Brooks and Subsumption

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• Brooks and Subsumption

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Intelligent Robot Systems
Knowledge
AI control
Perception
Planning and control
Supervision
Basic level control
Sensors
Actuators
World
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Typical Robot System
world
human
Sensors
Actuators
Human interface
Robot
Execution of program
Control
Data acquisition
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Tradition approach

• decomposition by function
• Sensors ?
  ? Actuators
  

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Traditional AI model
R. Brooks, Cambrian Intelligence
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Supervision/Control of a robot
Human Interface
Control of high levels
Supervision
Control of low level
Control of low levels
Robot
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Hierarchical Architecture
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Hierachical Architecture
StrategicThis level generates goals and
strategies for achieving those goals which result
in the achievement of an overall objective
Where to go
TacticalThis level generates the tactics by
which a goal is to be achieved
how
ExecutiveThis level execute the tactical plan
execute
Three Level Architecture
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Context

• Physical symbol system hypothesis
• PSS Has physical patterns called symbols and
  collections of these symbols called expressions
• PSSH A PSS has the necessary sufficient means
  for general intelligent action
• Important assumption
• Intelligence occurs via reasoning
• E.g., searching for operators logical inference
• Thinking vs. acting
• PSSH type approaches emphasize thinking
• But what about intelligence based on acting?
• E.g., some nonhuman animals may act more than
  think but they seem to have a kind of
  intelligence

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Reactive Robotics
The Brooks School
Behavioral Robotics

• Subsumption architectures for walking machines

Rodney Brooks, Connell
Brooks, R. A. (1999). Cambrian Intelligence The
Early History of the New AI. Cambridge, MA MIT
Press.
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Who is Rodney Brooks?

• Adelaide born. Flinders, Stanford, , MIT
• Fujitsu Professor of Computer Science and
  Engineering (EECS Dept) at MIT.
• Director of the Artificial Intelligence
  Laboratory at MIT.
• Companies Lucid, IS Robotics Inc., Artificial
  Creatures.
• Claims he is a pragmatist.

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The early years of subsumption

• Aware of the failure of mobile robotics to live
  up to its potential.
• Autonomous vehicles were not that autonomous and
  weren't even very good vehicles.
• Brooks identified various aspects of mobile
  robotics which he considered to be important and
  obvious

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Key Topics of Behaviour-based Approach

• Situatedness
• Embodiment
• (animal or insect) Intelligence
• Emergence

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Situatedness

• A situated automaton is a finite-state machine
• whose inputs are provided by sensors connected to
  the environment, and
• whose outputs are connected to effectors.
• The world is its own best model
• Traditional AI, working in symbolic abstracted
  domain.
• Problem solvers which are not participating in
  the world as agents.
• Dealing with model world - no real connection to
  external world.

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Why situatedness is better for mobile robot
behaviors?

• Alternative approach, to use a mobile robot which
  uses the world as its own model, referring to
  information from sensors rather than internal
  world model.
• Representations are developed which capture
  relationships of entities to robot.
• Situated agent must respond in timely fashion to
  inputs but much information from the world.

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Embodiment

• Embodiment Physical grounding of robot in real
  world.
• According to Brooks (1991), embodiment is
  critical for two reasons.
• Only an embodied agent is validated as one that
  can deal with real world.
• Only through a physical grounding can any
  internal symbolic system be given meaning.

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Emergence

• Intelligence emerges from interaction of
  components of the system.
• Behaviour-based approach - intelligence emerges
  from interaction of simple modules.
• e.g. Obstacle avoidance, goal finding, wall
  following modules.

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Main Ideas of Behavior-based Approach

• Main ideas
• No central model maintained of world
• No central locus of control
• No separation into perceptual system, central
  system and actuation system
• Behavioural competence improved by adding one
  more behaviour specific network to existing
  network.
• Crude analogy to evolutionary development
• No hierarchical development
• Layers or behaviors run in parallel

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Model of Brooks the perceptual and action
subsystems are all there really is
Cognition is only in the eye of an observer
A. Brooks, Cambrian Intelligence
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Brooks Criticizes traditional robotics

• Intelligence is determined by the dynamics of
  interaction with the world he says
• Some activities we think of as intelligent have
  only been taking place for a small fraction of
  our evolutionary lineage.
• Simple behaviors to do with perception and
  mobility took much longer to evolve.
• Would make sense to begin by looking at simpler
  animals.
• - looking at dynamics of interaction of robot
  with its environment.

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Example of Criticisms of traditional approach -
Brooks

• We should take evidence from biology and
  evolution.
• SPA systems highly constrained.
• Early work formal systems, Blocks World.
• Funding forced relevance and new slogan.
• But this ignores knowledge acquisition!
• Introspection is misleading.
• Brooks rejects symbol system hypothesis.

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Key Brooksian Ideas

• Situatedness and embodiment, as discussed.
• Approximate evolution
• Incremental additions improve performance
• Layers
• Each layer
• Corresponds to new behavior
• Relies upon existing layers
• Has minimal interaction with other layers
• Is short connection between perception
  actuation
• Advantages of this paradigm.

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Brook's General Robot Requirements

• He identified a number of requirements of a
  control system for an intelligent autonomous
  mobile robot.
• Multiple Goals
• Some conflict, context dependent
• Multiple Sensors
• All have errors, inconsistencies and
  contradiction.
• Robustness
• The robot must by fault-tolerant.
• Extensible
• You have to be able to build on whatever you built

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Subsumption architecture - the Brooks approach

• levels of competence
• example competences
• layers of control
• subsumption
• structure of layers
• extensions
• finite state machines

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What were the weaknesse of traditional approaches?

