This small article is born from a discussion I had with a friend of mine this week. He was writing a review on Playersunknown’s Battlegrounds (from now on, PUBG) and he ended up talking about the evolution of the genre and its triumph over every other competitor. The article was good but it did not enter in detail about, what I think, it is a greatly important and interesting question: Why PUBG? Why not any of the other dozens of battle royal games we were plagued in the last years?
PUBG is clearly a winner in this competition. It sold more than 8 million copies on Steam only, and I can see the trend going with the future release on consoles. The problem, in my opinion, is that, on paper, there is nothing in PUBG implementation that seems “right”. Nevertheless, it won.
Machiavelli once said that success is 50% luck. That’s definitely true for PUBG. But the other 50% must be researched in the PUBG qualities. Analyzing them, despite the massive “errors”, it is very important for any game designer.
Against Addiction and Gambling-like Mechanic in Free to Play Games
I want to take the cue from a last week massive Reddit thread on micro-transaction in Free2Play (F2P) games to give my opinion of the topic. I think it is important. We need to increase awareness that predatory practices in F2P games are incredibly close to gambling and share with it the same self-destructive and harmful addictive behavior. This is wrong in so many way: it is dangerous for the victims, it is dangerous for the game itself, and it is dangerous for the entire F2P model.
Improve Inventory-Aware Pathfinding with Map Preprocessing
This article has been originally published on Gamasutra. In the last article we introduced a basic approach for Inventory-Aware Pathfinding (IAP), a pathfinding algorithm capable of interacting with obstacles and not just avoiding them. If you have not read it, I encourage you to go back and read it to understand the basic challenges and the main ideas behind the proposed solution. For instance, we can have a pathfinding algorithm that can solve small plans and “puzzles” involving reasoning like “before passing this door, I need to get that key”.
Randomness in PCG is about the result, not the parameters
I feel the urge of stating the obvious: randomness in Procedural Generation refers to the perceived randomness of the outcome; not the randomness of the input parameters. In some sense this is “obvious” but, at the same time, is one of the most common mistake I see when developers tackle procedural content generation in their games. It is an understandable mistake, though. There are two assumptions we subconsciously make when we approach randomness: 1) we think that uniformly random parameters produce uniformly random outputs (that’s blatantly false), and 2) we think that uniformly random outputs yield to uniformly random perception in the human (even more false).