From the Governor , I thought ? <| sep |> I will preface this by saying that I have a moderate interest in game design , but only a rudimentary understanding of machine learning algorithms . As such , I ' m not sure if what I ' m going to say is actually applicable , but I ' ll have a go anyway . As uzeddyzed mentioned , quantifying the amount of ' fun ' a player is having at any moment in time is a crucial problem . However , I would argue that it ' s not an impossible problem , per se . Reducing complex subjectivist structures into more or less objective conceptual frameworks is one of the domains of scientific research after all . Here I ' m going to assume the stance of an objectivist researcher , in that I believe that even though ' fun ' is a subjective variable , it would potentially be possible to come up with a model that would treat it as something that can be measured and manipulated . It would take a lot of work and research , of course , but I think it could be possible . What exactly do I mean by that ? I ' m going to propose a number of ways in which I think machine learning algorithms can be employed to enhance the ' fun ' aspect of a game . First , consider the problem of difficulty levels . The standard choice is between ' easy ', ' normal ' and ' hard '. Of course , some games extend this scale to add ' hardcore ' or ' pacifist ' modes , but let ' s consider the three standard ones . An often mentioned issue is that generally speaking the developers don ' t have time to optimise gameplay for every player skill level . As such , often the ' normal ' difficulty level constitutes the experience that the developers have actually designed for us to consume . More often than not , the other difficulties come down to manipulating variables such as hit points , or damage , for both the player and enemies . How does that translate into gameplay changes ? I ' m going to generalise heavily here , and talk about my subjective experiences as well . The ' easy ' mode is often perceived as the ' I ' m playing for the story ' mode . I think it is a very important option to include in games to cater to the players who want to experience the game at their own pace , making it very much a leisure activity . If someone with no FPS experience wants to enjoy BioShock for the story , let them . The ' hard ' mode caters to more competitive players who see beating a game as a challenge . However , often people complain that harder difficulties suffer from debilitating lack of balance , turning enemies into bullet sponges and the player into a onehit ragdoll . As an experienced gamer , I tend to play on either ' normal ' or ' hard '. But it can often be a tricky choice for me . Too often I find that the ' normal ' mode doesn ' t present a legitimate challenge to me , while the ' hard ' mode is too unforgiving . I like a challenge , but I don ' t like replaying the same stretch of a level dozens of times just because I get killed with a couple stray bullets , while I have to unload a full clip to subdue a single enemy . For me this i breaks the immersion I ' m supposed to be this badass dude , fighting against other dudes , why are they stronger than me ? !, and ii gets me frustrated very quickly . An extreme example out of the games I played most recently is Painkiller Overdose . On regular difficulty your health is fully replenished at every checkpoint in a level . That ruined the fun aspect for me , since the element of risk was essentially removed . Conversely , on the hard difficulty it would take me ages to complete a level . Health replenishment is extremely scarce , and each wave of enemies presents a deathly threat . I had to micro manage my health pretty much at every checkpoint , lest I find that the next stretch is impossible to beat . Going back to the topic of fun , wouldn ' t it be great if an algorithm would adapt a game ' s difficulty level to each player ? Make difficulty a continuum , rather than a discrete choice . In an ideal implementation , I envision a system that somehow uses the first area of the game the tutorial to gauge a player ' s ability , and then adapts the difficulty accordingly , tweaking as appropriate throughout the game . The process could even be ' encoded ' inside the game ' s world , e . g . your enemies are robots that ' adapt ' to your play style in nonimmediately obvious ways . The main issue does come down to measuring fun . Some people like to chill , some people like a challenge . Making an algorithm that would accommodate for different styles would be tricky , but would it be impossible ? Speaking from my experiences , my difficulty ' sweet spot ' lies somewhere between ' normal ' and ' hard '. A system that could take note of my progress and adapt accordingly would be great . My second point is inspired by my experiences with loot systems in games like Diablo II , or Borderlands . The most recent one I ' ve played is Titan Quest . The problem I find with randomised loot is that more often than not it truly is randomised , which in itself is great as it adds elements of unpredictability , but I often find myself thinking that there must be a better way to handle such systems . For example , it ' s often the case that I chance upon powerful loot sometime in midgame , and I stick with it for the remainder of the game simply because I don ' t get across anything better . This kind of defeats the point of such looting systems , as you end up sticking with rare loot that just happened to drop , and nothing else actually provides value to the player . Would it be possible to make a loot system that avoids this shortcoming ? An algorithm that would run in the background , ensuring that you find new useful gear more consistently ? The difficulty would lie in avoiding a system that would constantly ' handhold ' players and shower them with useful loot . An element of randomisation is great , but perhaps tweaking it to work more smartly could work , too . Again , I ' m not sure if any of this actually constitutes machine learning , or maybe some games already do this , but I certainly think more games could try to implement adaptive gameplay algorithms in more novel ways .