By Narinder Singh
I am indebted to my thesis consultant, Michael Genesereth, for his advice, notion, and aid which has made this examine attainable. As a instructor and a sounding board for brand spanking new rules, Mike was once super valuable in declaring Haws, and suggesting new instructions to discover. i might additionally wish to thank Harold Brown for introducing me to the applying of synthetic intelligence to reasoning approximately designs, and his many worthwhile reviews as a reader of this thesis. major contribu tions through the opposite participants of my studying committee, Mark Horowitz, and Allen Peterson have significantly enhanced the content material and association of this thesis by way of forcing me to speak my rules extra essentially. i'm tremendous thankful to the opposite contributors of the good judgment team on the Heuristic Programming undertaking for being a sounding board for my rules, and supplying precious reviews. specifically, 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 examine used to be supported through Schlumberger Palo Alto learn (previously Fairchild Laboratory for man made Intelligence). i'm thankful to Peter Hart, the previous head of the AI lab, and his successor Marty Tenenbaum for delivering a very good setting for acting this research.
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Extra resources for An Artificial Intelligence Approach to Test Generation
The remainder of this subsection will present an example illustrating the utility of reasoning with the reformulated design of the full-adder device, and later analyze the utility of structural abstraction in general. Suppose we have the goal of controlling the carry output of the device to 1, or o. 4. The top-half of the figure shows the search space for controlling the carry output to 1, and the bottom half shows the search space for controlling the carry output to o. In this figure we have used the name of a port to represent the goal of controlling its value to 1, and name of a port with an overbar to represent the goal of controlling its value to o.
Value abstraction provides a reduction in the average number of nodes to explore which is exponential in the number of bits at each 4The inputs to the second multiplier are 2n - 1 bits wide. However, since e is connected to b which has n bit inputs, the n - 1 most significant magnitude bits of e are always o. 10: Search space using the value abstracted behavior of multipliers. input of the first multiplier. This reduction is primarily due to the reduction in the branching factor of the nodes in the search space, since the depth of the two search spaces is approximately the same.
For a system of linear equations, the cost of solving these constraints is polynomial in the depth of the circuit using linear-programming techniques, which is substantially cheaper than search which is exponential in the depth. These savings are made possible because by using constraint propagation we only propagate information through each module and connection once, and the cost of solving these constraints is polynomial in the depth. Using search, however, we may have to repeatedly propagate information through the same modules and connections in the design, each time for a different value.
An Artificial Intelligence Approach to Test Generation by Narinder Singh