By Narinder Singh
I am indebted to my thesis consultant, Michael Genesereth, for his advice, idea, and help which has made this examine attainable. As a instructor and a sounding board for brand new rules, Mike used to be tremendous priceless in declaring Haws, and suggesting new instructions to discover. i might additionally wish to thank Harold Brown for introducing me to the applying of man-made intelligence to reasoning approximately designs, and his many useful reviews as a reader of this thesis. major contribu tions by way of the opposite participants of my analyzing committee, Mark Horowitz, and Allen Peterson have vastly more suitable the content material and association of this thesis by means of forcing me to speak my principles extra basically. i'm tremendous thankful to the opposite contributors of the common sense crew on the Heuristic Programming venture for being a sounding board for my principles, and supplying worthwhile 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 learn used to be supported by way of 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 great setting for appearing this research.
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Additional resources for An Artificial Intelligence Approach to Test Generation
In this chapter we will present a more formal discussion of reformulation. Before we define how designs can be reformulated, we present a precise definition of a device which is a physical entity, and a design, which is a specification of this device. We will next examine the different types of design reformulation operations, and demonstrate the utility of performing these reformulations by examining their impact on the size of the search space, and the size of the design. We will show that the different reformulation operations can reduce both the depth and branching factor of the search space, and also reduce the size of the design.
For example, the right two solutions in part (b) of the figure include identical solutions for controlling the sum output to o. The identical solutions arise due to the fanout points at the first two inputs of the full-adder. 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 set of objects includes the modules, ports, connections and statevariables of the device, and the values used to describe the behavior of this device. Modules define the components of a design. Each module has a set of input and/or output ports which are the only points through which it can communicate with its environment. Communication between modules is defined by connections which relate the values of the ports at its two endpoints. State variables are used to define a partial history of the values at ports, or the internal state of modules.