the nature of intelligent decision support systems adam maria gadomski [email protected]...

The Nature of Intelligent The Nature of Intelligent Decision Support SystemsDecision Support SystemsThe Nature of Intelligent The Nature of Intelligent Decision Support SystemsDecision Support Systems

Adam Maria GadomskiAdam Maria [email protected]

1997 ENEA copyright

IDSIDSSSIDSIDSSS

Workshop” Intelligent Decision Support Systems for Emergency Management”, Halden, 20-21 October 1997

p. 2Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

IDSSsIDSSs ENEA, ERG-ING-TISGI, 97

• Contexts of IDSSs

• Theoretical Background

• Technologies and Examples

• Contexts of IDSSs

• Theoretical Background

• Technologies and Examples

Contexts of IDSSs

• Internet Contexts

• Application Context

• RTD Context

33

IDDS - Internet Context (Alta Vista, Infoseek)

Concept number of documents

• DSS (various types)............................… 10 000• DSS for Emergency Management ....……………….....…...... 200 • Operator Support Systems.................... 40

• IDSS ........................................... 100

• Decision-Making Model ....................….. 1900• Multi Agent Systems (MAS) ...............…. 1300• Intelligent Agents ..............................….. 5100• Knowledge Based & Expert Systems ..............................….. 7000

44

IDSSsIDSSs ---- Applications Context

Health

Business

Informatin acquisition

(Internet, Data Mining)

IDSS

Servicies & Public Administration

Industry

Operatorslevel

Manageriallevel

Routine

Routine

Emergency

Emergency

Emergency

Manageriallevel

Emergency

Emergency

55

IDSSsIDSSs ---- RTD Context

Information Systems & Data Processing

Numerical Simulation & Optimization

Knowledge Based & Expert Systems

Logic & Meta programming

Multi-Agent & Intelligent Agent Technologies

Neuro-Fuzzy Technologies

IDSS

INTERESTS and EXPANTIONCognitive sciences & philosophy

66

IDSSsIDSSs ---- Historical Context

1986 - Paradigms for Intelligent Decision Support, David D. Woods in "Intelligent Decision Support in Process Environments" (E. Hollnagel, editor); Springer-Verlag.

...Advances in AI are providing powerful new computational tools that greatly expand the potential to support cognitive activities in complex work environments (e.g., monitoring, planning, fault management, problem solving). The application of these tools, however, creates new challenges about how to "couple" human intelligence and machine power in a single integrated system that maximizes joint performance.

7777

IDSSsIDSSs ---- First ConclusionsHistorical Context

1988, APPROACHES TO INTELLIGENT DECISION SUPPORT,. Editor: R.G. Jeroslow, Georgia Institute of Technology, Atlanta, GA B. Jaumard, P.S. Ow and B. Simeone, A.

1990, Model of Action-Oriented Decision-Making Process: Methodological Approach, A.M.Gadomski, Proceedings of the "9th European Annual Conference on Human Decision Making and Manual Control", CEC JRC Ispra.

88

IDSSsIDSSs ---- First ConclusionsHistorical Context

K. Sycara, Utility theory in conflict resolution P.S. Ow and S.F. Smith, Viewing scheduling as an opportunistic problem-solving process S. De, A knowledge-based approach to scheduling in an F.M.S. T.L. Dean, Reasoning about the effects of actions in automated planning systems D.P. Miller, A task and resource scheduling system for automated planning F. Glover and H.J. Greenberg, Logical testing for rule-base management J.N. Hooker, Generalized resolution and cutting planes D. Klingman, R. Padman and N. Phillips, Intelligent decision support sytems: A unique application in the petroleum industry K. Funk, A knowledge-based system for tactical situation assessment R.R. Yager, A note on the representation of quantified statements in terms of the implication operation L.D. Xu, A fuzzy multiobjective programming algorithm in decision support systems S.D. Burd and S.K. Kassicieh, A Prolog-based decision support system for computing capacity planning From APPROACHES TO INTELLIGENT DECISION SUPPORT, .1988.

99

IDSSsIDSSs ---- First Conclusions

LIST OF QUESTIONS:

Why - emergency management?

Why - cognitive sciences ?

Why - advanced technologies ?

Why - now ?

Let’s go to experience-based and theoretical explanations

1010

IDSS IDSS --- Emergency Management

Characteristics of emergency / crisis domains

Characteristics of emergency managers (IDSS users)

Information available for emergency managers

Characteristics of decisions

1111

IDSSs IDSSs --- Emergency Management

Characteristics of emergency / crisis domains: industrial distributed infrastructure emergencies, it covers high-risk industrial plants accidents, industrial territorial disasters and calamites. In general, it is referred to a high risk, complex domain not formally structured, such as ports, territory with population, airport infrastructure, railways node, oil pipes systems, chemical industry, etc. and to adequate human organizations which contribute as executors and partners in emergency management. Especially - multi-events emergencies where previously prepared plans have to be changed or realized under unexpected conditions.

