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Knowing our World: An Artificial Intelligence Perspective (Hardcover, 1st ed. 2021)
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Knowing our World: An Artificial Intelligence Perspective (Hardcover, 1st ed. 2021)
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Knowing our World: An Artificial Intelligence Perspective considers
the methodologies of science, computation, and artificial
intelligence to explore how we humans come to understand and
operate in our world. While humankind's history of articulating
ideas and building machines that can replicate the activity of the
human brain is impressive, Professor Luger focuses on understanding
the skills that enable these goals. Based on insights afforded by
the challenges of AI design and program building, Knowing our World
proposes a foundation for the science of epistemology. Taking an
interdisciplinary perspective, the book demonstrates that AI
technology offers many representational structures and reasoning
strategies that support clarification of these epistemic
foundations. This monograph is organized in three Parts; the first
three chapters introduce the reader to the foundations of computing
and the philosophical background that supports the AI tradition.
These three chapters describe the origins of AI, programming as
iterative refinement, and the representations and very high-level
language tools that support AI application building. The book's
second Part introduces three of the four paradigms that represent
research and development in AI over the past seventy years: the
symbol-based, connectionist, and complex adaptive systems. Luger
presents several introductory programs in each area and
demonstrates their use. The final three chapters present the
primary theme of the book: bringing together the rationalist,
empiricist, and pragmatist philosophical traditions in the context
of a Bayesian world view. Luger describes Bayes' theorem with a
simple proof to demonstrate epistemic insights. He describes
research in model building and refinement and several philosophical
issues that constrain the future growth of AI. The book concludes
with his proposal of the epistemic stance of an active, pragmatic,
model-revising realism.
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