The Soar Cognitive Architecture

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.

[1]  Slawomir J. Nasuto,et al.  Communicating neurons: A connectionist spiking neuron implementation of stochastic diffusion search , 2009, Neurocomputing.

[2]  Jill P. Mesirov,et al.  Protein structure prediction by a data-level parallel algorithm , 1989, Proceedings of the 1989 ACM/IEEE Conference on Supercomputing (Supercomputing '89).

[3]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[4]  C. D. Perttunen,et al.  Lipschitzian optimization without the Lipschitz constant , 1993 .

[5]  John E. Laird,et al.  Using diverse cognitive mechanisms for action modeling , 2010 .

[6]  Jordan B. Pollack,et al.  Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretation , 1988, Cogn. Sci..

[7]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[8]  M. Bishop,et al.  Views into the Chinese Room: New Essays on Searle and Artificial Intelligence , 2002 .

[9]  J. Bishop.  Stochastic searching networks , 1989 .

[10]  David L. Waltz,et al.  An English language question answering system for a large relational database , 1978, CACM.

[11]  Craig A. Smith,et al.  Appraisal theory: Overview, assumptions, varieties, controversies. , 2001 .

[12]  Mohammad Majid al-Rifaie,et al.  Resource Allocation and Dispensation Impact of Stochastic Diffusion Search on Differential Evolution Algorithm , 2011, NICSO.

[13]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[14]  K. Scherer.  Appraisal considered as a process of multilevel sequential checking. , 2001 .

[15]  Mohammad Majid al-Rifaie,et al.  An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation , 2011 .

[16]  Pat Langley,et al.  Cognitive architectures: Research issues and challenges , 2009, Cognitive Systems Research.

[17]  J. Mark Bishop,et al.  A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism , 2009, Cognitive Computation.

[18]  John E. Laird,et al.  A Multi-Domain Evaluation of Scaling in a General Episodic Memory , 2012, AAAI.

[19]  Richard Reviewer-Granger.  Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[20]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[21]  David L. Waltz,et al.  Understanding Scenes With Shadows , 1971 .

[22]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[23]  Richard L. Lewis,et al.  A computational unification of cognitive behavior and emotion , 2009, Cognitive Systems Research.