1212


Characteristics of emergency managers

They have: qualitative weakly structured knowledge about emergency domain, semi-formal knowledge about competencies of their own organization and other potential partners of emergency managing. They have a strong managerial skill, direct human assistants, an access to different experts and to information about the emergency and resources state.They need to cooperate with other emergency managers.They work under stress. They are not computer specialists.

1313


Information available: frequently, limited access to information, information is not complete, uncertain, on different levels of details, too much or too dense various information, difficult or time consuming access to specially requested data.

Characteristics of decisions : must be made under time and resources constrains. Every decision depends on risk evaluation and manager competencies. It is focused on what to do in emergency domain (not only how to do), who should intervene and who should serve as an expert. Planned and just activated actions can be not efficient and can require immediate modifications. Erroneous human decision can be cause of serious and essential losses. 1414

IDSSs - Theoretical Background


Industrial Emergency Management

decision-making model.

passive DSS and active DSS, i.e. Intelligent Decision Support System

architectures & intelligent agents

demo-prototypes

IDSSs IDSSs ENEA, ERG-ING-TISGI, 97


Industrial Emergency

A state of risk and/or losses generation:

a) which is over the level accepted by local

administration

b) which is caused by an industrial accidentManagement

A control of autonomous functional units by task communication in

order to achieve an expected goal in the predefined domain.


IDSS IDSS - ManagementManagement ENEA, Gad, 97

risk

losses

An qualitative indicator of the current state of physical objects proportional to the probability of an event which may generate losses, and to the value of the maximal losses could be caused to this object by such event .* Risk value can be assessed by event specialists or obtained from experts during knowledge acquisition.* Risk value depends on many attributes of the risk objects and attributes of its environment.

An qualitative/quantitative indicator of death, injury,destruction in human, economical, cultural and ecological/environmental sense


IDSS IDSS - - ManagementManagement ENEA, Gad 97

Emergency domain

control of (human) autonomous functional units,

afu,by comands which activate afu according to

emergency plans. or include specific tasks.autonomous functional units

autonomous functional units:

fire brigades,

police,

...

are characterized by competence (types of interventions), and access to information

sourcesgoal a state of the domain which emergency managers intend to obtain

(consider most important).

domain


IDSS ------------------IDSS ------------------ ENEA, ERG-ING-TISPI, 97

Emergency

manager

Emergency

manager

Emergency

manager

Experts Executor 1

(afu)

Executors N

(afu)

. . .

different roles

Emergency

Supervisor

EMERGENCY DOMAIN

cooperation

DOMAIN OF ACTIVITYDOMAIN OF ACTIVITY

OF EMERGENCYOF EMERGENCY

MANAGERMANAGER

DOMAIN OF ACTIVITYDOMAIN OF ACTIVITY

OF EMERGENCYOF EMERGENCY

MANAGERMANAGER

. . .

coordination

taskstasks

tasks

cooperation


actions

IDSS IDSS ENEA, ERG-ING-TISGI, 97


decision-making model

Passive DSS and active DSS, i.e. Intelligent Decision Support System

abstract intelligent agents

demo-prototypes

p. 20Some references: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

IDSS IDSS ENEA, ERG-ING-TISGI, 97

Definitions

Decision-making (d-m) is a mental activity implied by the necessity of a choice either • without known criteria or • without known alternatives.

Decision - a result of the choice.

reasoning pathcriticalnode

alternatives

d-mdata decision

??

??

decision

2121

IDSSIDSS ENEA, A.M.Gadomski, 97

decision-making model

Requires definitions of a reasoning mechanizm and the following relative concepts:

--

I - InformationI - Information

P - PreferencesP - Preferences

K - KnowledgeK - Knowledge

Decision (intervention)

domain

DDDD

Decision (intervention)

domain

DDDD

2222

IDSSIDSS ENEA, A.M.Gadomski, 97

Simplified action-oriented decision-making model

Let’s assume:

IIyy = K = Kii ( I ( Ix x ); ); oror K Kii: I: Ix x IIyy IIyy = K = Kii ( I ( Ix x ); ); oror K Kii: I: Ix x IIyy

I represents states/situation/changes of the decision domain, DD

Ki represents an inference association on DD

Ko represents an available operation on DD

AAy y represents an action on DD.