• Cant account for large aspects of
  Intelligence
• Reliant on representation
• Rapidly changing boundary conditions
• Hard to map sensor values to physical
  quantities
• Not robust
• Relatively slow response
• Hard to extend
• Hard to test

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Brooks Dogma

• Brooks also introduced, what he called,
• "9 dogmatic principles",
• 1) Complex (and useful) behavior need not
  necessarily be a product of an extremely complex
  control system.
• 2) Things should be simple Interfaces to
  subsystems etc.
• 3) Build cheap robots that work in human
  environments
• 4) The world is three-dimensional therefore a
  robot must model the world in 3 dimensions.

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Dogma (cont)

• 5) Absolute coordinate systems for a robot are
  the source of large cumulative errors.
• 6) The worlds where mobile robots will do useful
  work are not constructed of exact simple
  polyhedra.
• 7) Visual data is useful for high level tasks.
  Sonar may only be good for low level tasks where
  rich environmental descriptions are unnecessary.
• 8) The robot must be able to perform when one or
  more of its sensors fails or starts giving
  erroneous readings.

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Dogma (cont)

• 9) "We are interested in building "artificial
  beings"
• --robots that survive for days, weeks and months,
  
• without human assistance,
• in a dynamic complex environment.
• Such robots must be self-sustaining

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Solution Subsumption

• Brooks and his group eventually came up with a
  computational architecture.
• Model arrived at by continually refining attempts
  to program a robot to reactively avoid collisions
  in a people-populated environment.
• Not intended as a realistic model of how
  neurological systems work.
• The model is called "subsumption architecture.
• Its purpose is to program
• intelligent,
• situated,
• embodied agents.

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new architecturesubsumption

• Introduced in Brooks seminal 1986 paper
• Consists of layered behaviors,
• from simple to complex,
• with simple interfaces
• Layers can override each other
• Each layer has a control program that is capable
  of working at the speed of environmental change
• Each layer now can do
• the appropriate model building,
• sensor fusion,
• etc.

• 1. No central model of world.
• 3. No separation into perception, central
  processing, and actuation.
• 3. Layering increases capabilities.
• 4. No hierarchical arrangement.
• 5. Messages on input ports when needed.
• 6. Behaviors run in parallel.

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Basic Architecture of an autonomous agent
Sensors
Cognition
Effectors
Vision, range, touch Decides
actions and Carry out actions

commands
effectors
Subsumption Architecture

• Control Problem for an autonomous robot
• Traditional Approach
• functional decomposition
• A new approach
• decomposition by activity

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Motivation

• To achieve cooperation and coordination among
  different robots and self governance with human
  like performance in a common work space.

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Key Problems for Subsumption-based mobile robots.

• Cooperation
• Coordination
• Learning
• Vision
• Localization

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Subsumption Architecture

• 1. Reactive robotics approach, which Brooks calls
  behavioral robotics.
• 2. The central idea of Brooks approach is that
  more sophisticated robot competencies should be
  built on top of simpler ones.
• Instead of all robot inputs feeding into a
  sensory perception unit, which creates a world
  model of the robots environment, which feeds
  into a planning module, Brooks has argued that
• robot perception and action should be closely
  linked,
• complex behaviors can be built from the
  interactions of simple ones.
• Example
• a robot that must walk should first learn to
  stand.
• Then later behaviors can exploit earlier ones
  a task which causes a legged robot to move its
  legs can make use of the knowledge embedded in
  the behavior that allows the robot to simply
  stand.

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Subsumption Architecture Massiveness

• Brooks has proposed that future unmanned
  interplanetary missions should be performed by
  hundredsor thousandsof simple, insect-like
  robots that act in teams to accomplish work,
  rather than a large and complicated monolithic
  device.
• Individual robots could be considered expendable
  without jeopardizing the success of the entire
  mission, whereas if a single large robot had a
  failure, the mission would be over.
• Homework (not this year)
• Read
• Rodney Brookss paper on Fast, Cheap, and Out of
  Control

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Behavior-based Robotics in 1984

• Groups at MIT and SRI independently began
  rethinking how to organise intelligence (around
  1984).
• Requirements
• Reactive to dynamic environment
• Operate on human time scales
• Robustness to uncertainty/unpredictability
• They implemented simple systems with similar
  features.

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Subsumption Priciples Network Construction

• 1) Computation is organized as an asynchronous
  network of active computational elements
• they are augmented finite state machines,
• with a fixed topology of unidirectional
  connections.
• 2) Messages sent over connections have no
  implicit semantics
• they are small numbers (typically 8 or 16 bits,
  but on some robots just 1 bit),
• their meanings are dependent on the dynamics
  designed into both the sender and receiver
• 3) Sensors and actuators are connected to this
  network,
• usually through asynchronous two-sided buffers.

