1. Behavior Based Systems
Ramin Mehran
Digital Control Laboratory
K.N.Toosi U of Tech.
Supervisors:
Professor Caro Lucas
Dr. Alireza Fatehi
September 5, 2019September 5, 2019
K.N.Toosi U of Tech.

2. Contents Where BBS Stands? Functional/Task Decomposition
Robotic Problem: BBS test bed
Reactive/BBS/Hybrid Arch.
Subsumption Arch.
Expressing Behaviors
Behavior Coordination/Arbitration
Learning/Robustness/Stability/Optimality
BBS and Context-Based Systems
Conclusion
September 5, 2019September 5, 2019
K.N.Toosi U of Tech.

3. Inter-disciplinary View
Prof. Zadeh: Fuzzy Systems
System Theory
Cybernetics, Control Theory
Artificial Intelligence
Intelligent Control
New AI
Behavior based Systems (Control)
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4. What is a control problem?
Concept of
Control
Action
Controller
Actuator
output
set point
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5. Functional Decomposition
Perception
Recognition
Map building
Planning
Action
sensors
actuators
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K.N.Toosi U of Tech.

6. Functional Decomposition
Control System
Perception
Recognition
Map building
Planning
Action
sensors
Image
Recog.
Control
Theory
Filtering
Search
Knowledge
Representation
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K.N.Toosi U of Tech.

7. Robotics: Increasing Complexity
Dynamic and Nondeterministic Environment
Nonholomic
Conf. space smaller than Cont. space
Sensors…
Similar to Real-Life Problems
Failed Approaches
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8. Brook’s Critiques Engineering Biological Inspirations
Robustness, Extendibility, Multiple goal, etc.
Biological Inspirations
Subtracts are used to build more complex capabilities
Recognition
Action
Perception
Planning
Map building
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K.N.Toosi U of Tech.

9. Brook’s Critiques (cont.)
Philosophical Inspirations
Learning
Unpredictability
Media Lab MIT - Leonard
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10. Intelligence is in the Eye of the Observer
Brooksian Manifesto
Intelligence
is in the Eye of
the Observer
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11. Call for change: The Architecture
Perception
Recognition
Map building
Planning
Action
build maps
explore
avoid obstacles
wander
manipulate
the world
sensors
actuators
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K.N.Toosi U of Tech.

12. Two Orthogonal Flows Planning Planning World Model Sensor World Model
Motor
Sensor/Motor
Control
Sensor/Motor
Control
Sensor
Motor
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13. Different Architectures
Planer based control
Moravec
Reactive control
Connel
Hybrid control
Arkin
Behavior-based control
Brooks
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K.N.Toosi U of Tech.

14. Behavior Based Properties
No Global Representation
e.g. No global map
Are feedback controllers
FSM, Fuzzy, PID, etc.
Achieve specific tasks/goals
(e.g., avoid-others, find-friend, go-home)
Executed in parallel/concurrently
Can store state and be used to construct world models
(local representation)
Behaviors can directly connect sensors and effectors
September 5, 2019September 5, 2019
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15. Subsumption Architecture
First BBS
Hierarchical
Levels of competence
Incremental
Extendable
Starting from most vital task
Level 3
Level 2
Level 1
Level 0
Sensors
Actuators
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16. Structure of Modules in SSA
Inhibitor I 3
Inputs
Outputs S 10
Reset
Suppresor
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17. SSA: Example Brook 1986 K.N.Toosi U of Tech.
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18. Reactive / Behavior-Based
Hybrid Architecture
Hybrid
Control!
Planner
Reactive / Behavior-Based
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19. Expressing Behaviors Finite State Machine (FSM)
Stimulus Response Diagrams
Schema
Fuzzy
Potential Fields
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20. Arbitration: Which action has Control
Subsumption has internal arbitration
Inhabitation and suppression
Hybrid Arch. Needs Beavior Arbitration
Fuzzy Behavior Arbitration
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21. Behavior Coordination
Competitive
Coordinative
Combined
Context-Dependent Blending
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22. Mathematical Modeling
Lack of Strict Modeling
Poor Nonlinear Dynamic Modeling
Stochastic Modeling for Learning
FSM
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23. Learning Reinforcement Learning Evolutionary Algorithms
Imitative Learning
Learning: Hierarchy, Behaviors, Sensor Fusion
Credit Assignment Problem
Evolutionary Algorithms
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24. Optimality/Robustness/Stability
Failure in each part eliminates a task, not a full collapse
Optimality measure as ave. reward
Behavior Stability analysis
No global stability analysis
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K.N.Toosi U of Tech.

25. BBS in Multi-Agent Systems
Planner-based Arch. Fails for exponential growth of state space
Uncertain and Unobservable
Classical planning is intractable
BBS uses local less complex strategies
September 5, 2019September 5, 2019
K.N.Toosi U of Tech.

26. BBS and Context-Based Systems
Context is in the eye of the observer?!
Hybrids are OK with context
Pure BBS hard to show context transitions
Creating new context?
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27. Lit. of Context-Based BBS
Arkin’s Case-based Schema
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28. Lit. of Context-Based BBS (cont.)
Bonarini’s Fuzzy Brain
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29. Lit. of Context-Based BBS (cont.)
Saffioti’s Context- based Behavior Blending
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30. Conclusion When to use BBS and When to avoid it? Does it do real time?
Do we know the model?
How uncertain is the environment and sensors?
When you can use a simple PID, use it!
September 5, 2019September 5, 2019
K.N.Toosi U of Tech.

31. Conclution (cont.) Pros Cons
Extendibility, Incremental, Real-world applicability, Robustness, Emergent, Modularity
Most Real-world working robots are BBS!
First 6 legged robot was Brook’s!
Cons
No global representation, unclear design method, Stability, Optimality, Not explicit mathematical model
September 5, 2019September 5, 2019
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32. Thank you!
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33. Moravec’s Perspective
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34. Potential Fields Expression
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35. Schema Expression Low and High Gain K.N.Toosi U of Tech.
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