AAyy = K = Koo ( I ( Ix x ); ); oror K Koo: I: Ix x AAyy AAyy = K = Koo ( I ( Ix x ); ); oror K Koo: I: Ix x AAyy

2323

IDSSIDSS ENEA, A.M..Gadomski,

97

Simplified decision-making model

IIinin’ = P ( I’ = P ( Iin in ;I;I ); ); oror P: ( I P: ( Iin in ;I);I) IIinin’’ IIinin’ = P ( I’ = P ( Iin in ;I;I ); ); oror P: ( I P: ( Iin in ;I);I) IIinin’’

P represents a preference relation on DD.

IIin in denotes the currently preferred state of DD, it can

be called intention, max. intention can be called goal.

I I denotes current state of DD.

A Preference depends on the parameter IM :

P( X;I ): If intention_is X and I IM then intention_is Y

what is equivalent to the sentence:

In the state of DD from the class IM, Y is_better then X .2424


97


In such manner we can construct reasoning pathes on the sets of Preferences and Knowledge.

In such manner we can construct reasoning pathes on the sets of Preferences and Knowledge.

In the reasoning processes modelling,

P, KP, Ki i , K, Koo can be, in natural way, represented by rules and operations (algorithms) on the level of a DD model, i.e. they are referred to classes of information employed in the model.

2525


97


K1K3

K4K2

A1

An example of the interference path

K9

A2

K5

K6

Decisional node

Here, we may demostrate that for the decison-making we need

or new information or new preferences or new knowledge.

I

2626

Decision-making (d-m) is a mental activity implied by the necessity of choice either without known criteria or without known alternatives.

• The criteria are meta-preferences• The alternatives are possible actions


97


K6

Decisional node

A1

A2

IxIx

= K6 ( I= K6 ( Ix x ););A1A1

A2A2 ]1st type of Decision-Making rules ( meta-preferences):

mP: ( if mP: ( if A1A1 AX andAX and A2 A2 AY; Ix IM then A2 A2 ) 2727


97

mP: ( if mP: ( if A1A1 AX andAX and A2 A2 AY; Ix IM thenthen A2 A2 )

where AX , , AY are classes of actions of the

decision-maker, and IM is a class of the states of DD.

In such conceptualization, IDSS has to have a fixed base of mP rules, such as (in informal way):

if is a fire then activation of fire-men is better than activation of police station.

2828


97

Passive DSS and active DSS, i.e. Intelligent Decision Support System

Passive classical DSSs provides information

Active Intelligent DSSs suggest possible actions (knowledge) and inform about used criteria (preferences).

2929

• Unfortunately, their application requires from

their users continous learning and training to

which typical emergency managers are not

enough motivated

• Large part of the user decisions relies on the

choice of the concrete button from menubars or

menutools being parts of a visualized

hierarchical menu structures (menu-driven

paradigm)

Passive DSS gives data and tool choice

for decision making.

Passive DSS

3030

EMERGENCY MANAGER

Computer network

EMERGENCY DOMAIN

( Human)

Human Organization

cooperation

Interventiondecisions Continuous

monitoring

Images,MeasuredData

Data request

dialoguemenu-driven

Computer specialists

Assistance of

DATA BASES MANAGEMENT SYSTEMSFunctional algorithmsGeographical DBDangerous Materials DBEmergency organiz. DBPlannes, Instructions DB

PassiveDECISIONSUPPORTSYSTEM

DSS

(Information System)

ENEA, A.M.Gadomski,97

3131

dataacquisition

. PASSIVE DSS.

Why Intelligent DSS ?

IDSSs are expecially important when:

the amount of information necessary for the management is so large, or

its time density is so high, that the probability of human errors during

emergency decision-making is not negligible

the coping with unexpected by managers (and DSS designer)

situations requires from the managers the remembering, mental

elaboration and immediate application of complex professional knowledge,

which if not properly used, causes fault decisions.

3232

INTELLIGENT DECISION-SUPPORT SYSTEM - OVERVIEW

link to computer networks

Interventions, decisions

Continuousmonitoring

Emergency Domain

dialogue,suggestions,explanations

IDSS (Artificial Intelligent Agent )

data flowon requestt

Decision support is based on:Informationcurrent data on Em.Domainand Em. OrganizationKnowledge:rules, instructions, procedures, plansPreferencesrisk criteria, role criteria,resource criteria

Human Organizations

Emergency Management Staff

Information system

Continuousmonitoring

amg,94

( Human Agents )

MIND

Adam M. Gadomski, 1995

ENEA

3333

IDSS - Domains of interventionsIDSS - Domains of interventions ENEA, A.M.Gadomski,97

Intelligent Decision Support System

Suggested intervention

Suggested executors

Suggested

experts

Suggested request of information

Suggested cooperation


How to do?

