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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…