1 Decision Support Systems Real World Applications.
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Transcript of 1 Decision Support Systems Real World Applications.
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Decision Support Systems
Real World Applications
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The abstract problem Control personal has to manage a
complex system Identify problems Understand the problems
Classify Explain
Evaluate problems Anticipate consequences
Solve the problems Generate a plan Take actions
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Why Agents?! Agents design advantages for control
systems Easy design - Each agent corresponds to
some role in the system (very self explaining) Abstraction
Functions object agents Task oriented
Basic and compound methods. Social methods.
Knowledge based The expertise model can be improved Reuse – Same role at different environment
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Why Agents?! Decision Support Systems
interact/replace human beings Decisions must be understandable to
human, therefore using agents will yield: better understanding of each role in the system
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 Agents replace the existing structure
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Problems Characteristics A lot of input Background work Human decision maker at the end Task oriented Examples:
Energy management Traffic management
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Energy Management Power plants generate electricity
Final consumption takes place far away Many things can go wrong in the middle:
Unpredictable problems: Equipment damage Disasters (winds, lightning)
Predictable problems: Temperature changes Overall demand changes.
Some damages effect quality while others deny the service
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The Architecture Based on a network of a company in Spain Networks are managed from a control
room 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: Lines Breakers & switches
May fire automatically, sending alarm messages
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The Goal Main Problem:
Usually caused by short circuits in the lines Malfunctioning equipment may cause a chain
reaction that extends the area of effect Solution
Isolating the effected area usually solves the problem
The goal: Minimize the disconnected area restore supply as soon as possible
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The electricity transport management problem Control personal has to manage a complex
system - control the switches and breakers Identify malfunctioning in switches and breakers Understand the problems
Classify - Diagnose the problem Explain the alarm messages according to the diagnosis
Evaluate problems Anticipate consequences that may cause expansion of
the area of effect Solve the problems
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 Constraints:
Existing expert systems Existing configuration of the data
transmission Two formats
Non chronological alarm messages – NAM Chronological alarm messages – CAM
Existing control structure
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The Multi-Agent Architecture Alarm Analysis Agents
Replaces an existing expert system Methods:
Reads messages Detects faults Establishes hypotheses regarding the
malfunctioning equipment Basic methods & compound methods
Rule based
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The Multi-Agent Architecture Control System Interface Agent
constitutes the application’s front end to the user Basic methods:
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 TMSTCSIMessages Information
Model
Disturbance Detection
Classify Situation Alarm ClassificationAlarm Detection
Acquire Data(direct algorithm)
Coordinate classification
Alarm AnalysisAgentAlarm Analysis
Agent
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Additional Agents Blackout Area Identifier
Determines the results of a given scenario (network state and faults)
Rule based Service Restoration Agent
Proposes a switching plan given alarm messages and the results of the diagnosis
User Interface Agent Serves as an interface between the multi-agent system and
the users for presenting data 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 Can be done with an acquaintance
model Frames that contain the methods that
the other agents can perform including:
The types of the methods The competence with which the method
can be applied
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Summary The energy transport problem is
very suitable for DSS 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 Traffic flows on public roads
increase at high rate Number of vehicles increase Roads infrastructure cannot be expanded
Significant economic loses Traffic Control Centers (TCC)
In charge of managing urban transport
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Available Information Messages from human observers
Gal-Galatz Policemen
Devices TV cameras Cellular phone
Sensors Loop detectors -Installed on strategic channels
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 Variable Message Sings (VMS)
Installed above the road (like those on the way to Tel-Aviv)
Traffic signs (closed road sign) Arbitrary message signs
Traffic lights Parameters of the traffic light can be
modified Relative amount of green time Overall length of a cycle Order of traffic lights
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The Urban Highway Traffic Control Problem system – Control the traffic lights and VMSs
Identify and locate problematic situation Understand the problems
Classify the cause of the problem (congestion/accident) Explain the problem in terms of traffic flows
Evaluate problems Anticipate consequences due to chain reactions of the
congestion Solve the problems
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 The structure of the system was
dictated by the way human operators worked
Problem areas topology All agents share the same architecture
and the same reasoning structure Their knowledge however, was based on
the specific problem area in their responsibility
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Basic Methods of the Agents
Data abstraction Determines qualitative measure for different variables
Problem Type identification 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 Calculate ‘the normal’ demand for a section of the network
Based on temporal pattern (hour, day of week, events...) Effect estimation
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
Calculates the effect of change in traffic flows
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Compound Methods Heuristic classification
Problem solving method Acquires relevant information Problems type are matches upon the
information The problems are integrated and refined
Contributor differentiation Determines how much a set of causes
contributes to a problem Identifies possible contributors Estimates each contributor
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Compound Methods Generate & Test
Evaluates proposals generated by the basic method until an adequate plan is found
Depends on outside constraints (coordination) Local management
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 Problem areas are not disjoint
Physical conflicts Logical conflicts
Two coordination solutions Coordinator agent Peer-to-peer communication
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 The agent with the most severe problem takes precedence
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Summary Once again a DSS is a very
suitable solution 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 Intelligence Word Medicine Every other problem that fits that
abstract problem definition…