Can machines design? Can they come up with creative solutions to problems and build tools and artifacts across a wide range of domains? Recent advances in the field of computational creativity and formal Artificial General Intelligence (AGI) provide frameworks for machines with the general ability to design. In this paper we propose to integrate a formal computational creativity framework into the Gödel machine framework. We call the resulting framework design Gödel machine. Such a machine could solve a variety of design problems by generating novel concepts. In addition, it could change the way these concepts are generated by modifying itself. The design Gödel machine is able to improve its initial design program, once it has proven that a modification would increase its return on the utility function. Finally, we sketch out a specific version of the design Gödel machine which specifically addresses the design of complex software and hardware systems. Future work aims at the development of a more formal version of the design Gödel machine and a proof of concept implementation.
Modern avionics systems are characterized by their increasing complexity and demanding safety requirements. The fulfillment of safety constraints is often only tested after the design of the systems, which leads to costly and time‐consuming design iterations. The complexity of avionics systems is still managed through a number of predominantly manual, paper‐based procedures. Relying on systems engineering and operations research methods and tools, a methodology was developed to support the design process. This methodology should reduce the design duration through an earlier consideration of safety constraints. It should also contribute to the overall improvement of the design, through an automated approach of its complexity. A generic meta‐model is created, suitable for various kinds of avionics systems, which contains requirements ‐ including safety requirements ‐ and design goals. From these formal requirements and goals an algorithm creates corresponding mathematical constraints and optimization criteria. All valid architectures fulfilling these constraints are automatically generated. Sets of efficient and diverse architectures among the generated solutions are selected by an algorithm.
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