This document summarizes Rodney Brooks' embodied AI approach from the 1980s. It discusses how Brooks argued that sensorimotor skills are essential for intelligent behavior, in contrast to the dominant symbolic AI approach. Brooks proposed a bottom-up approach known as the subsumption architecture, where autonomous robots are built up from layers of independent behaviors rather than using internal representations. While this approach enabled robust real-world behavior, it lacked abilities like meta-cognition, coordinated goal-setting, and learning from experience.

1. Introduction to AI
7th Lecture
1980’s – Body over Mind
Wouter Beek
me@wouterbeek.com
4 November 2010

2. Part I
1980’s, Body over Mind

3. Nouvelle AI
0 Sensorimotor skills are essential to higher level skills
like commonsense reasoning.
0 Abstract reasoning is the least interesting or
important human skill.
0 So who came up with the idea… (though tentatively)?
0 “It […] is best to provide the machine with the best sense
organs that money can buy […]. That process could
follow the normal teaching of a child.”
0 Alan Turing, 1950

4. Embodied AI – philosophical position
0 Embodiment: The functions of the mind can be
described in terms of aspects of the body.
0 Cognitivism: the functions of the mind can be described
in terms of information processing.
0 Computationalism: the functions of the mind can be
described in computational terms.
0 Cartesian dualism: the functions of the mind are
described in immaterial terms.
0 Embodiment hypothesis: conceptual and linguistic
structures are shaped by the peculiarities of
perceptual structures. [Lakoff & Johnson 1999]

5. Remember: Moravec paradox
0 Optimism due to machines solving things that are difficult for
humans:
0 Geometrical problems
0 Logical proofs
0 Games of chess
0 But things that are easy for humans are often difficult for
machines:
0 Taking the garbage out.
0 Recognizing the man walking across the street is Joe.
0 Sensorimotor skills and instincts are (arguably) necessary for
intelligent behavior, but pose enormous problems for machines.

6. Moravec paradox – The argument
0 The time that evolution took to produce a certain skill is
proportional to the difficulty to implement that skill.
0 The oldest human skills are unconscious and effortless.
0 The youngest human skills are conscious and require lots of
effort.
0 Effortless skills are the most difficult to implement.
0 Difficult skills are the easiest to implement, once the
effortless skills have been implemented.
But:
0 Cultural evolution is faster than biological evolution.
0 Temporal progression need not parallel complexity.
0 Temporal progression suggests a quantitative development.

7. Bottom‐up approach
Bottom‐up approach: Follow the evolutionary trail,
increasing the complexity of artificial agents.
Contrast this to:
0 Top‐down approach: start off with conscious
reasoning and add sensors/actuators later.
0 Only‐top approach: only solve conscious reasoning
problems, planning, conceptual learning and
language. Sensors/actuators, although interesting, are
not a part of AI.

8. Subsumption architecture
Characteristics:
0 Task decomposition
0 Parallel processing: layers having independent goals.
0 Bottom‐up design: from unconscious to conscious
behavior.
0 No internal representation: layers having implicit goals
Advantages:
0 Modularity
0 Robustness, autonomy
0 Iterative development and testing

9. Reactive planning
0 Operate in a timely fashion
0 Work in dynamic and unpredictable environments.
0 Compute only the next action, based on the current
stimuli.
0 Cognitive minimalism: behavior modules are FSM’s
without memory or learning abilities.

10. Neats & scruffies
0 What is the best way to conduct AI research?
0 Neats: By producing elegant solutions within a
theoretical framework.
0 Including a formal notion of operation, e.g. provability.
0 Scruffies: By hacking and tweaking.
0 Mid‐1970’s: Roger Schank defines the distinction.
0 1983: Nils Nilsson, both are needed @AAAI
presidential address.
0 1989: Rodney Brooks, robots should be fast, cheap and
out of control.
0 2000’s: The victory of the neats? Russell & Norvig, p25.

11. Part II
Reasoning without
Representation

12. Evolutionary decomposition
0 In evolutionary terms, reacting and acting took longer
to develop than intelligence and expert knowledge.
0 Critique: The temporal progression of intelligent life
forms need not be in line with the qualitative nor with
the quantitative progression of intelligent functions.
0 Artificial Flight researchers that try to emulate a
modern airplane by subdividing tasks based on
component segmentation will not manage to get to
the gist of the problem, i.e. aerodynamics.
0 In general: decomposition should proceed
evolutionary, from simple to complex (or bottom‐up)
and not by segmenting the complex (top‐down).

13. Brooks: AI as an empirical science
0 AI should pose and verify hypotheses (remember Newell &
Simon).
0 But AI never fails, so something must be wrong.
0 AI always succeeds by defining the unsolved parts of a
problem as not pertaining to AI.
0 This is done by factoring out all aspects of perception and
action.
0 This is a dubious form of abstraction (and it explains the
Brittleness problem).
0 Critique: AI often fails, and this failure is not always related
to perception or action.
0 For instance Machine Translation research of the 1950's and
1960's has failed because of the open world problem.

14. Brittleness problem
0 Brittleness problem: The inability to cope with
unexpected changes in the environment.
0 The brittleness problem in AI is caused by the division
between perception, action and reasoning. In real
organisms there is no such segmentation.
0 The solution is a specific interpretation of the
empirical enterprise of AI research:
0 Not top‐down hypothesis validation, but bottom‐up
engineering.

15. AI as bottom‐up engineering
0 AI should be the engineering task of building
Creatures that
0 are completely autonomous mobile agents
0 co‐exist with humans in the world
0 are seen by humans as intelligent beings in their own
right
0 Creatures should follow the following engineering
principles. They should
0 operate in a timely fashion
0 be robust, exhibiting a gradual change in capability
under environmental change
0 maintain multiple goals
0 do something, have a purpose in being

16. Horizontal vs vertical layers
0 [A] In traditional AI research, the assumptions that independent
research fields make are not forced to be realistic. This is a bug in the
functional decomposition approach.
0 The vertical layers: machine learning, vision, knowledge systems,
automatic translation.
0 [B] The traditional decomposition separates, among other things,
peripheral perception and action modules from central reasoning or
processing modules.
0 The fact that [A] assumptions are not enforced, does not imply [B] that
the underlying decomposition is wrong.
0 It must be shown that under the traditional, functional decomposition
of the research field, assumptions cannot possibly be enforced.
0 What really plays a role here: the assumption that reasoning and
language are heavily influenced by sensors and actuators, and by being
in the world.
0 The horizontal layers: obstacle avoidance, path finding, path planning.

17. Sparseness of representations
0 The world is its own best representation.
0 No world model maintenance, so more robust.
0 Each layer has an independent and implicit purpose or
goal.
0 The purpose of the entire Creature is implicit in the
collation of the independent purposes of the individual
layers.
Layer interactions in non‐symbolic terms:
0 Suppression: side‐tapping, replacing an original input
message by a message from a lower level.
0 Inhibition: side‐tapping, inhibiting an output message
without replacing it.

18. Activity‐Producing Subsystem Decomposition
Combines:
0 [1] bottom‐up decomposition (activity‐producing)
0 [2] horizontal layering (subsystem)
Brooks adds to this [3] the sparseness of
representations.

19. Embodied AI’s Disadvantages
0 Meta‐cognition: no reification of tasks, goals or
processes.
0 Goal interference: independent goal‐directed
behaviors.
0 Task coordination: subsumption in the case of
multiple levels is weakly structured.
0 Learning: related to the meta‐cognition disadvantage,
since there is no medium in which learning can take
place, i.e. no reification of thoughts.