1. The Nature of Intelligent Decision Support Systems Adam Maria Gadomski gadomski_a@casaccia.enea.it 1997 ENEA copyright IDSSIDSS Workshop Intelligent Decision Support Systems for Emergency Management, Halden, 20-21 October 1997

2. p. 2 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski IDSSs IDSSs ENEA, ERG-ING-TISGI, 97 Contexts of IDSSs Theoretical Background Technologies and Examples Contexts of IDSSs Theoretical Background Technologies and Examples

3. Contexts of IDSSs Internet Contexts Application Context RTD Context 3 3

4. 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 4 4

5. IDSSs IDSSs ---- Applications Context Health Business Informatin acquisition (Internet, Data Mining) IDSS Servicies & Public Administration Industry Operators level Managerial level Routine Emergency Managerial level Emergency 5 5

6. IDSSs IDSSs ---- 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 EXPANTION Cognitive sciences & philosophy 6 6

7. IDSSs 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. 77

8. 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 8

9. 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 9

10. IDSSs IDSSs ---- First Conclusions LIST OF QUESTIONS: Why - emergency management? Why - cognitive sciences ? Why - advanced technologies ? Why - now ? Lets go to experience-based and theoretical explanations 10

11. IDSS IDSS --- Emergency Management Characteristics of emergency / crisis domains Characteristics of emergency managers (IDSS users) Information available for emergency managers Characteristics of decisions 11

12. 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. 12

13. IDSSs 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

14. IDSSs 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 losses. 14

15. IDSSs - Theoretical Background p. 15 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski Industrial Emergency Management decision-making model. passive DSS and active DSS, i.e. Intelligent Decision Support System architectures & intelligent agents demo-prototypes

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

17. IDSS Management IDSS - Management 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 p. 17 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

18. IDSS Management IDSS - - Management 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 sources goal a state of the domain which emergency managers intend to obtain (consider most important). domain p. 18 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

19. 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 cooperatio n DOMAIN OF ACTIVITY OF EMERGENCY MANAGER DOMAIN OF ACTIVITY OF EMERGENCY MANAGER... coordination tasks cooperation p. 19 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski actions

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

21. 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 path critical node alternatives d-m datadecision ? ? 21

22. IDSS IDSS ENEA, A.M.Gadomski, 97 decision-making model Requires definitions of a reasoning mechanizm and the following relative concepts: - I - Information I - Information P - Preferences P - Preferences K - Knowledge K - Knowledge Decision (intervention) domain DD Decision (intervention) domain DD 22

23. IDSS IDSS ENEA, A.M.Gadomski, 97 Simplified action-oriented decision-making model Lets assume: I y = K i ( I x ); or K i : I x I y I y = K i ( I x ); or K i : I x I y I represents states/situation/changes of the decision domain, DD K i represents an inference association on DD K o represents an available operation on DD A y A y represents an action on DD. A y = K o ( I x ); or K o : I x A y A y = K o ( I x ); or K o : I x A y 23

24. IDSS IDSS ENEA, A.M..Gadomski, 97 Simplified decision-making model I in = P ( I in ;I ); or P: ( I in ;I) I in I in = P ( I in ;I ); or P: ( I in ;I) I in P represents a preference relation on DD. I in I 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 I M : P( X;I ): If intention_is X and I I M then intention_is Y what is equivalent to the sentence: In the state of DD from the class I M, Y is_better then X. 24

25. IDSS IDSS ENEA, A.M..Gadomski, 97 Simplified decision-making model In such manner we can construct reasoning pathes on the sets of Preferences and Knowledge. In the reasoning processes modelling, P, K i, K o P, K i, K o 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. 25

26. IDSS IDSS ENEA, A.M..Gadomski, 97 Simplified decision-making model K1 K3 K4 K2 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 26

27. 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 IDSS IDSS ENEA, A.M..Gadomski, 97 Simplified decision-making model K6 Decisional node A1 A2 Ix = K6 ( I x ); = K6 ( I x );A1A2 ] 1st type of Decision-Making rules ( meta- preferences): mP: ( if A1 AX and A2 A2 mP: ( if A1 AX and A2 AY; Ix IM then A2 ) 27

