Log Feb-23

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Log Feb-16

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Log Feb-9

[Logs are for personal reference]


Article classification (so far)

RTS Specific AI Articles

RTS Implementations

Serious Games

Serious Game Examples

Other Game Examples



Book: Good Video Games and Good Learning

Good Video Games and Good Learning
James Paul Gee, 2007

Get the book on Amazon.com
ISBN: 978-0-8204-9703-7

This book includes a collection of essays about videogames and their influence in learning and literacy. I really like this book because it gives a really good insight on videogames from a cognitive science point of view. It really is amazing how videogame components can be directly applied in schools and other learning environments.

Good Video Games, the Human Mind, and Good Learning

Good Video Games, the Human Mind, and Good Learning
[James Paul Gee, 2007]

Gee analyzes in his book "Good Games and Good Learning" different topics related to videogames, learning and literacy, such as motivations for players to keep playing and reasons why videogames are good learning tools.

In this chapter, Gee gives a series of principles that can be found in good learning practices that can be implemented in videogames. Gee states that we can make school and workplace learning better if we play attention to good videogames (which does not necessarily mean using videogames in school but he highly recommends it anyway). The principles suggested by Gee are the following:

1 - Co-design

Good learning reuires that learners feel like active agents, not just passive recipients.

In videogames, players make things happen. This kind of interactivity encourages the player to care about what's happening.

2 - Customize

Different sytles of learning work better for different people.

In videogames, players are able to customize the gameplay to fit their learning and playing styles.

3 - Identity

Deep learning requires an extended commitment and such commitment is powerfully recruited when people take on a new identity they value.

In videogames, players often assume other identities. Players can experiment with taking actions they normally wouldn't take or experience a completely different lifestyle.

4 - Manipulation and Distributed Knowledge

Humans feel expanded and empowered when they can manipulate powerful tools in intricate ways tha extend their area of effectiveness.

In videogames, the more a player can manipulate a character, the more involved the player will become.

5 - Well-Ordered Problems

The problems learners face early on are crucial and should be well-designed to lead them to hypothses that work well later.

In videogames, problems are usually presented in an increasing difficulty order which lets players form a good guess on how to proceed when they face harder problems.

6- Pleasantly Frustrating

Learning works best when new challenges are at the outer edge of, but within their competence.

Good videogames adjust challenges and give feedback so that different players feel the game is challenging but doable.

7 - Cycles of Expertise

Expertise is formed in any area by repeatd cycles of learners practicing skills until they are automatic, then having those skills challenged, point in which the cycle starts again.

Good games will create situations that allow extended practice and then tests of mastery of that practice, then a new challenge, etc. When a game does this well, it's considered to have a good pacing.

8- Information "On Demand" and "Just in Time"

Humans can use verbal information better when it is given just when they can put it to use ad when they feel they need it.

In a good game, players will not need the manual to play, but can use it as reference. After the player has played for a while, the game has already made much of the verbal information in the manal concrete.

9 - Fish Tanks

Fish tanks are simplified eco-systems that display some critical variables and their interactions that are otherwise obscured in the complex eco-system in the real world.

In videogames, fish tanks can be found in the form of tutorial levels, which generally are stripped down versions of the game.

10 - Sandboxes

Sandboxes are situations in which learners feel like the they are experiencing the real thing but with the risks and dangers greatly reduced.

In games, sandboxes are parts of the game where things cannot go too wrong too quickly. Many games offer the tutorial levels or first levels as sandboxes.

11 - Skills as Strategies

People don't like practicing skills out of context over and over again, but without lots of skill practice, they cannot get good at what they're trying to learn.

In videogames, players learn and practice the skills they need and want, because as a result, they will accomplish a goal thus, they can be perce

12 - System Thinking

People learn skills, strategies and ideas best when they see how they fit into an overall larger system.

Good games help players understand how each of the elements in the game fit into the general system of the game and its genre.

13 - Meaning as Action Image

Humans think through experiences hey have had and imginative reconstructions of experience.

This is the basis of videogames. They make the meanigs of words and concepts clear through experiences the players have and the activities they carry out.

With this principles, it's easy to see how videogames can teach us a lot about the learning environment, but also, they prove that videogames can be really good learning platforms. Serious Game designers must consider these principles when designing their next game, as they will greately improve the player's experience.

This information can be found in the book "Good Video Games and Good Learning".

