Subsumption Architecture and Nouvelle AI

Arpit MaheshwariNihit Gupta

Saransh GuptaSwapnil Srivastava

Seminar Roadmap

1. PSS and Knowledge Representation1.1 Basic Idea1.2 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

Perception Model 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 IntelligenceMachine: 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 System

s

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

ActuatorsSensors

Layered ArchitectureThe 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 objectsLayer 4: Monitor changesLayer 3: Build mapsLayer 2: ExploreLayer 1: Wander aimlesslyLayer 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) 139-159

2. 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