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Subsumption Architecture Incrementally build
network of state machines

• Incremental method for building robots
• Network of finite state machines links sensors to
  actions
• Internal timers
• Control system built in layers
• Model Message passing augmented finite state
  machines

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Finite State Machines

• In Brooks original work, each module was
  implemented as a finite state machine
• augmented with some instance variables
• A finite state machine has
• a set of states
• a start state (one or more stop states)
• a set of symbols
• a set of transitions
• For example
• an FSM for a light switch

• An FSM can be implemented in C
• using a state variable and switch statement

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Let us look again to more limitations of
Conventional Approaches

• Perception takes too long
• Perception is not a solved problem, nor will it
  be solved in the near future.
• Modeling/planning component assumes complete
  models are available
• Overall system cannot respond in real-time
• Most robots built this way have failed
• (or run very slowly)

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And the response of the Behavior-based Approach

• Decompose overall control system into a layered
  set of reactive behaviors
• Each behavior represents a complete mapping from
  sensors to motor commands
• Low-level behaviors (e.g. avoid) can run in
  real-time since they use little computation
• High-level behaviors are invoked only when
  necessary
• Requires arbitration strategy to choose among
  (or combine) conflicting behaviors

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What does it mean that behaviors run in parallel
and control directly?

• The control system is broken down into horizontal
  modules, or behaviors, that run in parallel
• each behavior has direct access to sensor
  readings and can control the robots motors
  directly

sense
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Brooks Assumptions for mobile robots Complex
behaviors from simple controls

• Complex and useful behavior need not necessarily
  be a product of an extremely complex control
  system
• Things should be simple
• if a single module is getting too big rethink
  the design
• unstable or ill-conditioned solutions are not
  good
• Ability to wander in real environments is crucial
• The real world is three dimensional
• the robot must model its environment in 3D
• Absolute coordinate systems are a source of error
• relational maps are more useful to a mobile robot

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Brooks Assumptions for mobile robots sensors
integration and autonomy -

• The real world is not constructed of exact shapes
• models may be useful, but should not be used
  alone
• Sonar data, while easy to collect, do not lead to
  rich descriptions of the world
• visual data is much better
• For robustness, the robot must be able to
  function when one or more of its sensors fails or
  starts giving erroneous readings
• recovery should be quick (self-calibrating)
• Robots should be autonomous and self-sustaining
  (able to survive weeks without human assistance)

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Robot Architecture
how much / how do we represent the world
internally ?
Motor Schemas
Just what we need.
Subsumption paradigm

• Subsumption composes simple reactions
  (behaviors) by letting one take control at an
  appropriate time.
• World state is computed in order to perform a
  specific task.

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Larger example -- Genghis
1) Standing by tuning the parameters of two
behaviors the leg swing and the leg
lift 2) Simple walking one leg at a time 3)
Force Balancing incorporated force sensors on
the legs 4) Obstacle traversal the legs should
lift much higher if need be 5) Anticipation
handles touch sensors (whiskers) to detect
obstacles 6) Pitch stabilization uses an
inclinometer to stabilize fore/aft pitch 7)
Prowling uses infrared sensors to start walking
when a human approaches 8) Steering uses the
difference in two IR sensors to follow
57 FSMs wired together !
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Brooks Approach

• Dont use logic-based description of percepts
• Dont apply search operators or logical inference
  or planning operators
• Goal formalized, but not generalized
• Arrive at a next action, not operator sequence
• Apply that operator sequence to world (detail)
• Respond based on contingencies interaction

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Levels for the Genghis Robot

• Level1 standup
• 2 modules per leg control alpha (advance) beta
  (balance) motor
• Level2 simple walk
• does not compensate for rough terrain
• Level3 force balancing
• Compensates for rough terrain
• Level4 leg lifting
• Level5 whiskers
• Level6 pitch stabilization
• Level7 steered prowling

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Layers and modules

• Each layer consists of a number of modules
• Modules
• Module is a hardware component
• CPU memory specialized hardware program
• Modules connect to other h/w components via
  physical wires
• Connections are fixed by design not dynamic
• Connections between modules are low bandwidth
• Modules within a layer
• Interconnect with data inputs outputs
• Modules are asynchronous
• No centralized clock
• No central memory

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Conflict resolution on a path from sensors to
actuators
Module
Example of suppression
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A Behavior-based Architecture for Box Pushing
Global reward
Local Rewards on all levels
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Connections between layers

• Layer0 is lowest
• Layer1 is next
• Etc.
• Layers are designed to have separate goals
• Lower layers deal with more important goals
• E.g., important survival
• Connections across layers
• Layer1 connects to Layer0 layer2 layer 1 etc.
• Connections always from higher level to lower
  level
• Lower layer cannot detect this connection
• Connections are fixed by design (not dynamic)
• Established by physical wires

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Suppression and Inhibition in modules

• These connections across layers are of two types
• Suppression Input lines
• When layer1 sends a message on this connection,
  layer1 input replaces layer0 output
• Inhibition Output lines
• When layer1 sends a message on this connection,
  layer1 causes the layer0 output to be stopped
  (for an interval)
• No output replacement
• Modules are callable sub-functions (or
  procedures)
• Cant make a procedure call and invoke a module
• Can send a message to a module if you have a wire
  from one module to another module
• Connections are for message passing (within
  layers) and suppression/inhibition (across
  layers)

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Explanation of the layers for a mobile robot

• Avoid contact with objects (moving or static)
• Wander aimlessly around without hitting things.
• Explore the world by seeing places in the
  distance which look reachable and heading for
  them.
• Build a map of the environment and plan routes
  from one place to another
• Notice changes in the static environment
• Reason about the world in terms of identifiable
  objects and perform tasks related to certain
  objects.
• Formulate and execute plans which involves
  changing the world in some desirable way
• Reason about the behavior of the objects in the
  world and modify plans accordingly

