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Subsumption Architecture and Nouvelle AI

Subsumption Architecture and Nouvelle AI . Arpit Maheshwari Nihit Gupta Saransh Gupta Swapnil Srivastava. Seminar Roadmap. 1. PSS and Knowledge Representation 1.1 Basic Idea 1.2 Problems with Abstraction 2. Nouvelle AI 2.1 Framework 2.2 Decomposition by activity

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Subsumption Architecture and Nouvelle AI

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  1. Subsumption Architecture and Nouvelle AI Arpit Maheshwari Nihit Gupta Saransh Gupta Swapnil Srivastava

  2. Seminar Roadmap 1. PSS and Knowledge Representation 1.1 Basic Idea 1.2 Problems with Abstraction 2. Nouvelle AI 2.1 Framework 2.2 Decomposition by activity 2.3 Differences with Classical AI 2.4 Methodology in practice: Subsumption Architecture

  3. Roadmap (Contd..) 2.5 Challenges 3. Summary Comparision: Classical vs Nouvelle AI

  4. 1. PSS and Knowledge Representation • A physical symbol system consists of a set of entities, called symbols, which are physical patterns that can occur as components of another type of entity called an expression (or symbol structure) • A physical symbol system is a machine that produces through time an evolving collection of symbol structures

  5. PSS Hypothesis • A physical symbol system has the necessary and sufficient means for general intelligent action. Allen Newell and Herbert Simon, 1975

  6. Classical framework Plan and Act Perception Model

  7. Problems with Abstraction • Intelligence = Abstraction + Reasoning (Logically) • The efforts at AI are not truly intelligent (Why?) • Claim: An abstraction would never be as informed as the object itself e.g. chair

  8. Problems with Abstraction(contd..) Human: Sensing Intelligence Machine: Sensing Abstraction Reasoning Example- Chess playing

  9. 2. Nouvelle AI • Also called Behavior-based AI • It is extremely popular in robotics • It allows the successful creation of real-time dynamic systems that can run in complex environments.

  10. Framework • Concept of a “Creature” – an engineering methodology • Incremental Intelligence • Testing in Real World “The world is the best model of itself” • Intelligence stems from a tight coupling between sensing and actuation (No knowledge representation)

  11. Evolution: A motivation Expert Systems single-celled life insects 3.5 billion years ago Brooks’ conclusion: Complex behavior, knowledge, and reason are all relatively simple once the basics of survival - moving around, sensing the environment, and maintaining life - are acquired. 550 million years ago present day

  12. Decomposition by Activity • Layer: An activity-producing system • Each activity connects sensing to action directly • Advantage- A clear incremental path for simple to complex systems. Easy to add behaviors

  13. What is different? • No specific output of perceptions • No Central System • Representation got rid off Example: Eye sensing

  14. Society of mind • Proposed by Minsky • Nouvelle AI seems to draw inspiration from this concept

  15. Methodology in practice • Subsumption Architecture Developed by Rodney Brooks for robot control in 1986

  16. Earlier approach-Function modules Sensors Actuators

  17. Layered Architecture • The Subsumption Architecture is: • A layering methodology for robot control systems • A parallel and distributed method for connecting sensors and actuators in robots

  18. An example: A mobile robot Layer 5: Identify objects Layer 4: Monitor changes Layer 3: Build maps Layer 2: Explore Layer 1: Wander aimlessly Layer 0: Avoid hitting objects

  19. Merits • Multiple Goals • 2-fold Robustness • Additivity

  20. Structure of Layers • Each layer is made up of connected, simple processors: Augmented Finite State Machines

  21. Layers (contd..) • The most important aspect of these FSMs • Outputs are simple functions of inputs and local variables • Inputs can be suppressed and outputs can be inhibited • This function allows higher levels to subsume the function of lower levels • Lower, therefore, still function as they would without the higher levels

  22. Nouvelle AI Different from • Connectionism, Neural networks • Production rules system

  23. Challenges • Maximum number of layers? • How complex can the behavior be that are captured without central representation? • Can higher-level functions such as learning occur?

  24. Summary Classical AINouvelle AI • Make a detailed static React directly to the plan in advance world • Representation-based No central representation • Simplified-world Real world • Central and peripheral No such distinction systems

  25. References • R. A Brooks (1991). "Intelligence Without Representation", Artificial Intelligence 47 (1991) 139-159 • Brooks, “A Robust Layered Control System for a Mobile Robot”, Robotics and Automation, IEEE Journal of; Mar 1986, pp. 14 – 23, vol. 2, issue 1

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