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Mobile and Serious Games at GDC
Gamasutra's coverage of GDC this year is top notch, and they have several excellent pieces on the Serious Games Summit and GDC Mobile '05. Machine Learning details how game AI developers are turning to real life academic studies on decision making trees to make their work more realistic. A rundown on the Serious Games Summit Panel reveals answers from people who are making games for non-entertainment purposes. Finally, Final Fantasy for Mobile is an analysis of how the Square-Enix folks are planning to take the best-selling franchise to mobile devices. Meanwhile Alice's Wonderland blog continues to be the most immersive coverage here this year, with run-downs on Cory Ondrejka's Building Serious Games with Second Life talk and THQ's Tips from a Mobile Publisher session.
As opposed to those ivory tower academic studies, or the academic studies I make up to win water cooler debates ?
i always look forward to the next generation of games. i think that the generation of games after the next will change it all, and really bring us a immersed enviroment like we had been promised.
What, do they plan on re-releasing FFI-VI for a THIRD time for mobile phones now? Seriously. Square-Enix used to be about making good games. Now they are about making the most profit.
Hopefully I'm wrong about this though (the FF mobile deal, I don't think I'm wrong about my more general statement). I don't have a Gamasutra account to read the article and didn't feel like bothering to sign up for one.
Hero of Allacrost, a FOSS RPG for *NIX/*BSD/OS X/Win
The subscription/cost model is a little difficult to understand, though. I understand that you must buy land and IP of anything you create in Second Life belongs to you (I assume they mean copyright when referring to IP - hard to tell). What about the servers, though? Until they allow interconnections and expansion outside of their server farm, whatever they say about IP and user rights go out the window if they go belly up. Which means that there is the strong chance of designer intervention and not necessarily in the best interests of the residents, if that is what you want to call them.
Overall, though, I think that Second Life is one of the more interesting concepts in MMORPG's and one or two generations later may lead to some real advances towards a Metaverse type world.
What is this login shit on Gamasutra? Someone just post the text here so we don't all have to get the anal probe.
Fascism trolls keeping me up every night. When I starts a preachin', he HITS ME WITH HIS REICH!
Tutorial: Machine Learning Video game artificial intelligence has traditionally focused on simulating interesting behaviors, whether it's combat tactics, NPC interaction, stealth, or even story telling elements. In recent years, adventures, role-playing games and strategy games have shown us how sophisticated and rich game AI systems can be. Still, most of these games exhibit pre-scripted, "staged" behavior only, where character learning is a minor component, if taken into consideration at all. Adventure game characters will never remember us the second time they see us, and characters in a fighting game will seldom adapt to our fighting style through a series of combats. Still, as CPU power increases, and player's expectations sky-rocket, learning is slowly gaining ground in the game AI development community. A solid proof of this was evident at Monday's full day tutorial on game learning techniques at the Game Developers Conference in San Francisco, where professors John Laird and Michael Van Lent from the University of Michigan surveyed the different machine learning techniques to a room-filling audience. The tutorial was divided into chewable portions which, as a whole, provided a gentle but thorough overview of what machine learning (ML) is all about, the techniques and costs implied, and how games can benefit from it. Both speakers took turns, with frequent stops to allow questions from the audience, thus making a deep, complex subject easier to follow and understand. For the first portion of the talk, both speakers tried to give basic info on what ML is, when should it be used, and when it can or should be faked. ML is an added computational cost which only benefits some scenarios, and so it should only be used where it can positively affect the gameplay, not as a hyped piece of technology. Here's a recipe that appeared several times during the talk, and very well summarizes the pros and cons of learning in AI systems: Positive side of ML: More interesting, believable behavior due to learning Personalized, re-playable experience New types of games (Black and White and The Sims come to mind) Negative side of ML: More difficult to predict behavior, less control for designer May take a long time to evolve May get stuck / be unreliable It was interesting and refreshing to see two major league AI researchers give an unbiased, neutral opinion where ML is not the solution to all problems in the world, but just a new component that should be evaluated and used when needed. For the longer portion of the talk, both Laird and Van Lent focused on explaining classic machine learning techniques, with emphasis on their applicability for video games. Here's a brief overview of what was covered, and the key ideas: The first surveyed technique was classic decision trees, expanded with rule induction as the learning method. In decision trees, knowledge is represented in a tree with each node being a test, and each child node being an outcome of the test. So, by descending from root to leaves we select the exact configuration of the system, and thus the associated behavior. So, a tree may have a root that classifies characters between friendly or hostile. The second-level node may classify according to the type of weapon they are wielding, and the leaves may specify the behavior associated with each configuration (such as ENEMY with RANGED WEAPON implies FLEE behavior). Decision trees have been used in games for well over twenty years. What's interesting about them is that a number of algorithms (namely, the ID3 and the more recent C4.5 algorithm) have been devised to, given the right number of example cases, automatically build the tree by means of an induction paradigm. An example may look something like: = FLEE = ATTACK And by using ID3 on that set a tree would be built, and thus the character would automatically learn the decision criteria, correctly selecting behaviors according to that criteria. ID3 basically works recursively on the set of attributes to be used as classifiers
Is it a coincidence that the cell phone battery companies are sponsoring Final Fantasy and Dragon Quest for cell phone?
>A rundown on the Serious Games Summit Panel reveals answers from people who are making games for non-entertainment purposes.
Was the keynote speaker a SOE employee?
---- Take the Space Quiz!