Game: Fold It

Fold It is a game that helps science. Instead of having a great amount of resources destined to fold proteins, they let players around the world do that. With so many players competing to fold proteins, predictions can be made of the shape certain proteins will take. Also, players could end up designing proteins that might just be helpful to cure diseases.

In this game, each level is a "puzzle" which consists on a protein that you have to fold. There are several rules that you can follow to increase your score, for example, the more compact the protein, the better score you get.

The game is quite complex but the tutorials introduce each concept quite nicely. By the end of all the tutorial levels, you'll be able to tackle puzzles with real protein shapes and compete with other people around the world for the best scores.

What I liked about the game:

• The tutorial levels introduce the game mechanics really well.
• The game helps scientists find patterns in protein folding, which will eventually lead to understanding diseases and body functions.
• The game gives you constant feedback and doesn't penalize mistakes so you can easily improve your protein folding techniques.

What I didn't like about the game:

• Because camera movement and protein movement is performed with the same mouse button (depending on where you click) sometimes I ended up moving the protein instead of the camera, effectively destroying everything I had accomplished (The game comes with an undo command, but still it would be nice to have a different button or combination of buttons to move the camera).
• Even though the tutorials are excellent, it takes a lot of time to get used to the game once you decide to try regular puzzles.

Download the game here

University of Washington (2009) Fold It [http://fold.it/portal/]

Game: Warning Forever

Although this game is not a serious game, it has a really interesting gameplay mechanic.

Warning Forever is a Top-Down Shooter game which consists on killing a boss in each level to proceed. The catch is that the boss gets progressively more difficult in each level and it will evolve depending on how you killed the last one.

If you keep killing the boss from the same spot over and over, it will grow arms on that place to make it harder to kill from that spot, or it might begin turning around as to force you to attack from a position you are least comfortable with.

If a certain weapon kills you, it is probable that further iterations of the boss will contain more of that particular type of weapon.

You can change your attack angle and make your shots narrower or wider so you can vary your strategy through each level.

The varying nature of the game, along with the need to see how the boss will evolve makes this game quite addicting.

What I liked about the game:

• Evolving bosses make this game a different experience each time you play.
• High scores encourage gamers to replay the game and reach further levels.
• Easy to learn gameplay makes it easy to pick it up.

What I didn't like about the game:

• Sometimes it's not clear how the boss will evolve, most of the time is trial and error.
• Once you figure out how the game works, it's possible to force the boss to evolve how you want, instead of the boss evolving to counter attack your strategies.

Download the game here

Hikoza T Ohkubo (2009) Warning Forever [http://www18.big.or.jp/~hikoza/Prod/dlcount.cgi?product=wf]

Article: Proof of Learning

Proof of Learning: Assessment in Serious Games

David Michael, Sande Chen [2005]

This article explains why it is not enough for a game to say that "it teaches" and leave it at that, but to include, like every other tool of education, a way of testing that the player actually learned something.

When a computer is used to test a student, what comes to mind are long sequences of multiple-choice questions (MCQs), which makes this the easiest choice for assessment in serious games. Sadly, MCQs are not always the best choice (I might go as far as to say that they are probably the worst choice), mostly because even if they are good to measure memorization and retention of facts, they fail at evaluating if the student is following a process correctly and also, during a game, this kind of assessment will be percieved as boring.

So, how can someone do a correct and useful assessment in the context of a serious game?

Currently, there are three main types of assessment that are used in serious games:

• Completion Assessment (The player completed the lesson?)
• In-Process Assessment (How did the player completed the lesson?)
• Teacher Evaluation (Does the teacher think the student understands the material?)

1) Completion Assessment

Translated to Serious Games, this could be if the student completed the game. The problem with this is that the player could have cheated or exploited holes in the system (which, as the article states, is something honored and rewarded between gamers, but not so between students). It's important to know whether the student learned the material or just learned the game and how to beat it.

2) In-Process Assessment

In Serious Games, this can be done by logging and tracking information while the student plays the game. For example, the time required to complete the lesson, the number of mistakes made, etc. Depending on the material that is intended to be learned, different kinds of information will need to be stored and different actions will need to be taken depending on them.

3) Teacher Evaluation

It's hard for a game to replace a teacher, so the game has to include tools to assist teachers in their evaluation, which can be homework control, grade tracking, reporting, etc. One example can be the inclusion of an 'Observer Mode' where the teacher and other students can actually watch the whole process.

As a game developer, implementing these methodologies can prove frustrating, because great care should be taken in maintaining the game fun. This is not an impossible task though, PIXELearning is a company that uses its own propietary engine to include pedagogy and assessment methodologies in their products.

