Download Diagrammatic Reasoning in AI by Robbie T. Nakatsu PDF

By Robbie T. Nakatsu

Pioneering paintings indicates how utilizing Diagrams allows the layout of higher AI platforms

The booklet of Diagrammatic Reasoning in AI marks a tremendous milestone for an individual looking to layout graphical consumer interfaces to aid decision-making and problem-solving initiatives. the writer expertly demonstrates how diagrammatic representations can simplify our interplay with more and more advanced details applied sciences and computer-based info platforms. particularly, the e-book emphasizes how diagrammatic person interfaces will help us greater comprehend and visualize man made intelligence (AI) platforms. It examines how diagrammatic reasoning complements a variety of AI programming recommendations used to emulate human pondering and problem-solving, together with:

  • Expert platforms
  • Model-based reasoning
  • Inexact reasoning comparable to simple task elements and Bayesian networks
  • Logic reasoning

A key a part of the e-book is its vast improvement of functions and graphical illustrations, drawing on such fields because the actual sciences, macroeconomics, finance, enterprise logistics administration, and drugs. regardless of such super range of utilization, by way of purposes and diagramming notations, the e-book classifies and organizes diagrams round six significant issues: method topology; series and circulation; hierarchy and type; organization; reason and impact; and common sense reasoning. Readers will enjoy the author's dialogue of ways diagrams might be greater than only a static photograph or illustration and the way diagrams could be a crucial a part of an clever person interface, intended to be manipulated and changed, and from time to time, applied to deduce suggestions to tricky difficulties.

This e-book is perfect for lots of varieties of readers: practitioners and researchers in AI and human-computer interplay; enterprise and computing execs; photograph designers and architects of graphical consumer interfaces; and with reference to someone drawn to figuring out the facility of diagrams. via gaining knowledge of the various varieties of diagrams and their purposes in AI, all readers will achieve a deeper appreciation of diagrammatic reasoning.

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Extra info for Diagrammatic Reasoning in AI

Example text

One approach would be to create a database of questions and their corresponding answers, a kind of FAQs (frequently asked questions) for the situation at hand. A problem with this approach is that a system designer would need to anticipate all questions and their responses beforehand. Yet it is highly unlikely that all the relevant questions will be thought out ahead of time. Furthermore, the responses to the questions would be canned text, or prefabricated responses, which would ignore context and contain no model of system behavior.

Second, they recognize the limitations of AI, but nonetheless manage to work around them. Their successes are highly attributable to having a well-grounded understanding of the practical issues involved in implementing AI programs and machines. A central tenet of this book is that the designers of the next generation of AI programs will require a good grasp of the difficulties of AI to find work-around solutions. In this chapter, we have discussed two frameworks for dealing with the difficulties of intelligent systems.

One important theme that emerges is that a mental model can organize knowledge in some meaningful way. As many of the studies have demonstrated, without a meaningful representation of knowledge, the human mental processing system would be incapable of making sense of a complex system. An effective organization of knowledge allows one to comprehend complexity and to more effectively use a system in novel ways. In this section, we focus on how mental models organize knowledge in a complex domain.

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