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

  3. Prof. Zadeh: Fuzzy Systems Inter-disciplinary View • System Theory • Cybernetics, Control Theory • Artificial Intelligence • Intelligent Control • New AI • Behavior based Systems (Control) K.N.Toosi U of Tech.

  4. What is a control problem? Concept of Control Action Controller Actuator output set point K.N.Toosi U of Tech.

  5. Functional Decomposition Perception Recognition Map building Planning Action sensors actuators K.N.Toosi U of Tech.

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

  8. Perception Recognition Map building Planning Action Brook’s Critiques • Engineering • Robustness, Extendibility, Multiple goal, etc. • Biological Inspirations • Subtracts are used to build more complex capabilities K.N.Toosi U of Tech.

  9. Brook’s Critiques (cont.) • Philosophical Inspirations • Learning • Unpredictability Media Lab MIT - Leonard K.N.Toosi U of Tech.

  10. Brooksian Manifesto Intelligence is in the Eye of the Observer K.N.Toosi U of Tech.

  11. manipulate the world build maps actuators sensors explore avoid obstacles wander Call for change: The Architecture Perception Recognition Map building Planning Action K.N.Toosi U of Tech.

  12. Two Orthogonal Flows Planning Planning World Model World Model Sensor Motor Sensor/Motor Control Sensor/Motor Control Sensor Motor K.N.Toosi U of Tech.

  13. Different Architectures • Planer based control • Moravec • Reactive control • Connel • Hybrid control • Arkin • Behavior-based control • Brooks 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 K.N.Toosi U of Tech.

  15. Level 3 Level 2 Level 1 Actuators Level 0 Sensors Subsumption Architecture • First BBS • Hierarchical • Levels of competence • Incremental • Extendable • Starting from most vital task K.N.Toosi U of Tech.

  16. S I 10 3 Structure of Modules in SSA Inhibitor Inputs Outputs Reset Suppresor K.N.Toosi U of Tech.

  17. SSA: Example Brook 1986 K.N.Toosi U of Tech.

  18. Hybrid Architecture Hybrid Control! Planner Reactive / Behavior-Based K.N.Toosi U of Tech.

  19. Expressing Behaviors • Finite State Machine (FSM) • Stimulus Response Diagrams • Schema • Fuzzy • Potential Fields K.N.Toosi U of Tech.

  20. Arbitration: Which action has Control • Subsumption has internal arbitration • Inhabitation and suppression • Hybrid Arch. Needs Beavior Arbitration • Fuzzy Behavior Arbitration K.N.Toosi U of Tech.

  21. Behavior Coordination • Competitive • Coordinative • Combined • Context-Dependent Blending K.N.Toosi U of Tech.

  22. Mathematical Modeling • Lack of Strict Modeling • Poor Nonlinear Dynamic Modeling • Stochastic Modeling for Learning • FSM K.N.Toosi U of Tech.

  23. Learning • Reinforcement Learning • Imitative Learning • Learning: Hierarchy, Behaviors, Sensor Fusion • Credit Assignment Problem • Evolutionary Algorithms K.N.Toosi U of Tech.

  24. Optimality/Robustness/Stability • Robust • Failure in each part eliminates a task, not a full collapse • Optimality measure as ave. reward • Behavior Stability analysis • No global stability analysis 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 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? K.N.Toosi U of Tech.

  27. Lit. of Context-Based BBS • Arkin’s Case-based Schema K.N.Toosi U of Tech.

  28. Lit. of Context-Based BBS (cont.) • Bonarini’s Fuzzy Brain K.N.Toosi U of Tech.

  29. Lit. of Context-Based BBS (cont.) • Saffioti’s Context- based Behavior Blending K.N.Toosi U of Tech.

  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! K.N.Toosi U of Tech.

  31. Conclution (cont.) • Pros • 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 K.N.Toosi U of Tech.

  32. Thank you! K.N.Toosi U of Tech.

  33. Moravec’s Perspective K.N.Toosi U of Tech.

  34. Potential Fields Expression K.N.Toosi U of Tech.

  35. Schema Expression • Low and High Gain K.N.Toosi U of Tech.