By Narinder Singh
I am indebted to my thesis consultant, Michael Genesereth, for his assistance, thought, and help which has made this examine attainable. As a instructor and a sounding board for brand spanking new rules, Mike was once super valuable in stating Haws, and suggesting new instructions to discover. i'd additionally prefer to thank Harold Brown for introducing me to the appliance of synthetic intelligence to reasoning approximately designs, and his many helpful reviews as a reader of this thesis. major contribu tions via the opposite individuals of my studying committee, Mark Horowitz, and Allen Peterson have enormously superior the content material and association of this thesis by means of forcing me to speak my rules extra truly. i'm super thankful to the opposite participants of the good judgment workforce on the Heuristic Programming undertaking for being a sounding board for my rules, and delivering beneficial reviews. particularly, i need to thank Matt Ginsberg, Vineet Singh, Devika Subramanian, Richard Trietel, Dave Smith, Jock Mackinlay, and Glenn Kramer for his or her pointed criticisms. This study was once supported by way of Schlumberger Palo Alto examine (previously Fairchild Laboratory for synthetic Intelligence). i'm thankful to Peter Hart, the previous head of the AI lab, and his successor Marty Tenenbaum for delivering a superb surroundings for acting this research.
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Extra info for An Artificial Intelligence Approach to Test Generation
Parallel converter at the output, the ports of these components, the 8 connections, the boolean truth values 0 and 1, and the set ofreal numbers (for the integer inputs, and the current time). The set of functions in the original design F define the behavior of the full-adder, register, parallel to serial converters, serial to parallel converter, and the connections. Similarly, the set of relations in the original design R define the type of each component, and define the endpoints of the connections.
Different choices in the search space can converge at a common set of fanout nodes to define identical subgoals. Fanouts are used to share hardware across different functions to minimize the area, and the power consumption of the device. Since fanouts are common in digital designs, redundant subgoals can be expected to occur frequently in the search space. The nodes in the search space also include extraneous conjuncts in their subgoals. For example, in controlling the carry output to 1 the last conjunct b in the solution cab is extraneous.
In the remainder of this subsection we will evaluate the impact of structural abstraction on the efficiency of the reasoning process and the size of the design. In examining the efficiency of structural abstraction we will compare the relative sizes of the search spaces for a flat low-level design formulation and an abstracted design formulation, including a 33 Reformulation hierarchical design formulation. We will compare the sizes of the search spaces for the goal of controlling an output of the design to some value (we are only interested in a single solution).
An Artificial Intelligence Approach to Test Generation by Narinder Singh