Practical Planning. Extending the Classical AI Planning by David E. Wilkins

By David E. Wilkins

Planning, or reasoning approximately activities, is a basic component to clever behavior--and one who man made intelligence has came upon very tricky to enforce. the main well-understood method of development making plans structures has been lower than refinement because the overdue Sixties and has now reached a degree of adulthood the place there are solid customers for development operating planners.

Practical Planning is an in-depth exam of this classical making plans paradigm via a thorough case examine of SIPE, a considerably applied making plans method. the writer, the developer of SIPE, defines the making plans challenge mostly, explains why reasoning approximately activities is so complicated, and describes all components of the SIPE process and the algorithms had to in attaining potency. information are mentioned within the context of difficulties and significant matters in development a realistic planner; discussions of the way different platforms deal with those matters also are included.

Assuming just a uncomplicated history in AI, Practical Planning should be of significant curiosity to execs attracted to incorporating making plans functions into AI systems.

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Practical Planning. Extending the Classical AI Planning Paradigm

Making plans, or reasoning approximately activities, is a primary part of clever behavior--and one who man made intelligence has chanced on very tricky to enforce. the main well-understood method of development making plans platforms has been less than refinement because the past due Nineteen Sixties and has now reached a degree of adulthood the place there are sturdy customers for development operating planners.

Extra resources for Practical Planning. Extending the Classical AI Planning Paradigm

Sample text

T h e t r u t h criterion relies on the unification procedure; S I P E is a constraint-posting planner, so unification involves reasoning about constraints and deciding how much of the global constraint network to analyze. Chapter 5 describes S I P E ' s constraints and the unification procedure. T h e search algorithm must balance the use of resource reasoning and of plan critics with the application of operators. If the global constraint network is checked too frequently, the planning will be unnecessarily slow.

Split nodes have multiple successors, and join nodes have multiple predecessors. The subplans t h a t begin after the split node and end before the join node are referred to as parallel branches. Join nodes also have multiple successors, though the context selects only one of t h e m to be in the current plan. The other successors represent the remainder of the plan after alternative operator expansions have been applied within the split-join pair. S I P E assumes t h a t whenever a split-join pair is introduced, whether by application of an operator or in the original goals, the main effects of the last node in each parallel branch are intended to be true at the point in time represented by the join node.

Its triangle tables. T h e Hayes-Roths [18] use the t e r m hierarchical to refer to a top-down search of the space of possible plans where more abstract plans are at the t o p of this search space. This involves a hierarchical search space t h a t contains abstraction levels, b u t the levels in the hierarchy are not defined by these abstraction levels. "Hierarchical" planning is also used to refer to metaplanning. Reasoning at a metalevel involves reasoning about the planning process itself.

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