Nondeterministic programming: Difference between revisions

Revision as of 12:51, 27 October 2011

Revision as of 05:01, 13 January 2012

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One method of choice is embodied in [[backtracking]] systems (such as [http://mitpress.mit.edu/sicp/full-text/sicp/book/node91.html AMB], or unification in [[Prolog]]), in which some alternatives may "fail," causing the program to backtrack and try other alternatives. If all alternatives fail at a particular choice point, then an entire branch fails, and the program will backtrack further, to an older choice point. One complication is that, because any choice is tentative and may be remade, the system must be able to restore old program states by undoing side-effects caused by partially executing a branch that eventually failed.

Another method of choice is reinforcement learning, embodied in systems such as [http://www.cs.berkeley.edu/~bhaskararussell/papers/aaai02-alisp/.pdf Alisp].{{Dead link|date=October 2011}} In such systems, rather than backtracking, the system keeps track of some measure of success and learns which choices often lead to success, and in which situations (both internal program state and environmental input may affect the choice). These systems are suitable for applications to [[robotics]] and other domains in which backtracking would involve attempting to undo actions performed in a dynamic environment, which may be difficult or impractical.

==See also==