22.12.95
Computer-Aided Emergency Management Training
Based on Abstract Intelligent Agent Model : TOGA Approach
Adam M. Gadomski, Claudio Balducelli
Italian Agency for New Technologies, Energy and the Environment
ENEA, C.R. Casaccia, Via Anguillarese 301, 00060 Rome, Italy
e-mail: gadomski_a, [email protected]
:
Abstract
For decision-makers, emergencies in the human environment are high risk situations. Emergency managers
have explicitly defined goals and relatively well distinguished physical domains. Their decisions require
routine, procedure-based interventions defined in concrete contexts of losses but they also involve
unforeseen scenarios requiring an intelligent and non conformistic reactions.
The cooperation among human agents is subordinated to commonly accepted goals and should be carried
out according to the explicitly adopted top preferences. In emergency procedures, the agent activity
domains include its physical environment, social environment and organizational (intervention) units. These
domains are divided among human agents according to their organizational roles.
Agent's autonomy is limited by some a priori established constrains and driven by less or more specific
directives. The manager-agent's problem is how to recognize the situation in a proper conceptualization
context and to choice actions or a reasoning activity adequate to the required general preferences and to the
available knowledge.
The above extremal conditions should be useful for the identification of a human cognitive architecture,
intelligent goal-oriented behaviors and causes of human mental errors.
The paper discusses the applicability of an abstract intelligent agent model to the design of Intelligent
Training Systems (ITS) and to the interpretation of trainee errors . The aim of these class of ITS is to teach
human decision-makers managerial and cooperation patterns for large scale emergency situations.
The general conceptualization framework employed in this study is the TOGA (Top-down Object-based
Goal-oriented Approach) meta-methodology which has been developed in ENEA since 1986.
The main top TOGA assumption is that every real-world human problem involves in its own solution either
an explicit or implicit model of the problem solver. In TOGA, an abstract intelligent agent (AIA) based on
IPK (Information, Preferences, Knowledge) architecture is proposed.
In frame of the AIA model, intervention-goal achieving, task execution and cooperation are represented.
Abstraction mechanisms of learning, discovery, and navigation between different levels of student metapreferences and meta-knowledge are analyzed.
Our illustrative examples are based on the results of the EU Environment project MUSTER (Multi-User
System for Training and Evaluating environmental emergency Response) with the test case related to an
oil-tanker explosion in the Genoa oil-port. Here, emergency managers participate in the computer supported
training of the cooperation during simulated emergency states.
Some patterns for the structuring of human agent domains of intervention are also presented. A
specialization of the AIA model to the representation of trainee-manager and human-tutor roles are
illustrated. The AIA model of a trainee, enables an explicit conceptualization and analysis of domaindependent managerial decision-making and interpretation of human errors. The training supervisor may
identify the causes of trainee improper decisions. In general, the errors are referred either to the emergency
domain or to the cooperation domain. Summarizing, using the abstract intelligent agent framework, in the
both cases, the errors are caused by:
- insufficient or false information and, in consequence, wrong situation-assessment
- insufficient or false knowledge
- inappropriate preferences hierarchy, which includes personal motivation criteria
- temporal physical and psychical stress.
1. Introduction
The paper discusses the applicability of abstract intelligent agent model to the design of Intelligent
Training Systems (ITS) and to the interpretation of trainee errors . The aim of these class of ITS is
to teach human decision-makers of managerial and cooperation patterns for large scale emergency
situations.
An emergency situation in human organization context is an extremal situation with relatively well
distinguished domains of agent's activity, and which requires a rational explanations of agent's
interventions.
The cooperation among human agents is subordinated to commonly accepted goals and should be
carried out according to the explicitly adopted top preferences. In emergency procedures, the agent
activity domains include its physical environment, social environment and organizational
(intervention) units. These domains are divided among human agents according to their
organizational roles.
Agent's autonomy is limited by some a priori established constrains and driven by less or more
specific directives. The manager-agent's problem is how to recognize the situation in a proper
conceptualization context and to choice actions or a reasoning activity adequate to the required
general preferences and to the available knowledge.