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Emergent Behaviors in a Subsumption Architecture

• If the sensory inputs satisfy a precondition
  specific to that module, then a certain behavior
  program, also specific to that module, is
  executed
• One behavior module can subsume another behavior
• Complex behaviors can emerge from the interaction
  of a relatively simple reactive machine with
  complex environment

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Advantages of Subsumption Architecture

• Robustness
• Reactive and real-time response
• Flexible multi-goal, easy to extend
• The robot interacts with the real world, not
  symbols
• No explicit representation of goals needed
• No central control unit needed
• No mapping from sensor values to physical
  quantities needed

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Questions concerning limits of Subsumption
Architecture

• our approach
• How many layers can be built?
• How complex can a behavior be?
• Can higher level functions such as learning be
  achieved by this approach?

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Subsumption Architecture is a novel idea about
mobile robots in history.

• It brings some brand new ideas to all kind of
  robotics, not only mobiles.
• It breaks the traditional solely sequencing mode
  to a parallel mode.
• To some extent, it has actually affected the
  standard control architecture today.

. it affected also the AI..
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New Artificial Intelligence

• Reactive vs. Representational
• Emergent approaches (implicit)
• Braitenburg vehicles
• swarming, flocking
• Behavioral approaches (explicit)
• Brooks, Steels
• Physical Embodiment

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Levels (layers) of Control
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Communication among competencies
1. different sensors can be read by different
competencies
2. competencies such as observe can read outputs
from other competencies
observe
avoid obstacles
bump sensors
3. competencies can also write values into input
of other competencies
S
follow line
IR sensor
S
forage
motors
S
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Augmented Finite State Machine
R
Sensor
Finite State Machine
R
R
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Layers are composed of modules

• An example of module is shown below

Inhibitor
1
3
Behavioral module
Inputs
Outputs
S
10
Suppression and Inhibition
Suppressor
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More general behavior interactions suppression
and inhibition can be on both inputs and outputs
arbitrates among layers in an ad-hoc way
AFSK of a simple three-layered robot from Brooks
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Other examples of Subsumption Architectures

• Layer 0

robot
robot
collide
halt
motor
sonar
command
feelforce
runaway
force
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Layer0Layer1
robot
robot
collide
halt
sonar
motor
S
command
15
feelforce
runaway
force
We can suppress runaway behavior
avoid
wander
heading
Layer 1
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Another extension Layer 0
turn
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Layer 0
Subsumption Architecture
runaway behavior
FSM / DFA
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Layer 1 added
wander behavior
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Il livello 2
Layer 2 added
navigate behavior
wander behavior
runaway behavior
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Another Example of Subsumption Architecture
Mapping robot
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Subsumption
Another Example of Subsumption Architecture
Levels of competence

• A number of levels of competence are defined
• an informal specification of a desired class of
  behaviors for a robot over all environments it
  will encounter
• A higher level of competence implies a more
  specific desired class of behaviours

General purpose reasoning approach on the top
level
Control is layered, with higher level layers
subsuming the roles of lower level layers when
they wish to take control. The system can be
partitioned at any level, and the layers below
form a complete operational control system.
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Example Competencies in more detail
7 reason about the behaviour of objects in the
world and modify plans accordingly
6 formulate and execute plans that involve
changing the state of the world in some desirable
way
5 reason about the world in terms of identifiable
objects and perform tasks related to certain
objects
4 notice (observe) changes in the static
environment
3 build a map of the environment and plan routes
from one place to another
2 explore the world by seeing places in the
distance that look reachable and heading for them
1 wander aimlessly around without hitting things
0 avoid contact with (stationary or moving)
obstacles
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Step-by-step building of Layers of Control

• The key idea of levels of competence is that
  layers of a control system can be built to
  correspond to each level
• a control system can be built to achieve level
  zero competence (avoid obstacles)
• it is programmed, debugged and then fixed in
  operation
• another layer is then added (first-level control)
• it is able to examine data from the level-0
  system
• it is able to inject output, to suppress the
  level-0 output
• level-0 is unaware of the suppression and
  continues to run

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Structure of Layers

• How is each level built?
• does each have to be structured in horizontal
  manner?
• This is true to a certain extent, but the key
  difference is that not all desired perceptions
  need to be processed by each competence
• different decompositions can be used for
  different sensor-set task-set pairs
• Layers can be built on a set of small processors
• each one sends (small) messages to others
• no message acknowledgement is required
• there is no need for central control (or
  synchronisation)

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subsumption details

• Each layer has one function, conceptually
• Lower layers tend to be more reactive
• closed loop controls
• inputs tightly coupled to outputs
• Higher layers are more deliberative
• do higher-level sensor fusion modeling
• keep more state
• planning further in the future
• Layers can fake the inputs or outputs of other
  layers

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subsumption advantages(according to Brooks)

• Provides a way to incrementally build and test a
  complex mobile robot control system
• Supports parallel computation in a
  straightforward, intuitive way
• Avoids centralized control relies on
  self-centered and autonomous modules
• Leads to more emergent behavior -- Complex (and
  useful) behavior may simply be the reflection of
  a complex environment
• Compare with SPA - intelligence is entirely in
  the design of the planner (the programmer)

