Transcript IDA: Intelligent Decision Advisor (IDSS) for Emergency

IDSS
The Nature of Intelligent
Decision Support Systems
Adam Maria Gadomski
[email protected]
 1997 ENEA
copyright
Workshop” Intelligent Decision Support Systems for Emergency
Management”, Halden, 20-21 October 1997
IDSSs
ENEA, ERG-ING-TISGI, 97
• Contexts of IDSSs
• Theoretical Background
• Technologies and Examples
p. 2
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
Contexts of IDSSs
• Internet Contexts
• Application Context
• RTD Context
3
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 ....................…..
• Multi Agent Systems (MAS) ...............….
• Intelligent Agents ..............................…..
• Knowledge Based &
Expert Systems ..............................…..
1900
1300
5100
7000
4
IDSSs
----
Applications Context
Emergency
Health
Routine
Emergency
Operators
level
Emergency
Routine
Industry
IDSS
Business
Managerial
level
Emergency
Managerial
level
Emergency
Servicies &
Public
Administration
Informatin
acquisition
(Internet, Data
Mining)
5
IDSSs
----
RTD Context
INTERESTS and EXPANTION
Cognitive sciences
& philosophy
Information Systems
& Data Processing
Numerical Simulation &
Optimization
IDSS
Knowledge Based &
Expert Systems
Multi-Agent &
Intelligent Agent
Technologies
Logic & Meta
programming
Neuro-Fuzzy
Technologies
6
IDSSs
----
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.
7
IDSSs
---- First
Conclusions
Historical
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.
8
IDSSs
---- First
Conclusions
Historical
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.
9
IDSSs
---- First Conclusions
LIST OF QUESTIONS:
Let’s go to experience-based
and theoretical explanations
10
IDSS --- Emergency Management
Characteristics of emergency / crisis domains
Characteristics of emergency managers (IDSS users)
Information available for emergency managers
Characteristics of decisions
11
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.
12
IDSSs --- Emergency Management
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.
13
IDSSs --- Emergency Management
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
14
losses.
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
p. 15
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSSs
ENEA, ERG-ING-TISGI, 97
 Industrial Emergency Management
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 accident
Management
A control of autonomous functional units by task
communication in order to achieve an expected goal in the
predefined domain.
p. 16
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
risk
- Management
ENEA, Gad, 97
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.
losses
An qualitative/quantitative indicator of death,
injury,destruction in human, economical, cultural
and ecological/environmental sense
p. 17
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
- - Management
ENEA, Gad 97
domain
Emergency domain
autonomous functional units:
fire brigades,
police,
...
control of (human) autonomous functional
units, afu,
by comands which activate afu according to
emergency plans. or include specific tasks.
autonomous functional units
are characterized by competence (types of interventions), and access to information
sources
goal a state of the domain which emergency managers intend to obtain
(consider most important).
ENEA,A.M.Gadomski
p. 18
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
------------------
DOMAIN OF ACTIVITY
OF EMERGENCY
MANAGER
ENEA, ERG-ING-TISPI, 97
Emergency
Supervisor
coordination
different roles
Emergency
manager
cooperation
tasks
Experts
Emergency
manager
cooperation
tasks
tasks
Executor 1
(afu)
Emergency
manager
...
Executors N
(afu)
...
actions
EMERGENCY
DOMAIN
ENEA,A.M.Gadomski
p. 19
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
ENEA, ERG-ING-TISGI, 97
 Industrial Emergency Management
 decision-making model
 Passive DSS and active DSS, i.e.
Intelligent Decision Support System
 abstract intelligent agents
 demo-prototypes
p. 20
ENEA,A.M.Gadomski
Some references: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
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.
alternatives
reasoning path
?
data
critical
node
d-m
?
decision
decision
21
IDSS
ENEA, A.M.Gadomski, 97
decision-making model
Requires definitions of a reasoning mechanizm
and the following relative concepts:
-
I - Information
P - Preferences
K - Knowledge
Decision
(intervention)
domain
DD
22
IDSS
ENEA, A.M.Gadomski, 97
Simplified action-oriented
decision-making model
Let’s assume:
Iy = Ki ( Ix );
or
Ki: Ix  Iy
Ay = Ko ( Ix );
or
Ko: Ix  Ay
I represents states/situation/changes of the decision domain, DD
Ki represents an inference association on DD
Ko represents an available operation on DD
Ay represents an action on DD.
23
IDSS
ENEA, A.M..Gadomski, 97
Simplified decision-making model
Iin’ = P ( Iin ;I ); or P: ( Iin ;I)  Iin’
P represents a preference relation on DD.
Iin
denotes the currently preferred state of DD, it can be called
intention, max. intention can be called goal.
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 .
24
IDSS
ENEA, A.M..Gadomski, 97
Simplified decision-making model
In the reasoning processes modelling,
P, Ki , Ko 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.
In such manner we can construct reasoning pathes
on the sets of Preferences and Knowledge.
25
IDSS
ENEA, A.M..Gadomski, 97
Simplified decision-making model
An example of the interference path
A2
I
K1
K9
K3
K2
A1
K4
K6
Decisional
node
K5
Here, we may demostrate that for the decison-making we need
or new information or new preferences or new knowledge.
26
IDSS