The design of computer supported training systems needs an elaboration of a flexible humanoriented framework of training procedures. Our illustrative example refers to the CEC
Environment MUSTER project. Its goal is the elaboration of a multi-users tutoring system for the
training of the cooperation between emergency managers during emergency situations in the
Genoa Oil Port [Casablanca, 82], actually under development.
The foundations of the emergency management modeling have been developed in ENEA since 1989
in frame of the ESPRIT project "Information Technology Support for Emergency Management"
[Gadomski,89], [Sepielli,89], [Gadomski,90]. The results obtained are used in the present paper.
The methodological approach and general conceptualization of an abstract intelligent agent which
we heuristically employed for modeling of student-managers are based on the TOGA theory
developed by Gadomski see for ex. [Gadomski, 88,89, 93].
The subject of this paper is consequent to another work [Balducelli 92] relative to the MUSTER
project. The design of ITS requires modeling of emergency managers and their abstract instructor.
The abstract instructor functions should be divided and allocated to human instructor and ITS.
The basic cognitive properties of intelligent agents employed in a training process and their
contexts are identified and discussed.
The specific property of the MUSTER project is tutoring and training of students on the level of
management. For this task it is not sufficient to model the knowledge to be transferred from the
instructor to the students and to model how this transfer can be realized but it is also necessary to
implement some meta-rules which would enable students to increase their capability of the goaloriented operations on their own domain and cooperation knowledge.
One of these capabilities is a possibility of abstraction. It is an intrinsic property of intelligent
agent and it is analyzed deeper in the next paragraph.
2. Training of Abstraction and Learning Capabilities
The abstraction capability is one of the most important cognitive feature of an intelligent agent.
This capability, from the dawn of human civilization, allowed the social evolution of men over the
other animals.
We can imagine that one day, an hominid utilized a stone wedge-shaped object to kill the prey.
Then, day after day, he killed many other animals using the same object: finally he understood that
an object could be modeled in his mind if it was associated to a predefined goal. In this modeling
activity he discovered that it was not important that the modeled object was exactly the same that
he found by chance; it was important only that some object attributes and features/properties were
similar to the attribute present in his mental model of the object itself (sufficient hardness,
adequate shape ect.). The human mind learned to use the concept of tool which is an abstract
concept. The capacity to abstract conceptual properties for concrete examples is a typical feature
of the human mind and probably is the most important characteristic of the agent intelligent
behavior.
Many works in the AI literature analyse this important feature of intelligent agents. The necessity
to construct abstract models was considered for qualitative modeling of physical systems
[Kuipers,93] and for abstract interpreters of computer programs [Cousot,92]. Abstraction is also
recommended as useful strategy to acquire and formalize knowledge for experts systems building
[Balducelli,90]. All software technigues include implicity the abstraction mechanism.
In the perspective of modeling the behavior of an intelligent agent, it is necessary to consider that
a strong relation seems to be present between his abstraction and his learning capacity.
An interesting framework in which the concept of abstraction appears associated to the concept of
learning, was the ITSIE project developed in the European Community's ESPRIT program. The
goal of this project was the architectural definition of Intelligent Tutoring Systems for Industrial
Applications. It proposed an automatic adaptation of the learner agent cognitive behavior, moving
up and down between the three Rasmussen's knowledge levels [Rasmussen,82]: skill, rule, and
model level.
The example of reasoning paths in the fig. 1 shows that human can learn something more about his
domain of activity only by increasing his own abstraction capability. During the evaluation of the
observation, having learned a new concept (rule) on a certain abstraction level, he can pass on a
lower abstraction level to act more efficiently in his physical intervention domain ( = agent's 'enddomain of activity', end-d-o-a).
From this point of view, the main reason for which an intelligent agent performs abstraction
processes is the need of learning or discovering new features or new operational rules related to the
current intervention goal and end-d-o-a.
Starting from known particular situations, humans are able to extract abstract features and to build
models which are applicable to various situations of the same class. When humans acquire
competence and expertise in their end-d-o-a, they also improve the capability to increase and
decrease the reasoning abstraction level .
