🚧 Under Construction 🚧¶
The HTML version of the manual is currently under construction. If you find it difficult to read, there is a PDF version available here.
Introduction¶
Soar has been developed to be an architecture for constructing general intelligent systems. It has been in use since 1983, and has evolved through many different versions. This manual documents the most current of these: version 9.6.1.
Our goals for Soar include that it ultimately be an architecture that can:
- be used to build systems that work on the full range of tasks expected of an intelligent agent, from highly routine to extremely difficult, open-ended problems;
- represent and use appropriate forms of knowledge, such as procedural, declarative, episodic, and possibly iconic;
- employ the full range of possible problem solving methods;
- interact with the outside world; and
- learn about all aspects of the tasks and its performance on those tasks.
In other words, our intention is for Soar to support all the capabilities required of a general intelligent agent. Below are the major principles that are the cornerstones of Soar’s design:
- The number of distinct architectural mechanisms should be minimized. Classically Soar had only a single representation of permanent knowledge (production rules), a single representation of temporary knowledge (objects with attributes and values), a single mechanism for generating goals (automatic subgoaling), and a single learning mechanism (chunking). It was only as Soar was applied to diverse tasks in complex environments that we found these mechanisms to be insufficient and added new long- term memories (semantic and episodic) and learning mechanisms (semantic, episodic, and reinforcement learning) to extend Soar agents with crucial new functionalities.
- All decisions are made through the combination of relevant knowledge at run-time. In Soar, every decision is based on the current interpretation of sensory data and any relevant knowledge retrieved from permanent memory. Decisions are never precompiled into uninterruptible sequences.
Using this Manual¶
We expect that novice Soar users will read the manual in the order it is presented. Not all users will makes use of the mechanisms described in chapters 4-8, but it is important to know that these capabilities exist.
Chapter 2 and Chapter 3 describe Soar from different perspectives: Chapter 2 describes the Soar architecture, but avoids issues of syntax, while Chapter 3 describes the syntax of Soar, including the specific conditions and actions allowed in Soar productions.
Chapter 4 describes chunking, Soar’s mechanism to learn new procedural knowledge (productions).
Chapter 5 describes reinforcement learning (RL), a mechanism by which Soar’s procedural knowledge is tuned given task experience.
Chapter 6 and Chapter 7 describe Soar’s long-term declarative memory systems, semantic and episodic.
Chapter 8 describes the Spatial Visual System (SVS), a mechanism by which Soar can convert complex perceptual input into practical semantic knowledge.
Chapter 9 describes the Soar user interface — how the user interacts with Soar. The chapter is a catalog of user-interface commands, grouped by functionality. The most accurate and up-to-date information on the syntax of the Soar User Interface is found online, at the Soar web site, at https://github.com/SoarGroup/Soar/wiki/CommandIndex.
Advanced users will refer most often to Chapter 9, flipping back to Chapters 2 and 3 to answer specific questions.
Chapters 2 and 3 make use of a Blocks World example agent. The Soar code for this agent can be downloaded at https://web.eecs.umich.edu/~soar/blocksworld.soar or found here.
Additional Back Matter¶
After these chapters is an index; the last pages of this manual contain a summary and index of the user-interface functions for quick reference.
Not Described in This Manual¶
Some of the more advanced features of Soar are not described in this manual, such as how to interface with a simulator, or how to create Soar applications using multiple interacting agents. The Soar project website (see link below) has additional help documents and resources.
For novice Soar users, try The Soar 9 Tutorial, which guides the reader through several example tasks and exercises.
Contacting the Soar Group¶
The primary website for Soar is:
Look here for the latest Soar-related downloads, documentation, FAQs, and announcements, as well as links to information about specific Soar research projects and researchers.
Soar kernel development is hosted on GitHub at
This site contains the public GitHub repository, a wiki describing the command-line interface, and an issue tracker where users can report bugs or suggests features.
To contact the Soar group or get help, or to receive notifications of significant developments in Soar, we recommend that you register with one or both of our email lists:
For questions about using Soar, you can use the soar-help list. For other discussion or to receive announcements, use the soar-group list.
Also, please do not hesitate to file bugs on our issue tracker:
https://github.com/SoarGroup/Soar/issues
To avoid redundant entries, please search for duplicate issues first.
For Those Without Internet Access¶
Mailing Address:
The Soar Group
Artificial Intelligence Laboratory
University of Michigan
2260 Hayward Street
Ann Arbor, MI 48109-2121
USA
Different Platforms and Operating Systems¶
Soar runs on a wide variety of platforms, including Linux, Unix (although not heavily tested), Mac OS X, and Windows 10, 7, possibly 8 and Vista, XP, 2000 and NT). We currently test Soar on both 32-bit and 64-bit versions of Ubuntu Linux, OS X 10, and Windows 10.
This manual documents Soar generally, although all references to files and directories use Unix format conventions rather than Windows-style folders.