The Soar Cognitive Architecture
by John E. Laird /
2012 / English / PDF
12.3 MB Download
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.
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.