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subsumption successes

• Early efforts were a dramatic success, zipping
  around like R2D2 instead of pondering their plans
• Pinnacle was Herbert, who found soda cans in an
  office

Criticism of Subsumption

• Herbert didnt work very repeatably
• No subsumption-based robot since Herbert -- or is
  there?
• Is classical subsumption still in use?
• Cog is based on subsumption?
• Brooks publications, however, mainly describe
  imitation of human cognitive models and do not
  explicitly mention subsumption
• But, these models also stress non-monolithic
  control subsumption might be there implicitly

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Criticism of Subsumption

• More complex behaviors?
• This approach wont necessarily lead to system
  capable of more complex behaviors. A new
  controller is needed for each task.
• 2. Is it an evolution?
• The experimenter is deciding on what modules to
  add, and what environment and task the robot
  should be exposed to. - not the same as
  evolution.
• But in terms of evolution, new behaviors and new
  mental structures are learnt in response to the
  environment, not added by an experimenter.
• Similarly, in the development of an individual,
  new representational structures are developed in
  response to the environment, not added by an
  experimenter.

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Criticism of Subsumption

• 3. It would be more impressive if the robot
  learnt new behavior modules in response to the
  environment.
• This possibility is discussed by Brooks (1991),
  but not yet successfully tackled.
• 4. Emphasis in this approach on reacting to the
  environment.
• And it is the case that apparently quite
  sophisticated behaviors can result from simple
  reaction to the environment.
• But representations are needed for more complex
  tasks.
• e.g. Find an empty can and bring it back to the
  starting point requires the formation of an
  internal representation corresponding to a map.
• Need to provide an account of the development of
  representations.

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Other Reactive Approaches
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Other Reactive Approaches

• Two other reactive approaches that are popular
• Potential field methods
• a potential field is a concept from physics
• two examples are the gravitational field
• you do not need to be told which way to fall
• planets do not need to plan how to move around
  the sun
• and electromagnetic fields
• mobile phones, television, radio, etc.
• e.g. obstacles exert hypothetical repulsive
  forces on the robot
• Motor schema navigation
• multiple, concurrent schema generate separate
  behaviors which are summed to produce output
• schema are dynamically created/destroyed as needed

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Embedding not intelligence, but capabilities,in
everyday objects is one crucial part of the
solution.

• --Pattie Maes

84
Reactive vs. Algorithmic Control

• Algorithmic Control robots program is
  fundamentally a series of steps or actions to be
  taken in a predetermined order.
• Most effective when the robots world and its
  interactions with it are well-structured
• Manipulator arms in factories typically use
  algorithmic control with great success
• Loses its appeal when the robot must deal with
  unexpected situations
• When it is extended to deal with error
  situations, the algorithmic method becomes a
  complicated tree of branching decisions that is
  hard to design and debug
• Reactive Control robots program is organized
  around a collection of separate mini-programs,
  all running at once and able to take control of
  the robot as they see fit
• For a very simple, minimal HandyBug program,
  there might be
• a touch sensor process, which monitored the
  robots touch sensors and caused the robot to
  back up and turn when it hit something
• a periodic turn process, that caused the robot to
  take a turn every now and then
• and a wander process, that caused the robot
  simply to move
• Reactive control excels in complex situations
  with many unpredictable interactions

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Behaviour-based Robotics

• Idea of building autonomous mobile robots.
• New approach, where robots operate in the world,
  and use highly reactive architectures, with no
  reasoning systems, no manipulable
  representations, no symbols, and totally
  decentralized computation (Brooks, 1991)
• I wish to build completely autonomous mobile
  agents that co-exist in the world with humans,
  and are seen by those humans as intelligent
  beings in their own right. I will call such
  agents Creatures... (Brooks, 1991)
• Brooks, R. (1991) Intelligence without
  Representation Artificial Intelligence, 47,
  139-159.

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• See Elephants dont play chess, (1990 paper by
  Brooks)
• Brooks, Rodney A. (1990) Elephants dont play
  chess. In Pattie Maes (Ed) Designing autonomous
  Agents, Cambridge, Mass MIT Press.
• Because elephants dont play chess, no reason to
  assume they are not intelligent.
• Emphasis on kind of behaviour exemplified by
  elephants, rather than on more abstract human
  behaviours (e.g. games, speech recognition,
  problem solving).

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• A Creature must cope appropriately and in a
  timely fashion with changes in its dynamic
  environment.
• A Creature should be robust with respect to its
  environment minor changes in the properties of
  the world should not lead to total collapse of
  the Creatures behaviour rather one should only
  expect a gradual change in the capabilities of
  the Creature as the environment changes more and
  more.
• A Creature should be able to maintain multiple
  goals and, depending on the circumstances it
  finds itself in, change which particular goals it
  is actively pursuing thus it can both adapt to
  surroundings and capitalize on fortuitous
  circumstances.
• A Creature should do something in the world it
  should have some purpose in being.

88

• Set of principles (Brooks, 1991)
• The goal is to study complete integrated
  intelligent autonomous agents.
• The agents should be embodied as mobile robots
  situated in unmodified worlds found round
  laboratory. (embodiment).
• Robots should operate under different
  environmental conditions - e.g. in different
  lighting conditions, when sensors and actuators
  drift in calibration (situatedness).
• Robots should operate on timescales commensurate
  with timescales used by humans (situatedness).

