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A Summary of the Article “Intelligence Without Representation” by Rodney A. Brooks (1987) Presented by Dain Finn. Introduction. Initial goals of the AI field were ambitions – completely replicating human intelligence Gradual realization of what a huge task this is

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A Summary of the Article “Intelligence Without Representation” by Rodney A. Brooks (1987)Presented by Dain Finn
  • Initial goals of the AI field were ambitions – completely replicating human intelligence
  • Gradual realization of what a huge task this is
  • Focus shifts to specialized sub-problems:
    • Language processing
    • Vision
    • Knowledge representation
    • … etc …
  • Brooks (and others) believe human intelligence is too complex to try to decompose with our present knowledge
  • In favor of a different approach:
    • Incrementally build up capabilities – each step is a complete (yet simple) system in itself
    • Each step must be tested thoroughly – in the real world
  • Brooks believes:
    • Attempting to come up with models and representations of the real world is the wrong approach
Evolution of Intelligence

* Not to scale







fish and vertebrates

Expert knowledge





single-celled life

3.5 billion years ago

2.5 billion years ago

2.5 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.

550 million years ago

present day

  • Brooks: AI researchers tend to factor out motor skills and perception; he believes that these _are_ the difficult problems that need to be solved.
  • The blocks world: popular AI research concept in the 60’s and 70’s. Everything in the “world” was built from blocks. Criticized for its oversimplification and not being relevant to real-world problems.
  • New focus on representation: coming up with systems to abstract the semantics of a world, to reduce it to a simple set of pertinent facts that the computer could then work with.
  • Brooks: it’s the act of creating this type of abstraction that is the essence of intelligence. As long as we do all of the abstraction for our programs, we’re basically still working in the blocks world.
Incremental Intelligence
  • Brooks’ goal: To build “Creatures” - autonomous mobile agents that exist in the real world. More concerned with an engineering methodology for achieving this than with discovering how the human mind works or with philosophical topics.
  • Requirements of a creature:
    • Must cope with dynamic environment
    • Gradual changes in the environment should result in a gradual change of the creature’s behavior, not total collapse
    • Creature should maintain multiple goals. Depending on circumstances it will re-prioritize these.
    • The creature should have some purpose.
Incremental Intelligence

Input module #1

  • Engineering approach #1 - decomposition by function:
    • The traditional view is to have one central information processing system with input and output modules connected to it.

Input module #2

Central System

Output module #1

Output module #2

Incremental Intelligence
  • Engineering approach #2 - decomposition by activity:
    • Brooks’ idea is to make no distinction between different systems like “vision” and “central systems” – but rather to have individual layers that each produce one activity.

Activity layer 3

Activity layer 2

Activity layer 1

  • Representation is removed with the idea of multiple layers: the lines are blurred between where input and output is occurring. Sensor data is acted on by multiple systems working independently in parallel.
  • Low level activities allow fast reactions to dangerous circumstances without any delay of data processing.
  • By removing a central control point, there’s less chance of total collapse. Each layer extracts only aspects of the world that it finds relevant. Changes may produce difficulty in a single layer without the Creature completely breaking down.
  • Each layer serves its own goal. Each layer continuously monitors the environment and adjusts as needed. Separate hardware for each layer – so adding new goals won’t slow down the Creature’s processing.
  • The “purpose” of the Creature is a property of the design of the higher-level layers. There’s no central system selecting from a list of possible goals.
  • The Creature is a collection of competing behaviors. It is only the observer who sees the Creature to have a central goal: a pattern of behavior.
The Methodology in Practice
  • Creatures must be tested in the real world. A simplified world won’t do.
  • Simplified creatures first, then add sophistication.
  • With a thoroughly debugged system, add another layer. If there’s problems, it can only come from:
    • Existing layer
    • The new layer
    • An interaction between the new and existing
The Methodology in Practice
  • Brooks and colleagues built an initial set of robots to demonstrate their ideas. They use what they call the “subsumption architecture”
  • Each layer in the subsumption architecture is a network of simple finite state machines, which run asynchronously and send messages between each other. The arrival of messages or the expiration of timers trigger state transitions.
  • Layers are combined using “suppression” and “inhibition”: injecting messages into lower layers and telling them to temporarily suspend normal operation.
  • Example of one of his robots:
    • Lowest layer: robot avoids hitting physical objects
    • Middle layer: robot randomly wanders
    • Top layer: robot seeks out distant reachable points
What this is not
  • This “subsumption architecture” may resemble other approaches but Brooks believes it is distinct:
    • Connectionism: an approach that focuses on using multiple processors networked together; the focus tends to be on the connections themselves and the hope of finding a distributed representation in the system.
    • Neural networks: parent discipline of connectionism
    • Production rules: rule is selected from a rule database using a set of preconditions (variables)
    • Blackboard control system: localized knowledge sources put their information on a central “blackboard,” and different sub-units search this repository of information to make decisions.
Limits to growth
  • Brooks: although promising, many questions remain about this approach:
    • How many layers before the interactions become too complex?
    • How complex can an individual layer be without using central representations?
    • Can the simple finite state machine model support high level functions like learning?