MODEL
MODEL
KNOWLEDGE
KNOWLEDGE
learning/discovery
RULE
KNOWLEDGE
data
acqusition
RULE
KNOWLEDGE
learning/discovery
SKILL
data
acqusition
SKILL
KNOWLEDGE
data
acqusition
KNOWLEDGE
data
request
Observation activity
data
request
activation
activation
Action execution
Fig 1 - Navigation between three Rasmussen's Knowledge levels (three abstraction
levels).
In the TOGA (Top-down Object-Based Goal-oriented Approach) [ Gadomski, 94 ] meta-theory
there are distinguished two abstraction hierarchies, generalization (GL) and meta-levels hierarchies,
for example the following successive structures are defined: meta-preferences, meta-knowledge,
knowledge on meta-preferences, meta-knowledge on meta-preferences, and so on.
Hierarchical abstraction process can be performed from different points of view and can lead to the
construction of different abstraction spaces.
The formal architecture and navigation between different abstraction levels are discussed in the
Gadomski's paper [Gadomski, 93].
Therefore, the training process of a human intelligent agent consists of the improvement of his
abstraction capability in his different contexts of interest. Training must support this type of
intellectual behavior adopting training strategies adequate to end-d-o-a and student's initial
capabilities.
In this sense the accepted exercises and the drills must :
- indicate a proper abstraction direction in the problem knowledge space;
- give the possibility of testing of the student abstraction capability to navigation between different
abstraction levels;
- give the possibility of evaluation of the student operational knowledge used for such navigation,
i.e. real-time validation of methodological rules employed in his problem solving activity.
To make really effective the training process, a dynamic model of the student itself must be used
during the training sessions. In fact, the students may already have a partial skill or knowledge
about the matter to be learn.
It is normally ineffective and sometimes dangerous for the instructor to perform training sessions,
and to propose drills and exercises without taking into account the student's needs (preferences).
These problems was already investigated in AI with the aim to develop intelligent tutoring systems
[Sleeman 81], [Hartley 87], [Aiello 88].
The instructor's goal is not only to increase the efficiency of a single agent, but his objective is to
improve the efficacy of a population of agents having different experiences and skills but
cooperating together to solve the same problem.
The considered tutoring problem is relative to the management of an incoming emergency
situation inside a port or a railway station. In this case, it is very important to improve the
emergency managers abstraction capacity. In fact, in many cases, from the evaluation of few
accidents already appeared in the past, or considered as hypothetical, it is necessary to generalize
the emergency procedures to take into account all the new possible accidents of the same classes.
In addition, every emergency manager must learn the behavioral models of other agents involved in
the emergency situation to solve hypothetical conflicts between them, to collaborate and to
negotiate (for ex. sharing common resources).
3. Knowledge about the Physical Emergency Domain
3.1 LAYOUT - RESOURCES - SCENARIO, LRS CONCEPTUALIZATION
The physical emergency domain is the end-d-o-a of the student, it is the domain of the goal of
emergency management. A mental image of the emergency domain is the domain of student
hypothetical interventions, i.e the domain of his attempts to achieving particular intervention-goals.
The suggested LRS conceptualization framework is composed of three layers:
- Layout Layer: LL,
- Resources Layer: RL,
- Scenario Layer: SL.
All of them can be represented by abstract objects-relations networks.
The layout layer is the frame for the most static information about the domain itself. Normally the
information represented in LL cannot be modified by the training supervisor before and during a
training session. The layout of the end-d-o-a is represented by more or less schematic maps of the
considered territory.
The resources layer represents all equipments, components and human organizations that are
active on the layout; they have defined goals and functions.
The scenario layer represents the set of sequences of events that may be considered in the layout.
They are in relation with the resources and with the emergency management actions .
In order to build an integrated emergency environment image it is necessary to map the resources
layer on the layout layer and the scenario layer on the resources layer. All of them must be
conceptually referred to the possibilities of observation and modifications/interventions by
emergency managers.