89
Hybrid Control Architectures
90
Robot Architecture
how much / how do we represent the world
internally ?
As much as possible.
SPA architecture

• Sense -- plan -- act design of Shakey and the
  Stanford Cart
• Some tasks do require a deliberative approach,
  i.e., reasoning about the world.
• Current robots incorporate both reaction and
  reasoning. (sometimes termed hybrid systems)

91
Standard Approach - 1
world
world
perception
cognition
action
92
Standard Approach - 2

• Agent design we have been considering
• Sequential flow
• Percepts are obtained from sensors in world
  (somehow)
• Get a logic-based or formal description of
  percepts
• E.g., wumpus world percepts
• We apply search operators or logical inference or
  planning operators
• General (replaceable) formal goal
• Arrive at some operator or operator sequence
• Apply that operator sequence to world (somehow)

93
Taxonomy of Control Architectures hybrid

• A variety of different approaches have been tried
  for implementing the sense-plan-act control cycle
• These approachescan be categorised as
• model-based
• reactive
• hybrid

model based
reactive
behavior based
Three-Layer Architecture is an example of hybrid
system
94
Examples of Hybrid Approaches

• The SSS three-layer architecture
• the servo-subsumption-symbolic architecture
  combines Brooks architecture with a lower-level
  servo control level and a higher-level symbolic
  system Connell

• Fuzzy logic and neural network controllers
• fuzzy logic rule-base(s), neural network(s) and
  combinations of both take inputs from sensors and
  process the data to generate output to actuators

95
Learning Approaches

• Traditional learning techniques
• rather than attempt to predefine and predict a
  symbolic model of the real-world, the robot
  learns how to operate and how to behave by
• supervised learning
• desired output is known for each set of input
  settings (e.g. ANNs)
• reinforcement learning
• learning by trial and error through performance
  feedback
• Evolutionary algorithms
• using genetic algorithms to find good network
  weights
• significant problems with evolving real solutions
  in reasonable time on current mobile robot
  hardware

96
Control Problems for an autonomous robot

• Rapidly changing boundary conditions
• Real-time response
• Information collected over noisy channels

97
The Control Cycle

• A fundamental methodology derived in the early
  days of robotics from engineering principles is
  the sense-think-act cycle
• the principle is to continuously attempt to
  minimise the error between the actual state and
  the desired state
• based on control theory

sense
98
Model Based

• A symbolic internal world-model is maintained
• the sub-tasks are decomposed into functional
  layers
• similar to classical artificial intelligence
  approach

sense
modelling
planning
task execution
motor control
99
Problems with Models

• An adequate, accurate and up-to-date model must
  be maintained at all times
• this is very difficult in practice!
• suppose, for example, the sensors detect an
  object that we have not got a symbol for (a novel
  object)
• A model-based system is extremely brittle
• if one of the functional layers fails (e.g.
  hardware problems, software bugs), then the whole
  system fails
• Significant processing power is required
• maintaining the model takes time, so slow
  responses!?
• Despite much effort, little progress was made!

100
Reactive Controllers

• In order to try to overcome the shortcomings of
  model-based robots, modern approaches have
  centred predominantly on simple reactive systems
  with minimal amounts of computation
• model-free systems
• More correctly, the models are simple and
  implicit
• the systems do not use symbolic models but, for
  example, a rule-set which tells a robot how to
  react to a corner when following a wall may be
  considered to be a simple, implicit model
  fragment
• it implicitly encodes assumptions about the
  environment

101
Behavior Based

• The control system is broken down into horizontal
  modules, or behaviors, that run in parallel
• each behavior has direct access to sensor
  readings and can control the robots motors
  directly

sense
102
Three-layer architectures (TLA)
103
Three-layer architectures (TLA)

• Response to subsumption, simultaneously and
  independently developed by gt3 groups
• TLA design seems to implicitly
• Agree that different processing models are needed
  to react to events on different time scales
• Agree with loose asynchronous interfaces
• Disagree with the infinite regression of layers
• Disagree with the subsumption mechanism itself --
  i.e. overriding of inputs/outputs
• But is this the essence of subsumption?

104
the role of state

• SPA
• Uses extensive internal state
• Plans slowly and infrequently
• Gets into trouble when its internal state loses
  synchronicity with the world
• Reactive
• The World is its Own Best Model
• No internal state
• Tight sensor to actuator coupling
• Runs headlong into the problem of extracting
  state information from the world using sensors
• Hybrid/Three Layer
• Cant we all just get along?

105
the three-layer architecture

• Consists of (surprise!) 3 layers
• Reactive layer (Controller)
• Stateless, sensor-based
• Short time scale actions
• Glue Layer (Sequencer)
• Has a memory of the past
• Selects primitive behaviors for Controller
• Planning Layer (Deliberator)
• Plans for the future
• Time-consuming operations (search, complex
  vision, etc.)