One of these methodologies include a series of qualitative questions (I would assume at the end of a gaming session) such as: "You chose to do X. What was your basis for this decision? Why did you not choose Y?" So the teacher has more information available to judge how well the student really understands the material.

The article ends with a quote from Kevin Corti, former CEO at PIXELearning:

"[Serious games] will not grow as an industry unless the learning experience is definable, quantifiable and measurable. Assessment is the future of serious games".

Read the full article here

Michael, David; Chen, Sande. (2005). Proof of Learning: Assessment In Serious Games. Retrieved March 31, 2011 from Gamasutra.com: www.gamasutra.com/view/feature/2433/proof_of_learning_assessment_in_.php

Paper: Evolution of Artificial Intelligence In Video Games

Evolution of Artificial Intelligence In Video Games: A Survey

Ken Mott [2009]

In this paper, Mott explains how AI has been implemented in different types of games and the future challenges it faces in modern games.

One of the first rough implementation of game AI ever made was in the game Pong. The opponent's paddle had to move up and down to be able to return the ball to the player. This is known as Tracking AI. In Pong, this can hardly be considered intelligent, but this is how it began.

Another implementation of AI was done in board games that had been made into video games, like Backgammon. The AI used in these games was called Path Finding or Searching and was used to determine the next move of the computer.

In games like Space Invaders, a different form of AI was introduced: Pattern AI, which is used to make the enemies appear more human like. In Space Invaders, this was used when an enemy ship drops down to hit the player. Instead of following a straight line, it would track the player and curved it's path.

In modern games, AI has gained a lot of interest. In First Person Shooters, game AI is used to make enemies seek cover or to create a sense of teamwork between them using Waypoint AI. Mott states that further improvement is needed in this genre because bots still charge into enemy filled areas and fail to find new ways to traverse the level different from the intended one.

In Real Time Strategy games, AI is controlled by scripting. The problem with this is that players will learn the patterns of the enemy and adapt to them. To counter this problem, Dynamic Scripting can be used or even Genetic Algorithms to change the AI patterns.

Finally in games like The Sims, different algorithms are used not to make them seem intelligent, but to create what is called Artificial Life, which is when programmers try to make characters seem more human-like, not just in their decisions, but also in their behavior.

As time goes by and computers let developers create more complex AI without sacrificing graphics, we'll see much better AI agents that enhance the gameplay experience of players around the world.

Read the paper here.

Mott, Ken. (2009) Evolution of Artificial Intelligence In Video Games: A Survey

Paper: Player Modeling for Intelligent Difficulty Adjustment

Player Modeling for Intelligent Difficulty Adjustment

Olana Missura and Thomas Gärner [2009]

Automatic difficulty adjustment is a topic of great interest in game development. It can keep the player in the best possible 'fun' state and in the case of serious games, it can keep the player in the best state for whatever the game's objective is.

In this paper, the authors compare static to dinamic difficulty adjustment and explain their algorithm to determine a good difficulty setting for any player.

Traditionally, games where the player can adjust the difficulty involve the player choosing one of several difficulties and the game changing accordingly. The problem with this approach is that it's difficult for a developer to model the correct difficulty for each level of the game, as some might be perceived as too easy or too hard, depending on the player. To refine this, the developer needs to spend a great amount of resources to thoroughly test the difficulty settings of the game. The aim of this paper is to ease this process by modelling different players and trying to classify each player to set the difficulty accordingly.

The method they use for this purpose is quite interesting and it can be used in any game as long as the correct measurments are captured. They created a simple game with three difficulty settings. Game testers played the game setting the difficulty as they saw fit. While they were playing the game, data like score and player health was being recorded. After enough testers played the game, a clustering algorithm was applied to the data and was able to make a classification of the behavior of a player related to the difficulty they were playing in. So now, the only thing that has to be done is to classify a new player as fast as possible in the beginning of the game to automatically set the difficulty of the game.

This method had some technical limitations, like not every game during the same length of time and not every game ending the same way, but the data can be normalized to solve this problem.

They concluded that intelligently adjusting the difficulty was significantly better than the traditional static difficulty adjustment method and to further add to the investigation, different clasification algorithms should be tested, like nerual networks or gaussian processes.

Read the article here.

Missura, Olana; Gärtner, Thomas. (2009). Player Modelling for Intellignet Difficulty Adjustment

AI Topics on Educational Serious Game Development

Videogames have gained importance over the last few years, not only as entertainment products but also as a learning tools, advertising platforms and even as instruments for different types of scientific research. These are only a few of all the possible uses that videogames can have, but all of them can be grouped into a same category, which is called 'Serious Games'.

