Propositus is a concept developed to improve the study of complex systems. It has been used in a variety of fields, including mathematics, computer science, and engineering. The concept is based on the idea of making a set of assumptions about the system and then using these assumptions to understand the system’s behavior.
Definition
Propositus is defined as a process of studying a complex system by making assumptions about its behavior, and then using those assumptions to gain insights into the system. It is an iterative approach that involves making and testing hypotheses about the system’s behavior, and then refining those hypotheses as new evidence is uncovered.
History
The concept of Propositus was first introduced by computer scientist and mathematician John von Neumann in the 1940s. He used it to study mathematical models of self-replicating systems. Since then, it has been used in many different fields, including economics, psychology, and engineering. It is also used in artificial intelligence research to understand the behavior of complex systems.
In the 1960s, computer scientist Marvin Minsky used Propositus to study neural networks. He proposed that a neural network could be understood by making assumptions about its behavior and then testing those assumptions. Since then, Propositus has been used in many different areas of artificial intelligence research.
In the 1990s, Propositus was also used in the development of adaptive systems. These systems are designed to be able to adapt to changing environments and conditions. By making assumptions about the system and then testing those assumptions, researchers are able to develop adaptive systems that can respond to changing conditions.
References
Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley.
Minsky, M. L. (1988). The Society of Mind. New York, NY: Simon & Schuster.
von Neumann, J. (1966). Theory of Self-Reproducing Automata. Urbana, IL: University of Illinois Press.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893