Advanced Multi-Agent-System for Security applications

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Advanced Multi-Agent-System for Security
applications

• Dr. Reuven Granot
• Faculty of Science and Scientific Education
• University of Haifa, Israel
• rgranot_at_smile.net.il

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Robotic activities at University of Haifa

• The new Faculty of Science and Scientific
  Educations mission is focused toward
  interdisciplinary research and education.
• The robotic activities have their background in
  the initiative of the Research Technology Unit
  at MAFAT Israel MoD were I served in the last
  decade as Scientific Deputy.
• We have concentrated interest and research in
  Multi Agent Supervised Autonomous Systems (Tele
  robotics), while continuing steady support of the
  Manual Remote operations in different combat
  environments.

3
Overview

• The Tele-robotics paradigm.
• The Control Agent as the implementation of the
  relevant behavior.
• Human Robot Interaction.
• JAUS and Real time Control System Architectures.
• Evaluation of concepts using Small Size Scaled
  Model.
• Video demonstration.

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The Need of Unmanned Systems
Regarding Defense and Security the need is well
recognized to perform tasks that are

• DDD
• Dull
• Dirty
• Dangerous
• Distant at different scale
• Macro space,
• Micro telesurgery, micro and nano devices

All these applications require an effective
interface between the machine and a human in
charge of operating/ commanding the machine.
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The Tele-robotics paradigm
Telerobotics is a form of Supervised Autonomous
Control.
A machine can be distantly operated by

• continuous control the HO is responsible to
  continuously supply the robot all the needed
  control commands.
• a coherent cooperation between man and machine,
  which is known to be a hard task.

Supervision and intervention by a human would
provide the advantages of on-line fault
correction and debugging, and would relax the
amount of structure needed in the environment,
since a human supervisor could anticipate and
account for many unexpected situations.
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Remote Controlled vehicles in combat environment

• RC is still preferred by designers
• Simple, but not practical for combat environment
  because the human operator
• is very much dependent upon the controlled
  process
• needs long readjustment time to switch between
  the controlled and the local (combat) environment.

• The state of the art of the current technology
  has not yet solved the problem of controlling
  complex tasks autonomously in unexpected
  contingent environments.
• dealing with unexpected contingent events
  remains to be a major problem of robotics.
• Consequence A human operator should be able to
  interfere remains at least in the supervisory
  loop.

The needed control metaphor Human Supervised
Autonomous
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Why Security Systems should make use of the
Telerobotic paradigm

• Require
• Reduced number of human operators.
• HO should control simultaneously several systems.
• High flexibility and factor of surprise.
• HO should be capable to deal with other duties in
  somehow relaxed mode of operation.
• Means
• Distributed systems.
• Coherent collaboration of human intelligence with
  machine superior capabilities.
• Make the machine an agent in human operators
  service.

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The spectrum of control modes.
A telerobot can use

• traded control control is or at operator or at
  the autonomous sub-system.
• shared control the instructions given by HO and
  by the robot are combined.
• strict supervisory control the HO instructs the
  robot, then observes its autonomous actions.

Solid line major loops are closed through
computer, minor loops through human.
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Human Robot Interaction

• In supervised autonomously controlled equipment,
  a human operator generates tasks, and a computer
  autonomously closes some of the controlled loops.
  
• Control bandwidth
• Robot SW high
• Human response slow

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The Agent

• An agent is a computer system capable of
  autonomous action in some environments.
• A general way in which the term agent is used is
  to denote a hardware or software-based computer
  system that enjoys the following properties
• autonomy agents operate without the direct
  intervention of humans or others, and have some
  kind of control over their actions and internal
  state
• social ability agents interact with other agents
  (and possibly humans) via some kind of
  agent-communication language
• reactivity agents perceive their environment,
  (which may be the physical world, a user via a
  graphical user interface, or a collection of
  other agents), and respond in a timely fashion to
  changes that occur in it
• pro-activeness agents do not simply act in
  response to their environment they are able to
  exhibit goal-directed behavior by taking the
  initiative.

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Agents are not Objects

• Agents may act inside the robot software to
  implement behaviors
• Feedback controllers
• Control subassemblies
• Perform Local Goals/ tasks

• Differ from Objects
• autonomous, reactive and pro-active
• encapsulate some state,
• are more than expert systems
• are situated in their environment and take action
  instead of just advising to do so.

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The Control Agent

• The agent is a control subassembly.
• It may be built upon a primitive task or composed
  of an assembly of subordinate agents.
• The agent hierarchy for a specific task is
  pre-planned or defined by the human operator as
  part of the preparation for execution of the
  task.
• The final sequence of operation is deducted from
  the hierarchy or negotiated between agents in the
  hierarchy.

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Agent control loop

• agent starts in some initial internal state i0 .
• observes its environment state e, and generates a
  percept see(e).
• internal state of the agent is then updated via
  next function, becoming next_(i0, see(e)).
• the action selected by agent is
• action (next(i0, see(e))))
• This action is then performed.
• Goto (2).

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Human Operator

• Monitors the activities and the performance of
  the assembly of agents.
• Responsible for the completion of the major task
  (global goal)
• may interfere by sending change orders.
• emergent (executed immediately)
• as is ordered or
• normal
• checked by the interface agent
• which negotiates execution with other agents in
  order to optimize execution performance
• Conflict resolution algorithm
• defined as default, or
• defined by the human operator in its change order
  or
• suggested to the operator by a simplified
  decision support algorithm.

