Soar (cognitive architecture)
Soar[1] is a cognitive architecture,[2] originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.
The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of cognitive capabilities found in humans, such as decision making, problem solving, planning, and natural-language understanding.
Since its beginnings in 1983 as John Laird’s thesis, it has been widely used by AI researchers to create intelligent agents and cognitive models of different aspects of human behavior.
[2] and dates back to one of the first AI systems created, Newell, Simon, and Shaw's Logic Theorist, first presented in 1955 and as the General Problem Solver in 1957.
At each step, a single operator is selected, and then applied to the agent’s current state, which can lead to internal changes, such as retrieval of knowledge from long-term memory or modifications or external actions in the world.
A third hypothesis is that if the knowledge to select or apply an operator is incomplete or uncertain, an impasse arises and the architecture automatically creates a substate.
In the substate, the same process of problem solving is recursively used, but with the goal to retrieve or discover knowledge so that decision making can continue.
This can lead to a stack of substates, where traditional problem methods, such as planning or hierarchical task decomposition, naturally arise.
More complex behavior arises automatically when knowledge is incomplete or uncertain, through a third level of processing using substates, roughly corresponding to System 2.
Soar supports reinforcement learning, which tunes the values of rules that create numeric preferences for evaluating operators, based on reward.
Substates provide a means for on-demand complex reasoning, including hierarchical task decomposition, planning, and access to the declarative long-term memories.
In the future, the learned rules automatically fire in similar situations so that no impasse arises, incrementally converting complex reasoning into automatic/reactive processing.
[3] Symbolic input and output occurs through working memory structures attached to the top state called the input-link and the output-link.
SVS internally represents the world as a scene graph, a collection of objects and component subobjects each with spatial properties such as shape, location, pose, relative position, and scale.
A Soar agent using SVS can create filters to automatically extract features and relations from its scene graph, which are then added to working memory.
Each of Soar’s long-term memories have associated online learning mechanisms that create new structures or modify metadata based on an agent’s experience.
The process of agent development is explained in detail in the official Soar manual as well as in several tutorials which are provided at the research group's website.
The Soar architecture is currently maintained and extended by the Center for Integrated Cognition (CIC), a research group led by John Laird and Robert Wray.
JSoar closely follows the University of Michigan architecture implementation, although it generally does not reflect the latest developments and changes of that C/C++ version.
[6] The first large-scale application of Soar was R1-Soar, a partial reimplementation by Paul Rosenbloom of the R1 (XCON) expert system John McDermott developed for configuring DEC computers.
[7] NL-Soar was a natural-language understanding system developed in Soar by Jill Fain Lehman, Rick Lewis, Nancy Green, Deryle Lonsdale and Greg Nelson.
Some of the capabilities incorporated in TacAir-Soar and RWA-Soar were attention, situational awareness and adaptation, real-time planning and dynamic replanning, and complex communication, coordination, and cooperation among combinations of Soar agents and humans.
A mobile application for the game Liar’s Dice has been developed for iOS which runs the Soar architecture directly from the phone as the engine for opponent AIs.
