What is Soar?

Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior. Researchers all over the world, both from the fields of artificial intelligence and cognitive science, are using Soar for a variety of tasks. It has been in use since 1983, evolving through many different versions to where it is now Soar, Version 9.

We intend ultimately to enable the Soar architecture to:

• 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, semantic, episodic, and iconic
• employ the full range of problem solving methods
• interact with the outside world, and
• learn about all aspects of the tasks and its performance on them.

In other words, our intention is for Soar to support all the capabilities required of a general intelligent agent.

The ultimate in intelligence would be complete rationality which would imply the ability to use all available knowledge for every task that the system encounters. Unfortunately, the complexity of retrieving relevant knowledge puts this goal out of reach as the body of knowledge increases, the tasks are made more diverse, and the requirements in system response time more stringent. The best that can be obtained currently is an approximation of complete rationality. The design of Soar can be seen as an investigation of one such approximation. Below is the primary principle which is the basis of Soar's design and which guides its attempt to approximate rational behavior.

• 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, the contents of working memory created by prior problem solving, and any relevant knowledge retrieved from long-term memory. Decisions are never precompiled into uninterruptible sequences.

For many years, a secondary principle has been that the number of distinct architectural mechanisms should be minimized. Through Soar 8, there has been a single framework for all tasks and subtasks (problem spaces), a single representation of permanent knowledge (productions), 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). We have revisited this assumption as we attempt to ensure that all available knowledge can be captured at runtime without disrupting task performance. This is leading to multiple learning mechanisms (chunking, reinforcement learning, episodic learning, and semantic learning), and multiple representations of long-term knowledge (productions for procedural knowledge, semantic memory, and episodic memory).

Two additional principles that guide the design of Soar are functionality and performance. Functionality involves ensuring that Soar has all of the primitive capabilities necessary to realize the complete suite of cognitive capabilities used by humans, including, but not limited to reactive decision making, situational awareness, deliberate reasoning and comprehension, planning, and all forms of learning. Performance involves ensuring that there are computationally efficient algorithms for performing the primitive operations in Soar, from retrieving knowledge from long-term memories, to making decisions, to acquiring and storing new knowledge.

For further background on Soar, we recommend The Soar Cognitive Architecture Laird, J. E.(2012), The Soar Papers: Readings on Integrated Intelligence, Rosenbloom, Laird, and Newell (1993), A Gentle Introduction to Soar: 2006 update, and Unified Theories of Cognition, Newell (1990). Also available are Soar: A Functional Approach to General Intelligence and Soar: A comparison with Rule-Based Systems. A full list of publications is available on the Soar publications page. Entries on the Soar Knowledge Base and the older Soar FAQ also provide answers to many common questions about Soar.

We would like to extend a special thank you to DARPA, ONR and AFOSR for their continued support of Soar and projects related to Soar.