106
Behavior Advantages

• It supports multiple goals and is more efficient
• there is no functional hierarchy between layers
• one layer does not call another layer
• each layer can work on different goals in
  parallel
• communication between layers is achieved via
  message passing which need not be synchronised
• The system is easier to design, debug and extend
• each module can be designed and tested
  individually
• The system is robust
• if one module fails, e.g. wander, then other
  layers, e.g. avoid obstacles, still function and
  behave correctly

107
Behavior Limitations

• It is extremely difficult to implement plans
• in pure form a behaviour-based robot has no
  memory (not even an internal state memory) and so
  is unable to follow an externally specified
  sequences of actions
• It can be very hard to predict how a large number
  of multiple behaviours may interact
• emergent behaviour is the term given to
  unexpected behaviour that comes about through
  these interactions
• sometimes it is useful, sometimes it is not!
• The robot can get trapped in a limit cycle
• trapped in a dead-end, repeatedly turning left
  then right

108
The new approach

• A robust layered control system based on task
  (activity) achieving
• What is the layer?
• A class of behavior
• Requirements
• Multiple goals
• Multiple sensors
• Robustness
• Extensibility

109
Conclusions Implications

• A kind intelligent action can be generated
  without
• Specific logical representation
• Logical reasoning or search
• Intelligence without typical representation
• Can planning, and other traditional AI goals be
  met?

110
Background

• Agent an entity with domain knowledge, goals
  and actions.
• Chess playing programs
• Sojourner Mars Exploration Robot
• Autonomous Agent uses sensors and effectors to
  independently achieve goals.
• Mobile Robots and Vehicles

111
Box-Pushing Task

• Consider the following task design a
  behavior-based robot to push boxes
• What should the design look like?
• What are the behaviors?
• How should they be combined?

112
Behaviors for Box-Pushing Task

• FINDER
• Goal Locate an object that might be a box
• Strategy Wander around till the sonars detect an
  object
• Priority Lowest (default behavior)
• PUSHER
• Goal Push an object
• Strategy Move forward while bumped and not stuck
• Priority Override FINDER when active
• UNWEDGER
• Goal Recover from stalled situations where
  PUSHER failed
• Strategy Turn and move forward to open space
• Priority Highest (override all other behaviors)

113
Control Flow Among Behaviors
114
Recycling Task

• Consider designing a behavior-based robot to
  recycle soda cans into trash bins
• What sensors would you use?
• What features would you design?
• What kinds of behaviors would be necessary?
• Dont peek at Connells paper yet!

115
Behaviors for Recycling

• Find soda cans
• Pick up a soda can
• Navigate the environment
• Avoid obstacles
• Find trash cans
• Head for home

116
HERBERT A Coke Can Collecting Robot

• Herbert was designed by Jonathan Connell for his
  Ph.d. thesis at MIT
• Herbert was one of the first demonstrations of
  behavior-based robots
• This robot demonstrated that a distributed
  colony of behaviors could be combined to do a
  nontrivial robotics task
• There is no central coordinator or master
  process underlying Herbert

117
Strategies for Combining Behaviors

• Subsumption
• One behavior subsumes another (overrides it)
• Easy to implement
• Combination may result in thrashing or jerky
  motions
• Bitwise
• Each behavior modifies action command bits
• Vector sum (schema approach)
• Each behavior proposes a certain motion vector
• The motion vectors are combined through vector
  addition
• The resultant vector is the direction of motion
  of the robot
• Smooth combination of behaviors

118
Conflicting Behaviors
Find box
Avoid
Wall
119
Bitwise Combination of Behaviors
ACTION COMMAND 1 BYTE
1
1
0
0
1
0
0
1
Turn fast
Turn left
Turn right
Turn med.
Trans. fast
Trans. slow
Back
Forward
Each behavior modifies certain fields of the
action command logical BIT and logical BIT OR.
120
Vector Sum Combination of Behaviors
Potential field motion planning obstacles are
repulsive , goals are attractive
GOAL
AVOID
OBSTACLE
MOVE to GOAL
121
Limitations of Behavior-Based Robots

• No easy way to incorporate global knowledge
  (symbolic maps, rules etc.)
• Hardwired behaviors -- robot cannot adapt to new
  unforeseen situations
• Lacks a planning/reasoning component -- cannot
  predict consequences of actions
• Extensions
• New behaviors can be learned using neural
  networks and reinforcement learning
• Global knowledge and planning achieved using a
  higher level deliberative system on top of
  behavior-based system

122
A Layered Robot Navigation Architecture
Sensor Reports
Planning and Execution Layer (POSMDP)
Neural Net Features
Action Commands
Action Reports
Local Occupancy Grids
Behavior-based Layer
Motor Commands
Raw Sensor Values
123
Limitations of Reactive Systems

• Action computed depends on current sensor values
  only
• State history is not captured
• Hard to write complex monolithic reactive systems
  
• Solution
• Decompose overall system into a number of
  behaviors
• Use small amounts of state information as
  memory
• Many robots have been successfully built using
  this approach

124
Hybrid Architecture
symbolic
Control values
sensors
actuators
125
Hybrid architecture for navigation
126
Future Work

• Short Term
• Galloping of Legged Robots
• Humanoid Robots for Soccer
• Long Term
• Creation of highly skilled Robots
• Search and Rescue Operations

127
Research Problems
128
Vision

• Fully Colored Environment
• Teams (Red Dark Blue)
• Identify Teammates
• Ball (Orange)
• Goals (Light Blue)
• Poles
• 6 combinations of Yellow, Pink, and Green
• Localization

129
Vision

• YUV color model
• Y represents luminance
• U and V represents chromacity
• Color classification algorithm
• 20 images used for training
• Convergence in less than one hour
• High accuracy

130
Localization

• Bayesian Probabilistic Localization
• Bayes Theorem
• P(SiO) P(Si) P(OSi) .
• ?j P(Sj) P(OSj)
  
