Fire Safety

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Re-configurable and Scalable Distributed Systems
with Autonomous Agents


Mohsen. A. Jafari, Ph.D. Thomas O. Boucher, Ph.D.
Dept. of Industrial Systems Engineering Rutgers
University
This work has been partially sponsored by a grant
from the National Science Foundation.
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Team Members

• Ardavan Amini
• Peng Zhao
• Leila Zia

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Outline of the Talk

• Vision statement
• Example
• General Overview of our system
• General Application areas
• Another Example
• Implementation Enabling Technologies
• Background Review

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Vision Statement

• To develop a systematic and unified control
  framework for re-configurable and scalable
  multi-agent distributed systems, which can be
  mapped to real life applications in
  manufacturing, business and transportation.

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Example

• Think of robot agents which carry letters between
  floors in a building, and they also do some other
  functions or tasks.
• Agent A1 carries letters between floors 0 2.
• Agent A2 carries letters between floors 1- 5.
• Requests are made by another agent, say B.
• Stairs and elevators can be used.
• Robots are subject to failures, e.g., failure of
  legs, hands (penalty).
• Robots may have more than one way of doing the
  same thing, but with different cost, e.g., a
  robot with two hands.
• Robots are not pre-programmed. They only have
  some basic skills or functions. Basic functions
  have some initial cost, but changes with
  different circumstances.
• They should learn how to use these skills to
  solve a problem (synthesize a controller).
• Some bad states or conditions exist.

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Agents Decision Making Process

• Each agent go through these stages
• Bidding
• Control synthesis and schedule optimization at
  local level
• Commitment execution

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Bidding

• Service requestor will broadcast the request,
  and service providers will respond with a plan
  and cost value.
• Two possibilities
• Agent has no experience.
• Synthesize a solution from some default initial
  condition and estimate a cost, no failures, no
  faults,
• Agent has some earlier experience.
• Bid value and actual cost and solution history
  exist.
• (Challenge) Diagnosis for cost differential
  between bid and actual can be established. Cost
  differential can be due to unexpected internal
  failures, different initial conditions,
  unexpected environmental adverse conditions,
• Expected cost can be established.
• Expected plan can be established.

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Synthesis Using Search Techniques
Issues Arcs are dependent on agents skill
options Experience from its own internal
behavior, failures, etc. Its perception of the
environment.
Agent A1 has 4 basic functions f1, f2, f3, f4.
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Agents Formal Definition
Mapping between basic functions and resources
Basic functions
Resources
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Agents Formal Definition

• At any time agents conditions can be defined
  by
• current state (status, location, )
• available resources and basic functions
• WTL work-to-do list, flags, initiators, and
  responses
• schedule

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Agents Perception of its Environment

• Problem
• To build an automaton which describes the
  agents view of its environment.
• Issues and Challenges
• Agent only sees from its environment a set of
  labels (e.g., sensory feedback). It can use these
  together with its own actions to synthesize its
  perception model. The cause and effect
  relationship between agents actions and the
  sensory feedback may not be obvious.
• Some Possible Approaches
• Decision Markov Processes reinforcement
  learning
• Hidden Markov Chains
• Language theory,

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Failures and Faults

• Initially agent may or may not be aware of its
  failure or fault conditions, internally or
  externally. Learned by experience.
• Failure detection and diagnosis routine computes
  the sequence of actions and conditions which lead
  to such a failure condition.
• Knowledge of failures and faults alter the cost
  of doing basic functions.

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General Overview of our System

• A distributed system of groups of agents where
• Each group has a manager in charge of bookkeeping
  and dispatching of new jobs to the group.
• Agents can be service providers or service
  requestors
• They are somewhat Intelligent, so that each
  agent individually can determine its sequence of
  actions (tasks) according to its local
  specifications and according to the specification
  of the overall system

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General Overview of our System

• Agents are somewhat autonomous, but can be
  colonized
• Agents possess a set of basic functions or
  services, agents are not pre-programmed.
• Agents communicate and negotiate with each other
• Service providers compete with each other for
  providing services to the service requestors
• New agents can plug in into the system, or
  existing agents can plug out of the system

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General Application areas

• Manufacturing systems
• Agents Machine, robots, AGVS, human/machines,
  sensors, cells,
• Business Systems
• Agents Human/machines, software agents,
  business units
• Transportation systems
• Agents Vehicles, traffic agents, sensors and
  signals,
• etc.