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Man Machine Interface is still one of the most
recognized technology gaps/ challenges of semi
autonomous systems.
Intelligent Control will be achieved using
Intelligent Agents.
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Interface Agent

• A software entity, which is capable to represent
  the human in the computer SW environment.
• It acts on behalf of the human
• Follows rules and has a well defined expected
  attitude/ action.
• May be instructed on the fly and may receive
  during mission updated commands from the human
  operator.

We need to build agents in order to carry out
the tasks, without the need to tell the agents
how to perform these tasks.
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Task-level supervisory control system block
diagram.
HO
raw robot outputs
formatted outputs
control signals
Controlling agent
Task level controller
Robot hardware
desired tasks

• An agent can be considered as a control
  subassembly, also called behavior.
• The feedback is given to the agent in both
  processed and raw form.

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RCS Embeds a hierarchy of agents within a
hierarchy of organizational units Intelligent
Nodes or RCS_Nodes.
JAUS
From M. W. Torrie
A hierarchy of Commanders different resolution
in space and time
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RCS_Node
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Agents in Behavior Generation hierarchy

• Tasks are decomposed and assigned in a command
  chain.
• Actions are coordinated
• Resources are allocated as plan approved.
• Tasks achievements are monitored (VJ)
• Execution in parallel

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Evaluation of concept

• As an emerging scientific field, the field of
  robotics (like AI) lacks the metrics and
  quantifiable measures of performance.
• Evaluation is done against common sense and
  qualitative experimental results.
• the legitimacy of transfer of conclusions over
  different scale applications or different
  implementations remains to be decided by specific
  designs.

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Small Size Scaled Model

• The implementation differs by mechanical,
  perceptual and control elements from the full
  scale application.
• It still may help to identify unusual situations
  which the software agent must be capable to deal
  with.
• Full scale machines may be tested only at field
  ranges, which are time consuming and very
  expensive.
• A small scale model may be tested in office
  environment, enabling the software developers to
  shorten test cycles by orders of magnitude.

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D9 Bulldozer

• A good starting project
• earthmoving tasks are loosely coupled with
  locomotion tasks.
• earthmoving tasks are not really simple and
• locomotion tasks are not really complicated.

• The operator has very limited information about
  his surroundings or machine performance.

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Expected situations

• The bulldozer moves forward placing the blade too
  low
• The human decides the blade should be placed
  higher
• Command issued lift the blade.
• experiencing too much power to enable earth
  moving forward
• the human operator would prefer to withdraw and
  attack the soil from a new position behind
• the human operator is distant
• the bulldozer is close to the ditch
• a better practice would be to first complete the
  maneuver.
• Bulldozer using Fuzzy Control decides to perform
  the better practice and withdraws only after the
  maneuver is completed.

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The Model
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Drawbacks

• DC motors are of relatively weak power and small
  dimensions
• which reduce our choice of suitable sensors.
• therefore, we implemented
• simulated beacon
• CMUcam placed above - is a simulation of the
  "Flying Eye" concept of FCS
• We were unable to control the speed of the
  vehicle.
• We had to restrict our testing to control
• the vehicle rotation around a perpendicular axis
• to manipulate the raising of the blade.

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autonomous-bulldozer\robot.WMV
4 min
autonomous-bulldozer\robot.mpg
3 min
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Conclusions

• Security systems should use the advantages of the
  Telerobotic paradigm in order to perform complex
  tasks with few operators.
• Agents are implementations of behaviors.
• Behavior based Architectures are better
  implemented using the Multi Agent technology.
• Human Machine Interaction is better implemented
  through the Interface Agent.
• Machine Intelligence may be achieved implementing
  agents into the JAUS/ RCS Model Architecture.

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Some References

• NATO Core Group in Robotics (members)  2005
  Bridging the Gap in military Robotics (to be
  published as NATO document) www.fgan.de/natoeuro/
  EuropeanRobotics-Publication.pdf
• Sheridan, T.B., Telerobotics, Automation, and
  Human Supervisory Control, MIT Press, 1992
• Granot R, Agent based Human Robot Interaction. at
  IPMM 2005, Monterey, California, 19-25 July 2005
• Granot, R., Feldman, M., 2004 "Agent based Human
  Robot Interaction of a combat bulldozer."
  Unmanned Ground Vehicle Technology IV, at SPIE
  Defense Security Symposium 2004 (formerly
  AeroSense) 12-16 April 2004, Gaylord Palms Resort
  and Convention Center Orlando, Florida USA, paper
  number 5422-25
• Granot, R., 2002 "Architecture for Human
  Supervised Autonomously Controlled Off-road
  Equipment.  Automation Technology for Off-road
  Equipment", ASAE, Chicago, Il, USA, July 26-28,
  2002, p24
• Meystael M. A. and Albus, S. J. "Intelligent
  Systems. Architecture, Design, and Control", John
  Wiley Sons Inc., 2002
• Michael Wooldridge, "Intelligent Agents Theory
  and Practice" http//www.csc.liv.ac.uk/mjw/pubs/k
  er95/

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Contact

• Dr. Reuven Granot
• rgranot_at_smile.net.il
• granot_at_math.haifa.ac.il
• University of Haifa 
• Faculty of Science and Scientific Education
• Mount Carmel Haifa  31905 ISRAEL    Office  
  972 4-828-8422 cellular 972 52 341-0193
• http//math.haifa.ac.il/robotics
• This presentation is downloadable from
  http//math.haifa.ac.il/robotics/Projects/MyPapers
  /RISE2006.ppt