Knowing our World: An Artificial Intelligence Perspective

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 supportsthe 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.

Explores the nature of knowledge, meaning, and truth through reviewing the history of science and the human creativity required to produce computer programs that support intelligent behavior. This quest is within the domain of epistemology Considers the intersection of epistemology and building programs that produce intelligent results Of interest to readers seeking to integrate the foundations of artificial intelligence, cognitive science, and philosophy

Autorentext

George Luger is Professor Emeritus of Computer Science at the University of New Mexico. Dr. Luger was also a Professor in the Psychology and Linguistics Departments, reflecting his interdisciplinary interests in Cognitive Science and Computational Linguistics.

The National Science Foundation, NATO, the British Royal Society, NASA, the Smithsonian Institution, NIH, the Departments of Defense, Energy and Transportation, NIH, and other government agencies have supported George Luger's research. He has worked with the Los Alamos and Sandia National Laboratories and for numerous companies. Currently, his consulting is in the design of natural language web agents and deep learning technologies that analyze information in very large collections of data.

Dr. Luger is the author of Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Addison-Wesley 2009), now in its Sixth Edition, and Cognitive Science: The Science of Intelligent Systems (Academic Press, 1994).

Klappentext

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 supportsthe 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.

Inhalt

PART I, In the Beginning.-1 Creating Computer Programs: An Epistemic Commitment.- 2 Historical Foundations.- 3 Modern AI and How We Got Here.- PART II, AI: Structures and Strategies for Complex Problem Solving.- 4 Symbol-Based AI and its Rationalist Presuppositions.- 5 Association and Connectionist Approaches to AI.- 6 Evolutionary Computation and Intelligence.- PART III, On Epistemology: Towards an Active, Pragmatic, Model-Revising Realism.- 7 A Constructivist Rapprochement and an Epistemic Stance.- 8 Bayesian-Based Constructivist Computational Models.- 9 Towards an Active, Pragmatic, Model-Revising Realism.-Bibliography.- Index.