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Another Example

• Process Control
• System A chemical reaction where temp., humidity
  and pressure must be controlled.
• Real time Control Local controllers, outside of
  our control scope, belong to system environment.
• Supervisory Control
• Shut down
• Turn on or off of switches for fans to control
  temp. or humidity.
• System reset, etc.
• Desirable behavior
• System should not explode. (a bad state)
• System should work with minimal cost.

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Example

• Problem
• Design multi-functional agent(s) which (among
  other tasks) can supervise this system according
  to the local and global specifications, including
  minimal cost.
• Issues and Challenges
• At different times, different agents can be
  assigned to control of this system (depending on
  some sort of bidding mechanism). These agents may
  also be responsible for doing many other
  functions.
• None of these agents are pre-programmed for this
  function. They only know about their basic
  functions and also possess a knowledge base of
  what they have already done.
• Agents could learn by exploring, from
  experience, etc.
• Each basic function of an agent is associated
  with an initial cost. The cost could change due
  to environmental factors.

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Example A Solution Strategy

• Learn Normative Model (simulate).
• May take a while and perhaps after a number of
  faulty loops a normative behavior with a
  reasonable cost can be obtained.
• Cost modeling of sequence of actions with some
  sort of discount factors may be needed.
• To avoid local minimal traps, explore can be
  used.
• Add some disruptive conditions (due to internal
  and external factors) to the above model.
• Build perception of the environment.
• Learn about disruptive behavior, explore, fault
  diagnosis.
• Update the normative model. Repeat.

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Fault Analysis Issues Challenges

• Embedded fault detection and diagnosis mechanism
• Run-time fault states (e.g., deadlocks or
  undesirable states for one or more agents) must
  be detected at control planning stage and
  prevented from happening at the execution stage
  in future.
• Fault states may or may not be known in advance,
  but in any case the means of detecting or
  preventing them can not be established at any
  design stage.
• Fault state detection and prevention in
  distributed systems require communication between
  agents. Local information on non-local fault
  states must be combined with information from
  other agents.
• Fault state prevention may require event
  disabling or enforcement at some earlier stages.

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Fault Analysis Issues Challenges

• Events can be observable or unobservable.
  Failures are unobservable events. This leads to
  non-deterministic behavior.
• Some sensory information available.
• Sensor readings can be affected by more than one
  component within the system (agent).
• Environmental factors could affect the sensory
  information.
• Failure detection/diagnosis from available sensor
  readings (indirect info) and observable events.

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Fault Detection/Diagnosis in Distributed Systems
Issues Challenges

• Distributed information on faults.
• Who should initiate the fault detection/diagnosis?
  
• What information should be exchanged?
• How the exchange should proceed? Who should be
  involved?
• The information is exchanged in real time but
  may be incomplete, delayed and erroneous.
• How the same fault should be avoided in the
  future?
• What information needs to be kept for avoidance
  in the future?

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Existing Models

• Forward Diagnosis The hypothesis updated
  according to the current events until a
  conclusion is made
• Propagation Model
• Event Based Model
• Probabilistic Model
• Backward Diagnosis When there is a fault,
  backtrack the event sequence to find the sequence
  of events and conditions leading to the failure
• Fault Tree Analysis
• Back Firing Timed Petri Nets

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An Example

• System has three components a heater, a
  thermocouple and a window.
• The heater is controllable. It has two normal
  states on and off.
• The window is uncontrollable.
• The thermocouple can measure the temperature of
  the room.
• The objective is to check if the heater works
  properly.

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Behavioral Model for the example
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State Space Model
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Reduced State Space Model
This model together with a probabilistic
reasoning model can then be used to diagnose the
failure.
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Agent Model
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Layers of the system
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Agent Enabling Technologies (Contd)

• Component Based Automation (CBA) by Siemens.
• IEC 61499
• Standard for distributed systems
• Low level communication
• Not fully developed / not adopted.