The IDSS should based on:• application of a generic ideal model of decision

maker (his role)

• its decomposability into human and computer

decision-makers

Ideal Managermodeling

IDSS Human Manager

decomposition

Interface

3535

Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

p.36

Why Intelligent Agent Technology (IAT)?IAT offers various reasoning tools to support classical passive menu driven DSSs to be “intelligent”. Specific advantage is the autonomy of intelligent agents in task execution. Intelligent agent has capabilities to: information filtering and interpretation according to the manager role and situation model. It may suggest new goals, alternative decisions or elaborate plans of the intervention. Intelligent agent can use various Artificial Intelligent methods which enable to copy with uncertain and incomplete data, qualitative reasoning, constrains satisfactions an so on. Its flexibility, modularity and reusing depend strongly on the type of architecture accepted. An “organization” of task-dependent intelligent agents can be considered as the kernel of IDSS.Now, a multiagent architecture based on a repetitive structure, the possibility of (user friendly) modifications of the specific emergency domain and user roles, are considered as a key research fields in the IDSS development.

ENEA, A.M.Gadomski,97

IDSS - Frame System IDSS - Frame System

Emergency Domain

Real-timeData Bases

Real-timeImage Bases

Passive DS (decision support)

Simulators ofmain events

Plumedispersion

Firepropagation

Explosionconsequences

MMI

Passive DS

ComputerNetworkInterface

USER

Intelligent Kernel

EvaluatorAgent

DiagnosticAgent

CommunicationAgent

Action ChoiceAgent

CommonKnowledgetools

amg

ExternalManualSymulatorSupport

EmergencyorganizationData Bases

GIS

DATA BASES SYS

A.MGadomski

ENEA

3737

IDSS developmentIDSS developmentA.MGadomski

ENEA

Symptoms Actions

Toxic Substancesand Risk Industries

DataBases

ConsequencesAnalysis

Algorithms

InterventionProcedures

Diagnosticmodule

Decision-makingmodule (agent)

Whathappens

What will happenor could bappen

What to do

GEOGRAPHICAL DATABASES

EVENT

Predictivemodule

CONSEQUENCES

3838


IDSSIDSS

First type architecture

Second type architecture

a structural intelligence of multi-agent system

or behavioral intelligence of multi-functionalsystem

Here we can have

?

4040

p41. Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

Abstract Simple Agent : ActionData acquisition

NewInformation

Decision

Goal

DS

PS KS

DS

PS

KS

Domain System:a representation of Physical Domain ofActivity

Agent PreferenceSystem

Agent KnowledgeSystem

Physical Domainof Activity

State- Information

IDSS - STRUCTURAL INTELLIGENCE

p.42 Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

Real Domain

Domain System

PreferencesSystem

KnowledgeSystem

DS

PS KS

DS

PS KS

DS

PS KS

DS

PS KS

DS

PS KS

Second meta level

First meta level

DS

PS KS

inf

inf

goal

inf

Abstract Simple Agent

A Multi-level Abstract Intelligent Agent Architecture

act.

IDSSIDSS ENEA, 97

p.43Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

Domain-Representation Module

Suggested Interventionsamg

Cause of

emergency-eventModification of DomainModel

Preferences System

Possible

consequences

Assessment max.negative consequences

Generation ofIntervention-goal

Knowledge System

Action planning

Decision-Making

Availableprocedures

information

goal

Decision-Making Module based on Abstract-Intelligent-Agent Architecture

Final decision

Domain-Representation Module

Suggested Interventionsamg

Explosion in

chemical plant Plant object in the state of losses generation

Preferences System

Possibleconsequences:

Assessment of large scale human losses

Choice of Intervention-goal: EVACUATION

Knowledge System

Evacuation planspreparation

plans selectionaccording to strategies criteria

Availableprocedures

information

goal

Decision-Making Module: An Example

-toxic plumegeneration- local damage- impact area

of Evacuation

Final decision

p44 Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

IDSS - Domains of interventionsIDSS - Domains of interventions ENEA, A.M.Gadomski,97

Intelligent Decision Support System

Suggested intervention

Suggested executors

Suggested

experts

Suggested request of information

Suggested cooperation


IDSSIDSS ENEA, 97

NEW ONTOLOGY

( DSS PROBLEMS ARE RECONCEPTUALIZED)

STRONG INTERDYSCIPLINARY APPROACH

NEW TECHNOLOGIES

(REASONING TOOLS and INTELLIGENT AGENTS ARCHITECTURE)

NEW POSSIBILITES OF UNCERTEN, COMPLEX AND HIGH RISK DOMAIN MANAGEMENT.

CONCLUSIONSCONCLUSIONS

4646

IDSS REFERENCES

Some references and other meta-information you can find on my Home-Pages:

wwwerg.casaccia.enea.it/ing/tispi/gadomski/gadomski.html

4747

the nature of intelligent decision support systems adam maria gadomski [email protected]...

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