The mapping of the resources layer on the layout layer is a simple geographical mapping. This
means that when the mapping is performed, only the attribute location of any object of type
resource is defined. As a consequence, also the attribute availability time may be redefined for
some resource objects. This is due to the fact that the layout contains constraints able to increase or
reduce the availability in time of a resource.
The mapping of the scenario layer on the layout and resources layer has the effect of modifying
several resources' attributes. The attribute availability time may be
influenced by the
meteorological factors or by the accessibility constraints; the attribute destructiveness is
influenced by the level of storage or by the meteorological factor or by the population density, etc.
In other words, one can say that the resources layer contains objects attributes for which only
average values may be determined without taking into account the other two layers. More specific
attribute values can be only determined if the mapping process is performed.
3.2 EMERGENCY PROPAGATION SCRIPT
The training supervisor has the duty to produce the most suitable training session designing
possible scripts of the emergency scenario evolution.
Script is a graphical representation of the web of events that may be considered in the layout during
specific emergency cases. It includes only the layout nodes which was, is or can be in an emergency
state.
LEGEN D
Dynamic states of nodes:
active
no more active
not activated (yet)
disactivated
not vulnerable node (no losses)
vulnerable node (with losses)
cause-consequence relation
primary source
transmitter
secondary sources
barrier
output node (emergecy escalation)
Fig 2 - An example of the emergency propagation script.
In abstraction space, the scripts can be constructed on different GLs (Generalization Level).
Every node of a script is charecterized by LRS attributes. Using LRS knowledge the script
construction requires:
1) The classification of layout objects in term of:
- selection of some primary sources of emergency states;
- identification of the possible secondary sources of emergency states to be modeled as nodes in an
emergency propagation net. These nodes can be identified taking into account the vulnerability and
destructiveness attributes of the involved objects;
- identification of potential transmitters of emergency states; the boundary between the in-site and
the off-site layout of the port is a critical node to transmit the emergency out of the port on the
public territory;
- identification of potential barriers of emergency states: the port in site fixed resources as the antifire system or the mobile resources as the anti-pollution "panne" are examples of potential barrier.
- identification of the vulnerable points generating great amount of losses;
- identification of the output nodes changing the emergency range from local to regional scale.
2) Identification of cause-consequence relations between the objects.
3) Integration of the previously recognized objects and relations into emergency propagation nets
(possible scripts of emergency evolution).
4) Identification of the temporary state of nodes from the management point of view.
The Fig. 2 illustrates an emergency script. The lines represent all possible paths of emergency
evolutions. The arrows indicate an emergency propagation, and link the nodes which were or
currently are in emergency state.
The script is a part of abstract student activity domain. From his perspective, the tutor's scripts
must be discovered, conceptualized and modified in order to stop the emergency propagation
process and , in parallel, to reduce total losses.
4. Cooperation and Coordination Knowledge
The main top TOGA assumption is that every real-world human problem involves in its own
solution either an explicit or implicit model of the problem solver. In TOGA, an abstract intelligent
agent (AIA) based on IPK (Information, Preferences, Knowledge) architecture is proposed.
In frame of the AIA model, intervention-goal achieving, task execution and cooperation are
represented. Abstraction mechanisms of learning, discovery, and navigation between different
levels of student meta-preferences and meta-knowledge have to be visible.
Emergency managers have different competences and responsibilities related to the physical
emergency domain. Cooperating, they construct a common emergency script, and interact and
communicate during its individual modifications.
In a defined physical domain like an Oil Port, in general, the emergency management activity is
performed by an emergency cell composed of a coordinator agent and other manager agents in
different roles ( they are responsible on parts of the emergency domain or on some resources like
firebrigates or police). During an emergency situation the coordinator cannot take decisions alone
but must to discuss them with other managers taking into account their respective points of view,
individual proposals and preferences.
Regional Emergency Coordinator
Regional
Fire Brigates
Manager
Harbour
Master
City
Police
Manager
Public
Healt
Manager
Off-Site
On-Site
Local Emergency
Coordinator
Local
Fire
Brigates
Electrical
Subsystem
Manager
Anti-fire
Subsystem
Manager
Fuel loading
Subsystem
Manager
Local
Police
Manager
Fig 3 - Emergency Management Cell structure.
