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 [email protected] Robotic activities at University of Haifa

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Advanced multi agent system for security applications l.jpg

Advanced Multi-Agent-System for Security applications

Dr. Reuven Granot

Faculty of Science and Scientific Education

University of Haifa, Israel

[email protected]

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Robotic activities at University of Haifa

  • The new Faculty of Science and Scientific Education’s 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 inMulti – Agent Supervised Autonomous Systems (Tele robotics), while continuing steady support of the Manual Remote operations in different combat environments.

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Overview l.jpg

  • 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 operator’s service.

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The spectrum of control modes. paradigm

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 paradigm

  • 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 paradigm

  • 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 paradigm

  • 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 paradigm

  • 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 paradigm

  • 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 paradigm

  • 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 paradigm 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 paradigm

  • 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 paradigm.


raw robot outputs

formatted outputs

control signals

Controlling agent

Task level


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 paradigm

Embeds a hierarchy of agents within a hierarchy of organizational units: Intelligent Nodes or RCS_Nodes.


From M. W. Torrie

A hierarchy of Commanders

different resolution in space and time

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Commanded Task (Goal) paradigm

Value Judgment

Perceived Objects & Events

Plan Evaluation

Situation Evaluation

Plan Results



Sensory Processing

World Modeling

Behavior Generation

Predicted Input


Knowledge Database

Commanded Actions (Subgoals)

Observed Input


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Agents in Behavior Generation hierarchy paradigm

  • 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 paradigm

  • 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 qualitativeexperimental 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 paradigm

  • 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 veryexpensive.

  • 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 paradigm

  • 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 paradigm

  • 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 paradigm

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Drawbacks paradigm

  • 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 paradigm

4 min


3 min

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Conclusions paradigm

  • 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 paradigmReferences

  • NATO Core Group in Robotics (members)  2005: Bridging the Gap in military Robotics (to be published as NATO document)

  • 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"

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Contact paradigm

Dr. Reuven Granot

  • [email protected]

  • [email protected]

    University of Haifa 

    Faculty of Science and Scientific Education

    Mount Carmel Haifa  31905 ISRAEL    Office   +972 4-828-8422 cellular +972 52 341-0193


    This presentation is downloadable from

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