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Herbert Simon's classic work on artificial intelligence in the
expanded and updated third edition from 1996, with a new
introduction by John E. Laird. Herbert Simon's classic and
influential The Sciences of the Artificial declares definitively
that there can be a science not only of natural phenomena but also
of what is artificial. Exploring the commonalities of artificial
systems, including economic systems, the business firm, artificial
intelligence, complex engineering projects, and social plans, Simon
argues that designed systems are a valid field of study, and he
proposes a science of design. For this third edition, originally
published in 1996, Simon added new material that takes into account
advances in cognitive psychology and the science of design while
confirming and extending the book's basic thesis: that a physical
symbol system has the necessary and sufficient means for
intelligent action. Simon won the Nobel Prize for Economics in 1978
for his research into the decision-making process within economic
organizations and the Turing Award (considered by some the computer
science equivalent to the Nobel) with Allen Newell in 1975 for
contributions to artificial intelligence, the psychology of human
cognition, and list processing. The Sciences of the Artificial
distills the essence of Simon's thought accessibly and coherently.
This reissue of the third edition makes a pioneering work available
to a new audience.
Experts from a range of disciplines explore how humans and
artificial agents can quickly learn completely new tasks through
natural interactions with each other. Humans are not limited to a
fixed set of innate or preprogrammed tasks. We learn quickly
through language and other forms of natural interaction, and we
improve our performance and teach others what we have learned.
Understanding the mechanisms that underlie the acquisition of new
tasks through natural interaction is an ongoing challenge. Advances
in artificial intelligence, cognitive science, and robotics are
leading us to future systems with human-like capabilities. A huge
gap exists, however, between the highly specialized niche
capabilities of current machine learning systems and the
generality, flexibility, and in situ robustness of human
instruction and learning. Drawing on expertise from multiple
disciplines, this Strungmann Forum Report explores how humans and
artificial agents can quickly learn completely new tasks through
natural interactions with each other. The contributors consider
functional knowledge requirements, the ontology of interactive task
learning, and the representation of task knowledge at multiple
levels of abstraction. They explore natural forms of interactions
among humans as well as the use of interaction to teach robots and
software agents new tasks in complex, dynamic environments. They
discuss research challenges and opportunities, including ethical
considerations, and make proposals to further understanding of
interactive task learning and create new capabilities in assistive
robotics, healthcare, education, training, and gaming. Contributors
Tony Belpaeme, Katrien Beuls, Maya Cakmak, Joyce Y. Chai, Franklin
Chang, Ropafadzo Denga, Marc Destefano, Mark d'Inverno, Kenneth D.
Forbus, Simon Garrod, Kevin A. Gluck, Wayne D. Gray, James Kirk,
Kenneth R. Koedinger, Parisa Kordjamshidi, John E. Laird, Christian
Lebiere, Stephen C. Levinson, Elena Lieven, John K. Lindstedt,
Aaron Mininger, Tom Mitchell, Shiwali Mohan, Ana Paiva, Katerina
Pastra, Peter Pirolli, Roussell Rahman, Charles Rich, Katharina J.
Rohlfing, Paul S. Rosenbloom, Nele Russwinkel, Dario D. Salvucci,
Matthew-Donald D. Sangster, Matthias Scheutz, Julie A. Shah,
Candace L. Sidner, Catherine Sibert, Michael Spranger, Luc Steels,
Suzanne Stevenson, Terrence C. Stewart, Arthur Still, Andrea
Stocco, Niels Taatgen, Andrea L. Thomaz, J. Gregory Trafton, Han L.
J. van der Maas, Paul Van Eecke, Kurt VanLehn, Anna-Lisa Vollmer,
Janet Wiles, Robert E. Wray III, Matthew Yee-King
The definitive presentation of Soar, one AI's most enduring
architectures, offering comprehensive descriptions of fundamental
aspects and new components. In development for thirty years, Soar
is a general cognitive architecture that integrates
knowledge-intensive reasoning, reactive execution, hierarchical
reasoning, planning, and learning from experience, with the goal of
creating a general computational system that has the same cognitive
abilities as humans. In contrast, most AI systems are designed to
solve only one type of problem, such as playing chess, searching
the Internet, or scheduling aircraft departures. Soar is both a
software system for agent development and a theory of what
computational structures are necessary to support human-level
agents. Over the years, both software system and theory have
evolved. This book offers the definitive presentation of Soar from
theoretical and practical perspectives, providing comprehensive
descriptions of fundamental aspects and new components. The current
version of Soar features major extensions, adding reinforcement
learning, semantic memory, episodic memory, mental imagery, and an
appraisal-based model of emotion. This book describes details of
Soar's component memories and processes and offers demonstrations
of individual components, components working in combination, and
real-world applications. Beyond these functional considerations,
the book also proposes requirements for general cognitive
architectures and explicitly evaluates how well Soar meets those
requirements.
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