23 March 1993 Multilevel causal-process modeling: bridging the plan, execution, and device implementation gaps
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Knowledge-based systems typically require extensive knowledge about the domain in which they are to operate, including knowledge about the objects and systems which exist within the domain, their properties, architectures, capabilities, and interactions. This is particularly the case for systems that require complex reasoning—e.g., systems that perform explanation, model-based and qualitative reasoning, multilevel and integrated reasoning, design/redesign, training and tutoring, and certain systems for knowledge acquisition and machine learning. Acquiring and representing knowledge at the breadth and depth required for these approaches is especially challenging in complex, real-world domains such as industrial and aerospace applications. On the other hand, it is commonplace in these domains for equipment to be well documented, both before and after manufacture, with detailed specifications about its functionality and performance characteristics. It will soon be the case that most of these equipment specifications will be written and developed as executable software simulations in hardware description languages such as the VHSIC Hardware Description Language (VHDL). VHDL was developed during the early 1980's as a standard, vendor independent description of hardware. With the growing complexity of microelectronics, it was clear that schematic representations would no longer be adequate for description and subsequent support. VHDL was standardized by the IEEE in 1987, thereby giving the industry a standard hardware description language. VHDL supports multiple levels of abstraction, but is most useful at describing the functional or behavioral aspect of hardware. It allows the designer to write a high level description of a piece of hardware. This description can then be used for simulation, analysis, and by a multitude of other tools that need to understand the structure and behavior of a specific piece of hardware. Clearly, it would be of great benefit for knowledge-based systems to obtain some of their prerequisite knowledge from such sources. As one prominent researcher in the area of explanation based learning speculated: "We foresee a future in which manufacturers of component equipment themselves provide descriptions, in some standard knowledgebased formalism, of the functionality of their product just as today they provide technical descriptions in a form understandable to [equipment] designers. As the manufactu,rer makes available refined versions ofinstalled equipment, the old knowledge-based description of the component is simply supplanted with the new. The knowledge engineer need only oversee incorporation of the new knowledge insuring that there are no negative interactions that harm overall system performance." 1 Realizing this vision of the future involves several technical challenges. One challenge is it to bridge the representational gap between the syntax of hardware design languages and Al systems. We do not see this as a significant technical hurdle because several studies have already demonstrated that this can be done. These investigations have used Al reasoning systems to reason about hardware models 2,3,4,5 or have done model-based reasoning using VHDL 6,7 Amore significant challenge involves bridging a gap in semantics. By this we mean going from reasoning at a relatively well-defined and self-contained level of hardware devices to planning and executing actions in the real world involving uncertainties associated with external agents and incomplete, uncertain, and incorrect information. All the previous studies we have cited principally reasoned about behaviors and functions of a device and its subcomponents, not about the function of the device and its subcomponents in the real world. Although one can envision modeling the whole world as a device in which any given device is a subcomponent, this has generally not been a tractable approach. It is this gap between modeling plans and their execution in the real world and modeling the internal behavior of devices in hardware design languages that is the focus of this paper.
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Keith R. Levi, Dale Moberg, Christopher A. Miller, Fred Rose, "Multilevel causal-process modeling: bridging the plan, execution, and device implementation gaps", Proc. SPIE 1963, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, (23 March 1993); doi: 10.1117/12.141741; https://doi.org/10.1117/12.141741

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