Abstract
A problem solver that more clearly defines its goals can make better decisions about the activities it should pursue. The problem solver can use the specific attributes of a more well-defined goal to constrain the possible steps leading up to that goal: it can back-propagate the desired characteristics of the goal to formulate a specific sequence of subgoals leading to the solution, and in turn can use these well-defined subgoals as context for determining what actions to take. In short, a goal-directed problem solver can identify and perform actions that lead toward its well-defined long-term goals, whereas a data-driven problem solver takes actions that seem promising based on its current state without any view of the long-term significance of those actions.
“Cheshire Puss,” she began ... “would you tell me, please, which way I ought to go from here?”
“That depends a good deal on where you want to get to,” said the Cat.
“I don’t much care where—” said Alice.
“Then it doesn’t much matter which way you go,” said the Cat.
“—so long as I get somewhere,” Alice added as an explanation.
“Oh, you’re We sure to do that,” said the Cat, “if you only walk long enough.”
-Lewis Carrol (Alice in Wonderland)
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© 1988 Kluwer Academic Publishers, Boston
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Durfee, E.H. (1988). Identifying Local Goals Through Clustering. In: Coordination of Distributed Problem Solvers. The Kluwer International Series in Engineering and Computer Science, vol 55. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1699-2_3
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DOI: https://doi.org/10.1007/978-1-4613-1699-2_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8958-6
Online ISBN: 978-1-4613-1699-2
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