Decision Support Systems

1. Decision Support Systems Real World Applications

2. 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

3. 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

4. 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

5. Problems Characteristics • A lot of input • Background work • Human decision maker at the end • Task oriented • Examples: • Energy management • Traffic management

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. Example of TMST CSI Information Model Messages Disturbance Detection Alarm Detection Classify Situation Alarm Classification Acquire Data (direct algorithm) Coordinate classification Alarm Analysis Agent Alarm Analysis Agent

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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.

25. 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

26. 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

27. Additional Potential Examples • Intelligence Word • Medicine • Every other problem that fits that abstract problem definition…