Decision Support Systems
Real World Applications
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The abstract problem
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Control personal has to manage a complex
system
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Identify problems
Understand the problems
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Evaluate problems
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Classify
Explain
Anticipate consequences
Solve the problems
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Generate a plan
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Take actions
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Why Agents?!
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Agents design advantages for control systems
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Easy design - Each agent corresponds to some
role in the system (very self explaining)
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Abstraction
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Task oriented
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Functions  object  agents
Basic and compound methods.
Social methods.
Knowledge based
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The expertise model can be improved
Reuse – Same role at different environment
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Why Agents?!
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Decision Support Systems
interact/replace human beings
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Decisions must be understandable to human,
therefore using agents will yield:
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better understanding of each role in the system
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Each role supports the humans
At any level of expertise
better understanding of the Logic and interactions
among the components
There already is a control structure
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Agents replace the existing structure
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Problems Characteristics
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A lot of input
Background work
Human decision maker at the end
Task oriented
Examples:
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Energy management
Traffic management
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Energy Management
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Power plants generate electricity
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Final consumption takes place far away
Many things can go wrong in the middle:
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Unpredictable problems:
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Predictable problems:
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Equipment damage
Disasters (winds, lightning)
Temperature changes
Overall demand changes.
Some damages effect quality while others deny
the service
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The Architecture
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Based on a network of a company in Spain
Networks are managed from a control room
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Information is sent to the control room
Protection equipment can be remotely operated
Field engineer operate in the field
The network consists of substations, and
each substation consists of:
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Lines
Breakers & switches
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May fire automatically, sending alarm messages
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The Goal
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Main Problem:
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Solution
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Usually caused by short circuits in the lines
Malfunctioning equipment may cause a chain
reaction that extends the area of effect
Isolating the effected area usually solves the
problem
The goal:
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Minimize the disconnected area
restore supply as soon as possible
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The electricity transport
management problem
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Control personal has to manage a complex
system - control the switches and breakers
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Identify malfunctioning in switches and breakers
Understand the problems
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Evaluate problems
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Classify - Diagnose the problem
Explain the alarm messages according to the diagnosis
Anticipate consequences that may cause expansion of
the area of effect
Solve the problems
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Generate a switching plan that isolates the area of effect
and restore supply to maximum number of customers
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The Multi-Agent Architecture
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Constraints:
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Existing expert systems
Existing configuration of the data transmission
 Two formats
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Non chronological alarm messages – NAM
Chronological alarm messages – CAM
Existing control structure
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The Multi-Agent Architecture
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Alarm Analysis Agents
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Replaces an existing expert system
Methods:
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Reads messages
Detects faults
Establishes hypotheses regarding the
malfunctioning equipment
Basic methods & compound methods
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Rule based
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The Multi-Agent Architecture
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Control System Interface Agent
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constitutes the application’s front end to the user
Basic methods:
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Acquires and distributes network data to other agents (formats
the message for use by other agents)
 Done using a hard-wired algorithm
Calculates the power distribution, given a certain state
 Done using a numerical simulator
A compound method which is used when a certain set of
messages arrive
A social method which generates classification with the help
of the alarm analysis agents
This agent wraps existing functionality
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Example of TMST
Messages
CSI
Information
Model
Disturbance Detection
Alarm Detection
Acquire Data
(direct algorithm)
Classify Situation
Alarm Classification
Coordinate classification
Alarm Analysis
Alarm
Agent Analysis
Agent
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Additional Agents
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Blackout Area Identifier
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Determines the results of a given scenario (network state
and faults)
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Service Restoration Agent
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Rule based
Proposes a switching plan given alarm messages and the
results of the diagnosis
User Interface Agent
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Serves as an interface between the multi-agent system and
the users for presenting data
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Browse through the lists of alarms
Display results of diagnosis along with explanations
Sets up guidelines for the other agents
Simulates the effect of a restoration plan
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Coordination
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Can be done with an acquaintance
model
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Frames that contain the methods that the
other agents can perform including:
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The types of the methods
The competence with which the method can be
applied
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Summary
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The energy transport problem is very
suitable for DSS
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Every agent decision may be explained to
the responsible engineer using the trace of
the reasoning methods
Problem definition fits into the abstract
problem definition
The multi-agent system managed to cope
with the existing constraints
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Road Traffic Management
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Traffic flows on public roads increase at
high rate
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Number of vehicles increase
Roads infrastructure cannot be expanded
Significant economic loses
Traffic Control Centers (TCC)
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In charge of managing urban transport
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Available Information
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Messages from human observers
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Devices
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Gal-Galatz
Policemen
TV cameras
Cellular phone
Sensors
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Loop detectors -Installed on strategic channels
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Speed - mean velocity of the passing vehicles
Flow - average number of vehicles per unit of time
Occupancy - average time that vehicles are spotted
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Available Control Devices
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Variable Message Sings (VMS)
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Installed above the road
(like those on the way to Tel-Aviv)
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Traffic signs (closed road sign)
Arbitrary message signs
Traffic lights
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Parameters of the traffic light can be modified
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Relative amount of green time
Overall length of a cycle
Order of traffic lights
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The Urban Highway Traffic
Control Problem
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system – Control the traffic lights and VMSs
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Identify and locate problematic situation
Understand the problems
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Evaluate problems
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Classify the cause of the problem (congestion/accident)
Explain the problem in terms of traffic flows
Anticipate consequences due to chain reactions of the
congestion
Solve the problems
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Generate a legal sign plan and/or traffic lights handling
plan, in order to eliminate or alleviate the congestion
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The Multi-Agent Architecture
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The structure of the system was dictated
by the way human operators worked
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Problem areas topology
All agents share the same architecture and
the same reasoning structure
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Their knowledge however, was based on the
specific problem area in their responsibility
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Basic Methods of the Agents
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Data abstraction
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Problem Type identification
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Calculate ‘the normal’ demand for a section of the network
 Based on temporal pattern (hour, day of week, events...)
Effect estimation
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Takes the data generated by the data abstraction method and
classifies the underlying problem
 Done by matching the data against problem scenario frames
Demand estimation
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Determines qualitative measure for different variables
Anticipates the effect of flows on a certain problem
 The state of the control devices
 Contribution of certain routes to the problem
Signal plan selection
Short term prediction estimation
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Calculates the effect of change in traffic flows
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Compound Methods
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Heuristic classification
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Problem solving method
 Acquires relevant information
 Problems type are matches upon the information
 The problems are integrated and refined
Contributor differentiation
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Determines how much a set of causes contributes to
a problem
 Identifies possible contributors
 Estimates each contributor
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Compound Methods
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Generate & Test
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Evaluates proposals generated by the basic method
until an adequate plan is found
Depends on outside constraints (coordination)
Local management
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Manages the network by integrating all the methods
 Identifies traffic problem
 Diagnoses its causes
 Generate a proper plan to overcome it.
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Coordination
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Problem areas are not disjoint
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Physical conflicts
Logical conflicts
Two coordination solutions
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Coordinator agent
Peer-to-peer communication
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Acquaintance model
 Does not represent information concerning method of
other agents
 Describes the resources that acquaintances require and
which effects they may have (on sections in the agent’s
problem area)
Local plans are sent to the relevant agents
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The agent with the most severe problem takes precedence
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Summary
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Once again a DSS is a very suitable
solution
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The traffic management problem fits the
abstract DSS problem
The DSS had to be based on existing
control engineer’s understanding of a
town’s traffic behavior
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Additional Potential Examples
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Intelligence Word
Medicine
Every other problem that fits that
abstract problem definition…
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