There is a growing body of research into how a learner’s (human or machine) goals can greatly influence the learning process. Study has taken place in fields such as cognitive science, psychology, education and, of most interest to us, artificial intelligence. Prior to the interest in goal-driven learning, most studies in this field focused on providing estimated functions based on limited inputs and outputs, without concern for the learning goal.
The underlying principal of the goal-based methodology is that learning is, for the most part, a strategic and active attempt on the part of the learner to identify and solve a particular problem or set of problems within the context of the tasks, goals, prior knowledge or expertise available locally as well as opportunities provided by the problem-space for learning. The learning process should be guided by good decision making in regards to what information is needed to achieve a particular goal-state because the value of what is learned is dependent on what impact that learning has on achieving a goal.
In a person, a goal can be directly linked to their ambition or determination to achieve some end. A goal is a desired outcome. A machine can seem determined because it will persevere with a task until it is finished but this is an illusion and a machine can’t desire anything. Another aspect of a goal is that it must be a tangible outcome; you must be able to describe the desired finishing state or else how would you know if you had achieved it?
We can then say that for a machine to have a goal it must have two fundamental things; a description of what the end state should be and the ability to persist until it reaches this state...
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...ts in the pursuit of their sub-goals which in turn contributed to a successful overall outcome. By breaking any problem into sub-goals where each agent can be allowed to develop the best possible individual relationships locally but with a purview that each sub-goal group must contribute to the global end-goal, we can create a system which benefits from both goal schemes. This creates a more robust machine that is also adaptable to new situations.
cyberneticzoo.com, (2009). 1951 - SNARC Maze Solver - Minsky / Edmonds (American) - cyberneticzoo.com. [online] Available at: http://cyberneticzoo.com/mazesolvers/1951-maze-solver-minsky-edmonds-american/ [Accessed 18 Apr. 2014].
Minsky, M. (1988). The society of mind. 1st ed. New York: Simon & Schuster, inc.
Ram, A. and Leake, D. (1995). Goal-driven learning. 1st ed. Cambridge, Mass.: MIT Press.
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