ENEA, A.M..Gadomski, 97
Simplified decision-making model
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
= K6 ( I );
]
A2
A1
A2
A1
x
Decisional
node
1st type of Decision-Making rules ( metapreferences):
mP: ( if A1  AX and A2  AY;
 IM then A2 )
K6
Ix
Ix
27
IDSS
ENEA, A.M..Gadomski, 97
mP: ( if A1  AX and A2  AY;
Ix  IM then 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.
28
IDSS
ENEA, A.M..Gadomski, 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).
29
Passive DSS
• 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.
30
.
PASSIVE DSS
ENEA, A.M.Gadomski,97
EMERGENCY DOMAIN
Intervention
decisions
EMERGENCY
MANAGER
Continuous
monitoring
Images,
Measured
Data
Data request
dialogue
menu-driven
( Human)
cooperation
Assistance of
Human Organization
DATA BASES
MANAGEMENT SYSTEMS
Functional algorithms
Geographical DB
Dangerous Materials DB
Emergency organiz. DB
Plannes, Instructions DB
Computer specialists
DSS
Passive
DECISION
SUPPORT
SYSTEM
(Information
System)
data
acquisition
Computer network
31
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.
32
INTELLIGENT DECISION-SUPPORT SYSTEM - OVERVIEW
Decision support is
Emergency Domain
based on:
link to computer networks
Information
current data on Em.Domain
and Em. Organization
Knowledge:
rules, instructions, procedures,
plans
Preferences
risk criteria, role criteria,
resource criteria
amg,94
Continuous
monitoring
Emergency Management Staff
Information system
IDSS
MIND
(Artificial
Intelligent Agent )
dialogue,
suggestions,
explanations
data flow
on requestt
Continuous
monitoring
Human Organizations
( Human
Agents )
Interventions,
decisions
ENEA
Adam M. Gadomski, 1995
33
IDSS - Domains of interventions
ENEA, A.M.Gadomski,97
 Intelligent Decision Support System
Suggested cooperation
Suggested request of information
Suggested
experts
Suggested intervention
Suggested executors
p. 34
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
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 Manager
modeling
decomposition
IDSS
Interface
Human
Manager
35
ENEA, A.M.Gadomski,97
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.
p.36
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS -
Frame System
A.MGadomski
ENEA
amg
Emergency Domain
DATA BASES SYS
Simulators
of
main
events
Real-time
Image Bases
External
Manual
Symulator
Support
Real-time
Data Bases
Emergency
organization
Data Bases
Passive DS (decision support)
GIS
Plume
dispersion
Passive DS
Intelligent Kernel
Fire
propagation
Explosion
consequences
Evaluator
Agent
Action Choice
Agent
Common
Knowledge
tools
Diagnostic
Agent
Communication
Agent
M
M
I
USER
Computer
Network
Interface
37
IDSS development
Toxic Substances
and Risk Industries
DataBases
Symptoms
ENEA
Consequences
Analysis
Algorithms
EVENT
Diagnostic
module
A.MGadomski
Intervention
Procedures
CONSEQUENCES
Predictive
module
Decision-making
module (agent)
Actions
GEOGRAPHICAL DATABASES
What
happens
What will happen
or could bappen
What to do
38
IDSS
First type
architecture
Second type
architecture
p. 39
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
?
Here we can have
a structural intelligence of multi-agent system
or behavioral intelligence of multi-functional
system
40
IDSS - STRUCTURAL INTELLIGENCE
Physical Domain
of Activity
Abstract Simple Agent :
DS
PS
KS
Domain System:
a representation of
Physical Domain of
Activity
Data acquisition
New
Information
Agent Preference
System
Agent Knowledge
System
Action
DS
Decision
StateInformation
KS
PS
Goal
p41.
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
Real Domain
inf
Domain System
inf
act.
Abstract Simple Agent
inf
Preferences
System
Knowledge
System
goal
DS
DS
PS
First meta level
DS
DS
KS
PS
PS
KS
PS
KS
DS
KS
PS
DS
KS
PS
KS
Second meta level
A Multi-level Abstract Intelligent Agent Architecture
p.42
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
ENEA, 97
Domain-Representation Module
Cause of
emergency-event
Modification of Domain
Model
Final decision
information
Preferences System
Possible
consequences
Knowledge System
Assessment max.
negative consequences
Generation of
Intervention-goal
Action planning
Available
procedures
goal
Decision-Making
amg
Suggested Interventions
Decision-Making Module based on Abstract-Intelligent-Agent Architecturep.43
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
Domain-Representation Module
Explosion in
chemical plant
Final decision
Plant object in the state
of losses generation
information
Preferences System
Possible
consequences:
-toxic plume
generation
- local damage
- impact area
Knowledge System
Assessment of large
scale human losses
Choice of Intervention
-goal: EVACUATION
Evacuation plans
preparation
goal
plans selection
according to
strategies criteria
Available
procedures
of Evacuation
amg
Suggested Interventions
Decision-Making Module: An Example
p44
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS - Domains of interventions
ENEA, A.M.Gadomski,97
 Intelligent Decision Support System
Suggested cooperation
Suggested request of information
Suggested
experts
Suggested intervention
Suggested executors
p. 45
ENEA,A.M.Gadomski
Some References: [“TOGA Theory...”, A.M.Gadomski, ,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97]
IDSS
ENEA, 97
CONCLUSIONS

NEW ONTOLOGY
( DSS PROBLEMS ARE RECONCEPTUALIZED)


STRONG INTERDYSCIPLINARY APPROACH
NEW TECHNOLOGIES
(REASONING TOOLS and INTELLIGENT
ARCHITECTURE)
AGENTS

NEW POSSIBILITES OF UNCERTEN, COMPLEX AND HIGH
RISK DOMAIN MANAGEMENT.
46
IDSS REFERENCES
Some references and other meta-information you can find
on my Home-Pages:
wwwerg.casaccia.enea.it/ing/tispi/gadomski/gadomski.html
47