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 2.3 Differences with Classical AI 2.4 Methodology in practice: Subsumption Architecture

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

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

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

Classical framework PerceptionModel Plan and Act

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

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

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.

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)

Evolution: A motivation 3.5 billion years ago insects single-celled life present day Expert Systems 550 million 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.

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

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

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

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

Earlier approach-Function modules Actuators Sensors

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

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

Merits Multiple Goals 2-fold Robustness Additivity

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

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

Nouvelle AI Different from Connectionism, Neural networks Production rules system

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?

Summary Classical AI Nouvelle 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

References 1.R. A Brooks (1991). "Intelligence Without Representation", Artificial Intelligence 47 (1991) 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