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IEC - The Basic Function Block
Agent Enabling Technologies (Contd)
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IEC - A Composite Function Block
Agent Enabling Technologies (Contd)
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IEC - An Example of Using Function Block for
Industry Control
Agent Enabling Technologies (Contd)
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A background review Theory of Agents

• The term "agent" is used (and misused) to
  describe a broad range of computational
  entities. This tends to obscure the differences
  between radically different approaches
• Some agents performs tasks individually ...
  Others need to work together
• Some are mobile ... some static
• Some learn and adapt ... others don't
• An agent is a reusable software/hardware
  component that provides controlled access to
  (shared) services and resources.

Michael Weiss - MITEL Corp
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A background review Theory of Agents

• In the literature we find three types of
  agents
• reactive or reflex agents,
• deliberative or goal-oriented agents, and
  collaborative agents.
• Reactive agents respond only to external
  stimuli and the information available from
  their sensing of environment. They show
  emergent behavior, which is the result of the
  interactions of these simple agents.

Brooks, R. A., A robust layered control system
for a mobile robot, IEEE Journal of Robotics and
Automation, vol. 2, pp. 14-23, 1986.
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A background review Theory of Agents

• Goal-directed agents have domain knowledge
  and the planning capabilities necessary to
  take a sequence of actions in the hope of
  reaching or achieving a specific goal.
• Collaborative agents work together to solve big
  problems. Each individual agent is autonomous.
  These agents can solve problems by
  collaboration and synergy. Problems will be
  parsed into smaller chunks that can be solved
  by a modular approach. The approach is based on
  specialization of agent functions and domain
  knowledge.

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A background review Theory of Agents

• Bratman has introduced agents with beliefs,
  desires and intentions (BDI) as a form of
  collaborative agents. What an agent believes to
  be true will be the basis for all of its
  reasoning, planning and actions. When an agent
  reasons about the state of the world (beliefs)
  and its desires (goals) it must decide what
  course of action to take (intensions).

Bratman, M. E., Intensions, Plans, and
Practical Reasons, Cambridge, MA Harvard
University Press, 1987.
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Agents and AI
A background review Theory of Agents

• The idea is to design intelligent agents to
  achieve specific tasks automatically.
• An intelligent agent works based on events, i.e.
  if any specific event happens the corresponding
  agent will react accordingly in order to satisfy
  a predetermined objective by taking some special
  actions under different conditions (states).
• When an event occurs the corresponding agent must
  recognize it and respond to it.

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A background review Theory of Agents
Source H. Nwana, BT Laboratories, U.K., Software
Agents An Overview, Knowledge Engineering
Review, Vol. 11, No 3, pp.1-40, Sept 1996.
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Agent Enabling Technologies (Contd)

• FIPA / KQML (Foundation for Physical Intelligent
  Agents Knowledge Query and Manipulation
  Language)
• Included in CORBA specification.
• A more practical form of KQML documented in
  evolving FIPA standard.
• Based on the linguistic concept of "speech acts"
  (Searle).
• Speech acts come in different flavors directives
  ("I command"), interrogatives ("I ask"),
  commissives ("I promise").
• Most common ask/tell, query/reply,
  offer/accept/reject (FIPA).
• Pragmatic extensions to speech acts
  facilitation, registration, errors.

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Centralized vs. Distributed Fault Analysis
Centralized Distributed
Advantage More accurate Communication and calculation are distributed Only process local information and neighbor's information
Disadvantage Need a special diagnosis agent, and will be the bottle neck of the system Difficult to be reconfigured, not suitable for a plug and play system Cannot outperform centralized one
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Example of Distributed Diagnosis (Deadlock
Detection)
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Higher Order Deadlock Detection
Check Knowledge Base
Check Knowledge Base
WFG A1 ? A2
WFG A2 ? A3
Check Knowledge Base
Check Knowledge Base
WFG A3 ? A2
WFG A2 ? A1
WFG A1 ? A2
WFG A2 ? A3
Deadlock Detected Add to knowledge Base
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Deadlock Detection Mitchell-Merritt Algorithm
M1
M2
M1
M2
M1
M2
M3
M1
M2
M1
M2
M1
M2
Deadlock detected
M3
M3
M3
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Future Work

• Complete underlying models and algorithms for
  synthesis and scheduling
• Prototyping
• Extension of the models to a multi-layered system