Gee, P (2007). Good Video Games, the Human Mind, and Good Learning. In C. Lankshear and M. Knobel (Eds.) Good Video Games and Good Learning (pp 22 -44). Peter Lang

Article: Real-Time Strategy High-Level Planning

Real-Time Strategy High-Level Planning
[Stefan Weijers, 2010]

In this article, the author analyzes the different elements needed for implementing AI in RTS games as well as different techniques used to achieve this. Here is a small summary:

Real-Time strategy games are computer games in which the player controls an army in real-time to destroy other players' armies, with each player requiring resources and buildings to create such army. The key to winning is to balnce the army production with the gathering of resources.

Tasks in real-time strategy games can be split in three levels of abstraction:
  • Unit Control (Lowest level, players control a specific unit).
  • Tactical Planning (Make plans on how to attack the enemy).
  • Strategic Planning (High-Level decisions involving army creation and management).
Identified Problems in real-time strategy games:

Resource Management
The gathering and balancing of resources can be done with a reflex agent. The AI has all the relevant information and can simply assign more units to a certain resource when it runs low.

Decision making under uncertainty
To be able to recognize important events, the game needs some sort of pattern recognition. Even with no concrete information available, the AI should be able to plan future actions.

Spatial and temporal reasoning
Applicability of a strategy needs to be reevaluated constantly. Terrain analysis is really importante for this. An important problem encountered for this aspect is the process time, because the world might have changed while the AI is still calculating its strategy.

This aspect is clearly lacking in RTS. Computer players never work with human players to overcome a strong opponent. The AI should be able to recognize the allied strategy and help them in the right way.

Opponent modeling, learning
Human players can spot weaknesses in a strategy and exploit them while computer players have a hard time accomplishing this. The AI needs to learn from previous experiences and formulate aplicable counter strategies.

Reasons why current commercial RTS AI fail to challenge human players:

Predictability and lack of spatial reasoning.
When the AI is predictable, players will inevitably exploit it. The predictability of commercial AIs comes from the fact that all AI is scripted.

Commercial games include a set of scripts that translate into several different strategies that the AI implements to give some diversity to games but that carries several problems:

Hard to implement
Implementation of scripts needs several expertns in the game to think of viable strategies, the game has to be near completion before the AI programmers can start implementing it and even after implementation, it needs to be tested and tweaked thoroughly.

Domain Specific
After implementing a scripted AI for a game, the implementation is not applicable for other RTS games. This forces game developers to go thorugh the implementation cycle again.

Game developers use other means to entertain players. Different scripted strategies or giving the AI more information than it should have are some examples to create diversity or add challenge.

Academic Research to solve these issues:

Dynamic Scripting
A reinforcement learning technique for scripts. This allows the AI to generate a strategy on the fly by selecting a viable tactic in the tactic database. Also, this allows the AI to overcome static challenges (players that use the same tactic over and over again).

This, however, carries some of the problems. It still needs a group of experts to create viable tactics and because most research is done with static opponents to learn from, their ability to counter other adaptive players is doubted.

Monte Carlo Planning
This type of planning generates a set of simulations for all possible actions, it then chooses the plan that corresponds with the best simulation for the player. Because in an RTS game the amount of possible actions is enormous, an abstraction of actions and states has to be found.

The problem with this technique is that it requires a lot of calculating and it doesn't learn from previous mistakes.

Case Based Planning
This technique is based on case based reasoining, which is similar to the dynamic scripting method. It is based on states and looks at past experience to calculate the best sub plan in the current state. Case Based Planning doesn't model the opponent and wont do adversarial planning. It needs a database of predefined tactics. The algorithm picks a random tactic and remembers the outcome. This technique allows an expert to play the game and teach the AI what good decisions are, simplifying the process of designing an AI.

The problem with Case Based Planning is that it needs to be trained against opponents before it can become strong. The more complex and diverse the opponents are, the longer it will take the CBP to learn how to win.

The author finally concludes that the most advanced technique is the Case Based Planning because it can adapt, change and enhance strategies, but pointing out which one of the three is the best is hard because advance scripts have been made in commercial games and the performance of the Case Based or Monte Carlo planners have yet to be proved in more complex games.

Read the full article here!

Wijers, S., 2010, Real-Time strategy high-level planning. Obtained in August 11th, 2011 from PIXEL: www.inter-actief.utwente.nl/studiereis/pixel/files/indepth/StefanWeijers.pdf