Focusing on the education genres of Serious Games I stumbled across several topics involving artificial intelligence that I found of great interest:

Natural Language Processing

Natural language applications that have been developed are far from perfect, but when correctly implemented, they can create experiences that can't be achieved with regular applications. One clear example is the game Façade, a game in which the player can play by saying what comes to his or her mind and the AI will make the characters react accordingly. Playing this game will reveal the advantages and disadvantages of such a broad gameplay mechanic: when the user plays the game as it was intended, the experience is enhanced to the point of being memorable, but when the player tries to 'game the game' (try to break it by writing nonsense or complex phrases), the AI tries as best as it can to give the player a reaction, but the game won't be able to react accordingly to each and every sentence thrown at it, which breaks the suspension of disbelief.

Another, more limited example is Scribblenauts series. In these games, the player has to solve different puzzle-like scenarios by using what he can find on said scenario. The catch is that the player can type anything up and that thing will materialize in front of his or her eyes. For example, if the objective is to reach an object that is too high, the player could type 'table' and 'chair', then place the chair above the table and jump on top of it to reach the object, or maybe another solution would be to type 'stairs' and see if the type of stairs that appear can be used. Crazier options include trucks, airplanes, atomic bombs, black holes, Santa Clause, Kraken, God, Chutlhu, and even meme characters like Longcat. Like Façade, this game won't be able to react to each and every word you throw at it, but because you use single words instead of complete sentences, the effect will last longer.

It would be interesting to see if natural language processing can be implemented in an education serious game to enhance the experience and improve the retention rate of the player.

Automatic Difficulty Adjustment

Being able to adjust the game's difficulty on-the-fly can be a great way to keep the player interested without the game becoming stale or too hard. In the case of a serious game focusing on education, this means keeping the player in the best possible learning state. There have been several attempts to do this, for example Watcharasatharpornpong proposed the use of genetic algorithms to automatically adjust the difficulty of a platformer game by generating levels that match the player's skill. Another example could be the game 'Warning Forever', a shooter in which the objective is to destroy the boss of the game in each level untill you die. The catch is that the boss learns from your previous actions and adapts accordingly in the following level, for example, if your strategy was to stay in the middle of the screen most of the time, it's probable that the next boss will turn around to force you to move from the center and change tactics.

It can also be interesting to see if this would be effective in an education-based serious game.

Expressive AI

Expressive AI is a term created by Michael Mateas which involves the AI making a game feel 'alive'. A simple example he provides is the famous game Pac-man. In this game, the ghosts' behavior make the player think that they are actually setting up traps but in fact, they are only following simple but really good thought out patterns. This effectively makes the game feel 'alive'.

The game Façade is also an example of a game Expressive AI because the characters really behave as expected creating the illusion that they indeed have feelings. This also makes the game fun, because it keeps the player interested and wanting to know how things will turn out, mostly because it's the player that's causing those reactions.

This is a broad topic that could find several implementations in an education-based serious game as a means to maintain the player engaged or as a means to improve the learning and retention rate of the player.

Educational games are perceived as 'boring', so creating an effective educational serious game that is perceived as a 'cool' game is quite a challenge, but with the correct implementation of several different techniques, it is possible to develop a fun game that engages players and at the same time, teaches them something without them noticing.

References:

• Watcharasatharpornpong, Nirach. Automatic Level Difficulty Adjustment in Platform Games Using Genetic Algorithm Based Methodology. (2009)
• Mateas, M. "Expressive AI: Games and Artificial Intelligence", in Proceedings of International DiGRA Conference. (2003).
• Wilcox, Bruce, Beyond Faade: Pattern Matching for Natural Language Applications. (2011)
• Bellotti, F., Berta, R., & De Gloria, A. (2010). Designing Effective Serious Games: Opportunities and Challenges for Research. International Journal of Emerging Technologies in Learning, 22-35.
• Iwatani, Toru. PacMan (1980)
• ProceduralArts. Façade (2005)
• 5th Cell. Scribblenauts (2009)
• Ohkubo, Hikoza. Warning Forever (2005)

Conference: The Mobile Serious Games Conference

Out & About: The Mobile Serious Games Conference

Out & About is the first ever event dedicated to the creation and distribution of serious games on mobile platforms.

New platforms, digital distribution strategies, and robust installed bases in mobile phones, and mobile handheld consoles have created an environment that makes it far more possible to consider deployment of serious games in the mobile space.

Out & About offers a means to see a combination of pioneering mobile serious games from the recent years alongside a number of new developments happening on iPhone, Android, and Nintendo DSi. From ARGs, to pedometer powered adventures, Out & About will bring you up-to-speed on this exciting new area of the serious games field.

Go to the Mobile Serious Game Conference main page.