AI and simulation: What can they learn from each otherSimulation and Artificial Intelligence share a fertile common ground both from a practical and from a conceptual point of view. Strengths and weaknesses of both Knowledge Based System and Modeling and Simulation are examined and three types of systems that combine the strengths of both technologies are discussed. These types of systems are a practical starting point, however, the real strengths of both technologies will be exploited only when they are combined in a common knowledge representation paradigm. From an even deeper conceptual point of view, one might even argue that the ability to reason from a set of facts (i.e., Expert System) is less representative of human reasoning than the ability to make a model of the world, change it as required, and derive conclusions about the expected behavior of world entities. This is a fundamental problem in AI, and Modeling Theory can contribute to its solution. The application of Knowledge Engineering technology to a Distributed Processing Network Simulator (DPNS) is discussed.
Document ID
19880020007
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Colombano, Silvano P. (RECOM Software, Inc. San Jose, CA, United States)
Date Acquired
September 5, 2013
Publication Date
August 1, 1988
Publication Information
Publication: NASA, Marshall Space Flight Center, Second Conference on Artificial Intelligence for Space Applications