• Movement Algorithm
• P(SiM) ?j P(Sj) P(Sj ? SiM)

131
RoboCup Agenten

• PS Anwendungen der agentenorientierten
  Programmierung
• Vortrag von Martin Lötzsch und Matthias Jüngel
• loetzsch_at_informatik.hu-berlin.de
• juengel_at_informatik.hu-berlin.de

132
Ideas

• Evolve state machines
• Learn State Machines
• Combinational (mv, fuzzy,arithmetic logic)
  functions are special cases of state machines
• They can be learned using Constructive Induction
• formal optimal construction methods based on
  logic synthesis programs which are very efficient
• Neural Nets can be included in layers

133
Questions ?
134
SUMMARY continues

• Robotics is challenging field for to reasons
• First , it requires hardware(sensors and
  effectors) that actually work, a real challenge
  for mechanical engineering
• Second, robots have to work in the physical
  world, which is more complex than most of the
  simulated software worlds that we have used for
  our examples in other chapters
• But modern autonomous robots with sophisticated
  sensors and effectors provide a challenging
  testbed for determining what it takes to build an
  intelligent agent

135
Summary

• FIRA
• Since 1996
• MiroSot, NaroSot, KheperaSot, RoboSot
• RoboCup
• Since 1997
• Simulation league, Small size league,Middle size
  league, Sony legged robot league

There is a similarity to international boxing.
136
Summary

• Summary of this lecture
• background
• the behaviour based approach
• Brooks assumptions about mobile robot design
• subsumption architecture
• levels of competence
• example competences
• layers of control
• subsumption
• structure of layers
• extensions finite state machines
• Next lecture
• Brooks subsumption architecture practice

137

• 7. SUMMARY
• The Physical World
• Industry applications
• Body with rigid links connected to each other by
  joints
• Sensors like vision, force, tactile, sonar
• The problem of moving a complex-shaped object(
  i.e., the robot and anything it is carrying)
  through a space with complex-shaped obstacles is
  a difficult one. The mathematical notation of
  configuration space provides a framework for
  analysis.
• Cell decomposition and skeletonization methods
  can be used to navigate through the configuration
  space. Both reduce a high dimensional, continuous
  space to a discrete graph-search problem.
• Some aspects of the world, such as the exact
  location of a bolt in the robots hand, will
  always be unknown. Fine-motion planning deals
  with this uncertainty by creating a sensor-based
  plan that will work regardless of exact initial
  conditions.
• Uncertainty applies to sensors at the large scale
  as well. In the landmark model, a robot uses
  certain well-known landmarks in the environment
  to determine where it is, even in the face of
  uncertainty.
• If a map of the environment is not available,
  then the robot will have to plan its navigation
  as it goes. Online algorithms do this. They do
  not always choose the shortest route, but we can
  analyze how far off they will be.

138
Conventional Approach vs. Behavior-Based Robots
(Brooks)
new AI
139
Reading
Autonomous Robot Teams in Dynamic and Uncertain
EnvironmentsProf. Manuela Veloso, Dr. Tucker
Balch, and Dr. Brett BrowningCarnegie Mellon
University
R. A. Brooks, A Robust Layered Control System
for a Mobile Robot, Cambrian Intelligence, The
MIT Press
J. O. Gray, D. G. Caldweel, Advanced Robotics
Intelligent Machines R. A. Brooks, Cambrian
Intelligence, The MIT Press
140
Subsumption Architecture
Book Computational Principles of Mobile Robotics
Papers Achieving Artificial Intelligence
through Building Robots
Famous paper, read the original!
Gregory Dudek and Michael Jenkin
Rodney Brooks

• A Robust Layered Control Systemfor a Mobile
  Robot
• Rodney A. Brooks
• IEEE Journal of Robotics and AutomationVol.
  RA-2, No. 1, March 1986

141
links

• Walking machines
• http//www.fzi.de/ids/WMC/OtherWM.html
• http//www.plustech.fi
• http//www.ai.mit.edu/projects/leglab/robots/robot
  s.html
• AI
• http//www.ai.mit.edu/
• http//www.ai.mit.edu/projects/cog/motor_video.htm

142
Sources

• Rodney Brooks
• Maja Mataric
• Nilssons book
• Jeremy Elson
• Norvigs book, chapter 2. Good. Stimulus-Response
  Agents
• English PH.D thesis, recent
• Jon Garibaldi
• Prof. Bruce Donald, Changxun Wu, Dartmouth
  College
• Leo Ilkko
• Prof. Manuela Veloso, Dr. Tucker Balch, and Dr.
  Brett BrowningCarnegie Mellon University
• Rabih Neouchi , Donald C. Onyango and Stacy F.
  President
• Axel Roth
• Ramon Brena Pinero ITESM
• Rhee, Taik-heon, Computer Science Department,
  KAIST
• Brian R. Duffy, Gina Joue
• Jon Garibaldi , De Montfort University
• Lucy Moffatt, Univ of Sheffield
• Yorick Wilks, Computer Science Department,
  University of Sheffield

Cecilia Laschi, Connell, Brooks Maja
Mataric Nilsson book Jeremy Elson Norvigs
book English PH.D thesis, recent Rhee, Taik-heon,
Computer Science Department, KAIST Axel
Roth Ramon Brena Pinero ITESM Rabih
NeouchiDonald C. Onyango and Stacy F. President