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What is Model-Based Reasoning

Chapter 146
Encyclopedia of Artificial Intelligence
The knowledge base comprises a model of the problem area, constructed from component parts. The inference engine reasons about the real world by exploring behaviors of the model.
Published in Chapter:
Knowledge-Based Systems
Adrian A. Hopgood (De Montfort University, UK)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch146
Abstract
The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve (Hopgood, 2003; Hopgood, 2005).
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Chapter 17
AI-Augmented Developmental Instruction for Improving Contemplative Practices in the Face of Complexity
The history of model-based reasoning (MBR) started with the creation of inferences in artificial intelligence (AI) to represent and model systems and their expected behavior in the physical world. Reasoning processes, making use of the inferences, involved comparison of the model system with observed data to gauge the degree of efficacy and accuracy of employed AI tools offered in intelligent decision- and performance-support systems. Over the years, MBR techniques employed in AI tools have been expanded for use in learning simulations to model a physical system, represent expertise usage of the system, with means to employ tutoring assistance for reasoning activities by learners to acquire or deepen understanding of the system and usage on the basis of how well the fit is between the learner and expert understanding levels and usage of the system. MBR, employed within immersive 3D simulations, can be designed to support usage of zone-of-proximal development (ZPD) to help guide the level and timing of tutoring offered to a learner on the basis of gaps revealed regarding system-model understanding and usage in context of a challenge.
Published in Chapter: AI-Augmented Developmental Instruction for Improving Contemplative Practices in the Face of Complexity; From: Recent Advances in Applying Identity and Society Awareness to Virtual Learning
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