1. Soar: An Architecture for Human Behavior Representation
Randall W. Hill, Jr.
Information Sciences Institute
University of Southern California

2. What is Soar? Artificial Intelligence Architecture
System for building intelligent agents
Learning system
Cognitive Architecture
A candidate Unified Theory of Cognition (Allen Newell, 1990)

3. History Inventors Officially created in 1983
Allen Newell, John Laird, Paul Rosenbloom
Officially created in 1983
Roots in 1950’s and onwards
Currently on version 8 of Soar architecture
Written in ANSI C for portability and speed
In the public domain

4. User Community Academia International Commercial
USC, U. of Michigan, CMU, BYU, others
International
Britain, Europe, Japan
Commercial
Soar Technology, Inc.
ExpLore Reasoning Systems, Inc.

5. Objectives of Architecture
Support multi-method problem solving
Apply to a wide variety of tasks and methods
Combine reactive and goal directed symbolic processing
Represent and use multiple knowledge forms
Procedural, declarative, episodic, iconic
Support very large bodies of knowledge (>100,000 rules)
Interact with the outside world
Learn about all aspects of tasks
Full Range of Tasks: NL processing and generation. Design.
Multiple Knowledge forms
Full range of problem solving methods: means-ends analysis (MEA), planning (HTN/PO),
Interact with outside world: sensors e.g., helicopter pilots can send/receive messages, sense other entities and the terrain, and sense and control the helicopter and weapon systems. Quake agents.

6. Cognitive Behavior: Underlying Assumptions
Goal-oriented
Reactive
Requires use of symbols
Problem space hypothesis
Requires learning
Symbolic Computational System -- Manipulates and evaluates symbols via other symbol structures. That computations can be performed on symbol structures rather than on numbers was a key insight in AI in 1950’s.
Problem Spaces -- Insight from early AI: use heuristic search spaces to deal with difficult tasks. Tasks are formulated as search in a space of states by means of operators that produce new states.
Production System -- Long-term memory for both program and data consists of parallel acting condition-action rules. Flexible, intelligent action requires that data in working memory call forth the knowledge in long-term memory about what to do -- recognize-act cycle.
Goal Hierarchy -- Intelligent activity is driven by difficulties. Subgoals are set up as ways of thwarting small difficulties. This principle applies in studies on human problem solving and in AI.
Chunking -- Based on George Miller’s paper on chunking. Soar learns continuously by building new productions to capture knowledge that Soar generated in process of resolving difficulties (impasses).
Soar is an accumulation and integration of results achieved over last 40 years.

7. Soar Architecture Perception / Motor Interface Long Term Knowledge
e.g., Doctrine, Tactics, Flying Techniques,
Missions, Coordination,
Properties of Planes, Weapons, Sensors, …
[ ]
[ ]
[ ]
[ ]
[ ]
[ ]
Match
Changes
Working Memory
situational assessment, intermediate results, actions, goals, …
Perception / Motor Interface

8. Soar Decision Cycle Elaboration Phase Input Phase Output Phase
Perception
Cognition
Motor
Elaboration Phase
Fire rules
Generate preferences
Update working memory
Input Phase
Output Phase
Sense world
Perceptual pre-processing
Assert to WM
Decision Phase
Command effectors
Adjust perception
Evaluate operator preferences
Select new operator OR
Create new state

9. Which Rule(s) Should Fire?
Fire all matched rules in parallel until quiescence
Sequential operators generate behavior
e.g., Turn, adjust-radar, select-missile, climb
Provides trace of behavior comparable to human actions
Rules select, apply, terminate operators.
Select: create preferences to propose and compare operators
Apply: modify the current situation, send motor commands
Terminate: determine that operator is finished
Elaboration
(propose operators)
Decide
(select operator)
Elaboration
(apply operator)
Elaboration
(terminate operator & propose)
Output
Decide
Output
Input
Input
Decide
Input

10. Example Rules
PROPOSE: If I encounter the enemy, propose an operator to break contact with the enemy.
SELECT: If I am enroute to my holding area and I come into contact with an enemy unit, prefer breaking contact over engaging targets.
APPLY: If the enemy is to my left, break to the right.
APPLY: If the enemy is to my right, break to the left.
TERMINATE: If break contact is the current operator, and contact is broken, then terminate break operator.

