Neural Networks, Machine Learning (ML), Deep Learning, … – that’s modern AI. These methods have produced spectacular advances in vision, natural language, pattern recognition, and many other areas. Entire academic, corporate, and national efforts have sprung up to join the race to avoid any ‘ML Gap’. In fact, the terms ML and AI have now commonly become synonymous.
A related hardware trend has taken the computing industry by storm: Massively Parallel Processing (MPP). ML, specifically the training of neural networks, greatly benefits from such hardware acceleration. This wave began with the ubiquitous Graphics Processing Unit (GPU), then expanding into devices such as the Associative Processing Unit (APU), Tensor Processing Unit (TPU), Vision Processing Unit (VPU), and even Field Programmable Gate Array (FPGA). Even tiny chips that perform dedicated monitoring and automation tasks (edge computing) are now integrating such MPP technology.
Of course, game designers have been scrambling to enhance and augment their offerings by incorporating such methods. Mainly in the realm of AI players, to better reflect human strategic thinking. Chess, Go, general knowledge, and many other specific machine players have now achieved supremacy over human players. Playing some modern games against one or more AI players is almost a ‘social’ experience.
It wasn’t always thus.
Back in the single-threaded days, those halcyon days of early PC gaming, AI had a much wider horizon. Symbolic themes such as logic, optimization, semantics, rules, expert systems, goals, and graphical knowledge representation dominated. Steps and decisions were the basis of design, and often some sort of ‘theory of mind’ existed, even if very simplistic. Logic and knowledge representation tools sprouted, such as Lisp, Prolog, and several Production Systems. Navigating decision trees, usually via some form of context awareness and backtracking capability was a process that was reassessed at each step. This was great for automating simple, explicit thought processes and playing simple games (tic-tac-toe, checkers, adventure, cards, etc). However, it fell short when confronted with the much harder areas of vision and common sense. Good Old Fashioned AI (GOFAI) went the way of the buggy whip. The lull that persisted from the late 1980s through the early 1990s is an example of an ‘AI Winter’.
But perhaps that total abandonment was a mistake. It’s easy to rush right past available treasures when blinded by shiny new objects.
Modern games are certainly beautiful to look at, with amazing realism and depth, but are they more meaningful? more playable? more fun?? Gameplay is about much more than realistic perception and rendering. This is especially true for Game Based Learning (GBL). In GBL, the firing of neurons in the player’s brain is the goal, much more than the passive presentation of eye candy.
The Human Computer Interface (HCI) has actually progressed little over the decades. Instead of the game living mostly in the player’s mind, as it did in the primitive arcade days, it is now presented almost as a movie, with entire scenes played out for them. Much of the overall experience can be obtained just by watching game demos. The most compelling feature of modern games is their community… of human players!
What if gameplay itself was the focus? Exploiting and augmenting the innate power of human intelligence. HCI enhancements could once again move the game into the player’s mind, where it belongs. Instead of pre-fab, frustrating, stultifying scenarios, the player engages with a snappy, assistive, powerful, and ‘syntonic’ game. Simple rules could be explicitly available or even player-created. The great fun of Minecraft is largely due to the process of learning and mastering such rules, which are presented in a progressive, bootstrapping way. Instead of hiding the underlying mechanisms in order to present slick, canned movies, the player could be aware of how and why things actually work. The best combat games are those that enable the player to construct their own custom devices. Sadly, this is always achieved with a rigid menu system instead of a pencil-and-paper approach. Again, the player’s own creativity is stifled or ignored. Fairly complex worlds, even player-modifiable ones, can be created in Prolog. Explicit rules and facts are both computer and human readable. Transparency and openness are the keys to truly immersive games – pulling back the Oz curtain is a good thing.
Automation is another way to greatly enhance the playing experience. Sometimes the player is forced to repeat a series of steps even though they only wish to make one or two minor changes from their last go through. Wasting time is not fun. Giving the player access to such a series to make minor tweaks would be a great time saver, which would allow the player to focus on strategy, not mundane tactics or plain drudgery. And a tiny sprinkling of assistive AI to avoid stupid mistakes could enhance gameplay. Players don’t mind AIs ‘cheating’ so much as they mind pointless glitches and ‘gotchas’. Most decent chess AIs have had this feature for decades. It’s surprising that many modern games don’t even have a simple ‘undo stack’, instead relying on frequent game saves. The best games also have an ‘advisor’ system that helps the player with decision-making in the current context, with the option to squelch advice and tips as the player gains experience. The focus should be not only on rich content, but also rich context.
There is another area where automation is valuable: testing. By providing for massive machine-play error checking and discovery of gameplay glitches and bottlenecks, Automated Testing can greatly speed and enhance development. This can include an array of techniques, from short scripts to sets of rules and goals to full-blown simulations of human players.
But here’s the biggest nugget for GOFAI in games: Expert Systems. Capturing well-structure semantic knowledge is a perfect fit for strategy games. Many simple ‘How-To’ experts that encapsulate human knowledge can be easily created, rapidly consulted, and efficiently stored without any massive frameworks or ML tools. The skill of AIs can be enhanced merely by ‘learning’ from human players. Note that this is far, far simpler than actual Machine Learning. Most rule knowledge bases take up kilobytes of RAM, not gigabytes of code and storage. A packaging system would allow players to share (or even purchase) expert systems from others. Both gameplay and AI skill could be upgraded and personalized. This brings in a robust educational component, again, like Minecraft does. Human readability is key. We all have a basic comprehension of facts, rules, and inference. There’s no daunting learning curve required. Why not incorporate this innate comprehension into games?
In summary, gameplay has become too enamored of shiny bobbles and photo-realism. The current wave of Machine Learning is leading to evermore complex and power-hungry requirements, a burden on both developers and players. GOFAI could mitigate things rapidly and easily, and shift the game experience from the eyes to the mind’s eye. Game dev expands beyond the studio, into the laboratory. Let the fun begin.
