Action selection is a process involving how a designed intelligent system will react next to a given problem. It is usually a field studied in psychology, robotics, and artificial intelligence. Action selection is synonymous to decision making and behavioral choice. The data gathered is researched and broken down in order to be able to adapt it to artificial systems like robotics, video games, and artificial intelligence programming.
Much of the data in the life sciences can be observed and experimented upon to evoke a variable response. All living creatures have their own instinctual reaction to food, predators, and mates. Creating a controlled environment where the studied animals are observed to always perform different solutions to different problems provides researchers and programmers with a basis for the advancement of their study. This in turn has led researchers and programmers in trying to recreate those instinctual responses in a controlled manner.
For researchers and programmers, the most common questions used in action selection are focused on what to do thereafter and what happens next. The responses in turn can be recycled for a new batch of experimental action selection. Prime examples of action selection can be found in games and artificial intelligence programming. In computer games, it can be found in First-Person Shooters (FPS) like Halo and Counter-Strike. Creatures, a pet-based game, uses an artificial intelligence engine that can make its own decisions by adapting to tasks.
What makes action selection a unique field is that there is always a strict guide to follow to have an acceptable level of data. The guide would always be based on a subject that is patterned off of a human or animal. For most, if not all researchers and programmers, a subject will always need to be placed in a location where the environment is unpredictable and is always changing. The subject will also need to react on time while performing a number of tasks. It must also interact with real live human beings in order to bring in a randomizing factor.
With those many random factors and a strict guideline to follow, research is never ending since there will always be a different set of circumstances for each experiment. One primary factor that makes researchers and programmers study this field intensively is response time. With each successful experiment where the subject has learned an action, a different avenue of action will come up. This in turn makes for a more complex subject when compared to a previous version.
