By Qiang Yang
This monograph on making plans in line with man made Intelligence equipment might serve either as a sophisticated textbook and as a basic reference booklet. AI making plans is an lively examine and purposes box occupied with motion and plan illustration, plan synthesis and reasoning, research of making plans algorithms, plan execution and tracking, and plan reuse and learning.
The ebook offers a transparent, thorough assurance of key components of classical AI making plans. Its major subject matter is to construct extra intelligence on a suite of simple algorithms and representations for making plans. It offers complicated options for plan new release utilizing decomposition and plan merging and for examining and evaluating making plans algorithms. The ebook includes illustrations, examples, algorithms, analyses, tables, and huge references.
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Additional info for Intelligent Planning: A Decomposition and Abstraction Based Approach
Subsequently, threats to this causal link are removed. This process repeats until every precondition of every step in the plan has an associated causal link, and all negative threats (see below for a definition) in the plan are removed. 2 shows a top-level version of a partial-order, backward-chaining algorithm. The initial plan consists of the start-step/finish-step pair. In steps 5, 7, 8 and 10, the algorithm uses a number of subroutines which we will flesh out in order of appearance. 3 demonstrates the plan-generation process.
The STRIPS representation is sometimes called propositional, since it permits only conjunctive literals as operator preconditions and effects. In many practical situations, however, the propositional representation is not sufficient. One often needs to consider universally quantified preconditions and effects, as well as conditional effects. Consider the house painting example. Suppose that one requirement for painting the ceiling is that all furniture in the room is covered with dropcloth. Instead of stating Covered(F) for each individual piece of furniture F, we can use a variable?
In the first, a decision has to be made as to which node among the set of all frontier nodes should be selected next for expansion. A naive strategy would be to order the frontier nodes by the total cost of operators in a current plan; a plan with the minimal cost is selected next. A second dimension of search control is defined as the problem of selecting a next "flaw" to work on, given that a frontier node or a plan has been selected. Here a flaw is either an open precondition or a threat to be resolved.
Intelligent Planning: A Decomposition and Abstraction Based Approach by Qiang Yang