1. SOAR
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2. The Basics SOAR is a theory of human cognition connectionist approach
Allen Newell, one of the founders of modern cognitive science and artificial intelligence.
Newell’s definition of intelligence
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3. Introduction Architecture by itself does nothing.
BEHAVIOR = ARCHITECTURE X CONTENT
A cognitive architecture must help produce cognitive behavior. Soar is a theory of what cognitive behaviors have in common.
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5. A Professional Baseball Team
Each position on a team could be a representation of an agent.
Each agent has the over all goal of winning the game.
Each has sub-goals of being successful at his position.
SOAR could be used to represent this team as a software model.
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6. Agents Make Soar Unique
Agents are software models of real world objects. They are objects that react to and produce intelligent behavior.
Dynamic
Imperfect knowledge
Prioritize the actions/ decisions
Computational limitations
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7. Agent Capabilities Perception- sensing the environment
Acton- respond to the environment
Planning- map out and decide what they will do before they do it.
Learning- incorporate from their environment
Cooperation & Coordination- able to cooperate and coordinate with other agents using Natural Language Capabilities.
Meta-Reasoning- ability to learn rationally. They can learn why.
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8. A Scene from out Game:
Agent John Doe is a Pitcher throwing his first pitch of his major league career.
He chooses to throw a curve ball.
The batter Joe Schmoe, hits the ball, but John is able to catch it after it bounces between home plate and the pitching mound.
John quickly throws the batter out at first base.
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9. Architecture: Goal Driven
The Goal Context is the heart of the architecture.
Defined by four slots and their values:
The goal: The motivation, direction
The problem space: organization
The State: an internal representation
of the situation.
The operator: the means to get from point a to point b.
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10. Joe’s Goals and the State
Joe’s Ultimate goal is to win the game along with his team
His immediate goal is to get the batter out.
His operators are the types of pitches that he can throw
The state space includes the batter, the runners on base, and the current count etc.
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11. Memory
Productions are memory structures, represented often as if-then statements
Constantly being matched
Lowest level of memory
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12. Productions
There are several features that make them models of human memory:
They are associational in nature.
They are independent of domain which allows for continuous incremental learning
They are dynamic and cognitively impenetrable.
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13. Architecture: LTM
map from the current goal context in WM to a new goal context.
The mapping is from context to context, triggers an association
LTM is what is true in general:
If John throws three strikes in a row then the batter is out.
If John throws four balls in a row then the batter walks to first.
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14. Architecture: Working Memory
WM arise in one of two ways: external perception or associations
holds the results of perception as values in current state.
four kinds of objects: goals, problems spaces, states, and operators.
WM is what the model thinks is true in a particular situation:
John’s working memory tells him that he is about to pitch to Joe Schmoe and that he has the goal of getting Joe out. His choices and the current state are all part of the working memory. This changes with the batter and the status of the game.
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15. Decision Cycle Moving from the general to the specific.
the processing component that generates behavior out of the content that resides in the LTM and WM
recognize-decide-act cycle
Two Phases: Elaboration and Decision
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16. Architecture: The Decision Cycle I, Elaboration phase
ALL productions that match the current state fire, producing new content in the working memory.
For example if the batter Joe was determined to be left handed then a new associations would be made.
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17. Architecture:The Decision Cycle II, Decision phase
Decision procedure interprets and suggests changes to the context.
The result is either a single change, or an impasse
There a limit on how much cognition can do at once.
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18. Example Continued
After the elaboration phase it is determined that John should either throw a curve ball or throw a fast ball based on all of the knowledge.
Based on the possibilities the decision phase determines that there is not enough information to make a decision. There is no basis for a preference, an impasse has been reached.
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19. Architecture: Impasses
There is an opportunity for learning.
An impasse occurs automatically whenever there isn’t enough knowledge.
Independent of any domain.
Automatically begins the creation of a new sub-goal context whose goal is to resolve the impasse.
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20. Example Opportunity for learning. Past Success Rate?
Joe recalls that in the past he is more successful with the curve ball.
Throws a curve ball. Unfortunately, the ball is hit and John recovers the ball and throws Joe out at first.
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21. Architecture: Chunking I
The primary learning mechanism.
Automatically creates new associations in LTM whenever results are generated from an impasse.
The new associations map relevant pre-impasse WM elements changes that prevent that impasse in future
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22. Architecture: Chunking II
Chunking serves many purposes
integrate different types of knowledge
speed up behavior
basis of inductive learning, analogical reasoning, etc.
only architectural mechanism for changing LTM, it is assumed to be the basis of all types of learning in people.
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23. Example
Take the information, weather, time of day, batter, field conditions, count etc.
Therefore the next time John may consider his success rate and other factors to avoid the impasse in the future.
Produces preferences, a preference added that states if it is windy then throw less fast balls.
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24. What Makes Soar Different
ALL knowledge that is relevant is activated rather than just matching rules and activating them
The Beliefs about the world are constantly being updated automatically. Not maintained by other rules
Agents use Preferences. The agent can express knowledge about which option it prefers in the current situation.
Create new and multiple states. RB systems have only one state
Generates new knowledge and allows the agent to avoid it in the future.
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25. Real World Applications
There have been several theories developed from the original SOAR architecture:
NL-Soar – natural language comprehension
SCA - theory of symbolic concept
NTD-Soar - a computational theory of perceptual, cognitive and motor actions performed by the NASA Test Director (NTD)
IMPROV - a computational theory of how to correct knowledge about what actions do in the world.
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26. Commercial Applications
Soar Technologies, Inc. The goal of Soar Technology, Inc. is to increase the realism of battle simulations by developing intelligent automated synthetic forces.
ExpLore Reasoning Systems, Inc. ERS builds intelligent software solutions for the mutual-fund, mortgage, credit-card, and insurance industries.
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27. Problems With SOAR The Einstellung Effect The Power Law of Learning
The programming for soar’s chunking can be very complex and difficult.
The Einstellung Effect
The Power Law of Learning
There are disadvantages Soar’s architecture.
large chunking may leads to incorrect knowledge
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28. The Future
Ultimately SOAR has great potential but it has many of the same limitations that humans have in learning.
Perhaps humans aren’t the best model for intelligent behavior. Maybe there isn’t a perfect model.
Artificial Intelligence can and will get better.
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29. References
Lehman, Laird, Rosenbloom, A Gentle Introduction to Soar, an Architecture for Human Cognition (1993)
Cognitive modeling, symbolic. Wilson, Keil (edl.), The MIT Encyyclopedia of the Cognitive Sciences. Cambridge, MA: MIT Press.
Soar Technology, Soar: A Comparison with Rule-based Systems, 2002 Soar Technology, Inc.
Soar Technology, Soar: A Functional Approach to General Intelligence, 2002 Soar Technology, Inc.
Soar Technology Soar: Along the Frontiers, 2002 Soar Technology, Inc.
Cognitive Theory, SOAR, Lewis, Richard L. Ohio State University, 1999
Cognitive Architectures
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