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Artificial Intelligence for Advanced Problem Solving by Dimitris Vrakas PDF

By Dimitris Vrakas

ISBN-10: 1599047055

ISBN-13: 9781599047058

Probably the most vital services of man-made intelligence, computerized challenge fixing, is composed customarily of the advance of software program structures designed to discover suggestions to difficulties. those platforms make the most of a seek house and algorithms with the intention to succeed in an answer.
Artificial Intelligence for complicated challenge fixing Techniques bargains students and practitioners state of the art study on algorithms and methods akin to seek, area autonomous heuristics, scheduling, constraint pride, optimization, configuration, and making plans, and highlights the connection among the quest different types and a number of the methods a selected program might be modeled and solved utilizing complicated challenge fixing suggestions.

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Behavior of the Planning Module In order to assess the performance of the planning function, tests are conducted on a single Sun Blade 1500 computer. The planning module is requested to provide a plan for four 8&$9ZLWK four targets on a graph with 50 nodes and 57 edges. The searches for a set of solutions by each vehicle correspond to a constraint satisfaction problem of about 300 variables and 500 constraints. This step leads to the generation of 46 solutions for WKH¿UVW8&$9DIWHUVHFRQGVVROXWLRQVIRU the second 8&$9DIWHUVHFRQGVVROXWLRQV for the third 8&$9 DIWHU  VHFRQGV DQG  solutions for the fourth 8&$9LQVHFRQGV,W is interesting to note that the number of solutions IRXQGLQWKH¿UVWVWHSFRUUHVSRQGVWRWKHVL]HRI the domains of four of the variables of the second step.

For each cell a local cost and an optimal cost to go under relaxing assumptions are computed. The A* algorithm uses the optimal cost to go as a heuristic function. , 2005) or by the visibility method (Kuwata, 2003). , 2002; Strady-Lécubin & Poncet, 2003). For the example problem, the graph given to the planning module is complemented by the visibility method in order to provide paths around the threatened areas. Each node j of the graph presents a set of upstream edges (Inj) and a set of downstream edges (Outj).

The remainder of this chapter is devoted to this topic and focuses on the extension of classical planning to deal with time and actions with duration, that is, temporal planning. Temporal Planning Motivation In real problems, the motivation for dealing with durative actions is clear: actions usually have widely different durations (Garrido, Fox, & Long, 2002; Smith & Weld, 1999). For instance, in a lo- gistics domain we cannot assume that two actions À\ SODQHFLW\$FLW\% and À\ SODQHFLW\$FLW\& have the same duration, because the average speed of the planes or the distance between the cities FDQ EH VLJQL¿FDQWO\ GLIIHUHQW 'LVFDUGLQJ WKH assumption that actions have the same duration leads to a more complex planning problem: not RQO\GRHVWKHSODQQHUQHHGWR¿QGWKHDSSURSULate set of actions to achieve the goals, that is, the plan, but also the appropriate execution time when the actions need to be executed, which highly increases the size of the search space.

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Artificial Intelligence for Advanced Problem Solving Techniques by Dimitris Vrakas

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