28. IDSS IDSS ENEA, A.M..Gadomski, 97 mP: ( if A1 AX and A2 mP: ( if A1 AX and A2 AY; thenA2 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

29. IDSS 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

30. 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 30

31. EMERGENCY MANAGER Computer network EMERGENCY DOMAIN ( Human) Human Organization cooperation Intervention decisions Continuous monitoring Images, Measured Data Data request dialogue menu-driven Computer specialists Assistance of DATA BASES MANAGEMENT SYSTEMS Functional algorithms Geographical DB Dangerous Materials DB Emergency organiz. DB Plannes, Instructions DB Passive DECISION SUPPORT SYSTEM DSS (Information System ) ENEA, A.M.Gadomski,97 31 data acquisition. PASSIVE DSS.

32. Why Intelligent DSS ? IDSSs are expecially important when: n 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 n 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

33. INTELLIGENT DECISION-SUPPORT SYSTEM - OVERVIEW link to computer networks Interventions, decisions Continuous monitoring Emergency Domain dialogue, suggestions, explanations IDSS (Artificial Intelligent Agent ) data flow on requestt Decision support is based on: Information current data on Em.Domain and Em. Organization Knowledge: rules, instructions, procedures, plans Preferences risk criteria, role criteria, resource criteria Human Organizations Emergency Management Staff Information system Continuous monitoring amg,94 ( Human Agents ) MIND Adam M. Gadomski, 1995 ENEA 33

34. IDSS - Domains of interventions IDSS - Domains of interventions ENEA, A.M.Gadomski,97 Intelligent Decision Support System Suggested intervention Suggested executors Suggested experts Suggested request of information Suggested cooperation p. 34 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

35. 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 IDSS Human Manager decomposition Interface 35

36. 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

37. IDSS - Frame System Emergency Domain Real-time Data Bases Real-time Image Bases Passive DS (decision support ) Simulators of main events Plume dispersion Fire propagation Explosion consequences MMIMMI Passive DS Computer Network Interface USER Intelligent Kernel Evaluator Agent Diagnostic Agent Communication Agent Action Choice Agent Common Knowledge tools amg External Manual Symulator Support Emergency organization Data Bases GIS DATA BASES SYS A.MGadomski ENEA 37

38. IDSS development A.MGadomski ENEA Symptoms Actions Toxic Substances and Risk Industries DataBases Consequences Analysis Algorithms Intervention Procedures Diagnostic module Decision-making module (agent) What happens What will happen or could bappen What to do GEOGRAPHICAL DATABASES EVENT Predictive module CONSEQUENCES 38

39. p. 39 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski IDSS First type architecture Second type architecture

40. a structural intelligence of multi-agent system or behavioral intelligence of multi-functional system Here we can have ? 40

41. 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 : Action Data acquisition New Information Decision Goal DS PS KS DS PS KS Domain System: a representation of Physical Domain of Activity Agent Preference System Agent Knowledge System Physical Domain of Activity State- Information IDSS - STRUCTURAL INTELLIGENCE

42. 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 Preferences System Knowledge System 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 goal inf Abstract Simple Agent A Multi-level Abstract Intelligent Agent Architecture act.

43. IDSS IDSS ENEA, 97 p.43 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski Decision-Making Module based on Abstract-Intelligent-Agent Architecture Final decision

44. Domain-Representation Module Suggested Interventions amg Explosion in chemical plant Plant object in the state of losses generation Preferences System Possible consequences: Assessment of large scale human losses Choice of Intervention -goal: EVACUATION Knowledge System Evacuation plans preparation plans selection according to strategies criteria Available procedures information goal Decision-Making Module: An Example -toxic plume generation - 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

45. IDSS - Domains of interventions IDSS - Domains of interventions ENEA, A.M.Gadomski,97 Intelligent Decision Support System Suggested intervention Suggested executors Suggested experts Suggested request of information Suggested cooperation p. 45 Some References: [TOGA Theory..., A.M.Gadomski,,AIA Proc.,!993,;[Gad. at al., TIEMEC95], [DiCostanzoat al.,TIEMEC97] ENEA,A.M.Gadomski

46. IDSS IDSS 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. CONCLUSIONS 46

47. 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