11. Goal Driven Behavior Complex operators are decomposed to simpler ones
Occurs whenever rules are insufficient to apply operator
Decomposition is dynamic and situation dependent
Over 90 operators in RWA-Soar
Execute-Mission
Fly-Flight-Plan
Engage
Prepare-to-
return-to-base
Fly-control-route
Select-
point
route
High-
level
Low-
Contour
NOE
Mask
Unmask
Employ-
weapons
Initialize-
hover
Return-
to-
control-

12. Coordination of Behavior & Action
Combines goal-driven and reactive behaviors
Suggest new operators anywhere in goal hierarchy
Generate preferences for operators
Terminate operators
Provides limited multi-task capability
Constrained by single goal hierarchy in Soar
Other possible approaches
Multiple goal hierarchies
Flush and re-build goal hierarchies when needed

13. Modeling Perceptual Attention
Problem
Naïve vision model
Entity-level resolution
Unrealistic field of view (360o, 7 km)
No focus of attention
Perceptual overload often occurs
Pilot crashes helicopter
Approach
Zoom lens model of attention
Gestalt grouping in pre-attentive stage
Multi-resolution focus
Control of attention
Goal-driven: task-based, group-oriented
Stimulus-driven: abrupt onset, contrast

14. Naïve Vision Model Model of Attention Entity-oriented Stimulus-driven
No perceptual control
Model of Attention
Gestalt grouping
Zoom lens effect
Goal and stimulus driven

15. Underlying Technologies/Algorithms
Optimized RETE algorithm
Enables efficient matching in large rule sets
Universal subgoaling
Operator-based architecture
Truth Maintenance System (TMS)
Learning algorithm
Chunking mechanism

16. Soar Applications Agents for Synthetic Battlespaces
Commanders and Helicopter Pilots (USC)
Fixed Wing Aircraft Pilots (UM, Soar Technology)
Animated, Pedagogical Agents
Steve (Rickel and Johnson, USC)
Game Agents
Quake (Laird and van Lent, UM)
Animated Pedagogical Agents - Center for Advanced Research in Technology for Education - CARTE

17. Intelligent Synthetic Forces
Helicopter pilots
Teamwork
C3I Modeling
Planning
Execution
Re-planning
Collaboration

18. Steve: An Embodied Intelligent Agent for Virtual Environments
3D agent that interacts with students in virtual environments
Can take different roles: teammate, teacher, guide, demonstrator
Multiple trainees and agents work together in virtual teams
Intelligent tutoring in the context of a shared team environment

19. Soar/Games Project U. of Michigan, Laird and van Lent
Build an AI Engine around the Soar AI architecture
Soar/Quake II project
Soar/Descent 3 project
U. of Michigan, Laird and van Lent
Soar/Quake AI
Socket
DLL = Dynamically Loadable Library - Quake II has a API published and you
use a DLL that it automatically loads to connect to it.
Interface
DLL
Sensor Data
AI Engine (Soar)
Knowledge Files
Actions

20. Validation Efforts Intelligent Synthetic Forces
Synthetic Theater of War ‘97 experience
Subject Matter Experts
Human Factors / HCI studies
e.g., B. John (CMU) & R. Young (U.K.)
Better models for validating integrated models of human behavior needed

21. Summary of Capabilities/Limitations
Mixes goal-oriented and reactive behavior
Supports interaction with external world
Architecture lends itself to creating integrated models of human behavior
Limitations
Learning mechanism not easily used

22. Future Development / Application Plans
Integrate emotion with cognition
Learn from experience
Incorporate inductive models of learning
Continue work on models of collaboration in complex decision-making
Extend the current C3I models