On the Fig. 3 the emergency management organization structure is presented. Two principal
classes of emergency: on-site and off-site emergency are distinguished. The first is the emergency
that can be managed by the local emergency coordinator inside the oil port with the resources
present in the port itself. The other is the emergency that can not be managed using only the local
resources.
Using the TOGA conceptualization framework, the relation between intervention goal and its
execution carriers (agents and their d-o-a), may be decomposed in tasks and actions. These tasks
and actions must be planned, monitored, controlled, and synchronized ( coordinated one with
another), how is shown in the Fig. 4.
modifications
Intervention
goals
Domain of
activity
TASKS
ACTIONS
Coordinator
Agent
Manager
Agent
Consequence relation
modifications
Executive
Agent
planning / execution
monitoring
Comunication
controlling
Fig 4 - Interrelations between 'intervention goals' and 'domain of activity' in presence of a
coordination.
In the emergency management case, tasks are all the intervention procedures that indicate WHAT
must be done, and require the support of many agents resources while actions are procedures that
specifies HOW particular task can be executed. The actions are addressed to a single resources
units (executive agents).
In a real situation the cell coordinator are responsible for cooperating planning, monitoring and
controlling at task level, while the other agents realize the same functions but at actions level.
Task planning activity can be executed only after a negotiation activity between the coordinator
and local managers. In fact, on the basis of manager individual possibilities (possible actions) many
different tasks could be suggested by the managers to the coordinator-agent. In many cases these
tasks can be in conflict each with other. Therefore, conflict resolution is another typical
coordination activity that can be performed in an iterative way between the involved agents.
Task monitoring activity is especially necessary when many unforeseen emergency states can
appear on emergency script during the execution of emergency interventions. The assessment of
the current situation, depending on the monitoring of a particular task execution , it can vary during
the emergency process and can cause the necessity of the re-planning previously chosen tasks.
The coordinator controls whether the actions on the executive level are performed according to
planned tasks and are synchronized in time and according to the common available resources. Also
in this case, new situation assessment and new plans can be generated by cooperating agents.
Task monitoring and control activity is based on the direct messages (information) from the
emergency domain and communications obtained from other emergency managers. Relatively to the
cooperation and executive levels, the coordination level is a meta-level. The agents on this level
require knowledge about cooperation and negotiation, which are critical in many high-risk, time limited , and stressed conditions.
Therefore to improve the capacity of planning, monitoring and controlling at the cooperation (task)
and coordination levels, a practical psychological and sociological knowledge is strongly required.
In the above perspective, the emergency organization can be viewed as a distributed multiintelligent agent ( MIA ) system with a local autonomy of its intelligent elements [Gadomski,92].
On the coordination level, his autonomy is limited by common intervention goals , time, and
available resources. On executive level, the agents autonomy is also limited by the tasks which they
obtained from the previous level. Some aspects of local autonomy of the human agents in
emergency management organization was preliminary analyzed in the work [Gadomski,
Gadomska, 89].
5. Computer Supported Training
The agent training with a computer simulation support is a case based and it is realized by
individual discovery of the students. Contrary to the direct knowledge acquisition method (which is
more characteristic for tutoring systems), the training system produces the examples of dynamic
scripts which are student's interventions domains.
Learning by examples utilizes an exercise library that must be build by a domain experts and
inserted off-line by the training supervisor to the system knowledge base. Learning by tutoring and
examples implies that the tutor must select (an exercise selection) the exercise from the library, on
the base of the continuously updated student model (his current intervention goal, duties,
competence and current learning level). In addition, during the sessions, he must furnish
explanations or suggestions (a student tracing), and evaluate the student learning capability and his
learning results.
When a student performs the learning process only by examples, he must be able to perform by
himself the abstraction process from a specific examples. He builds inside his operational
knowledge, a strategic procedures how to navigate between abstraction levels.
In the case of learning by tutoring, the tutor supports the student learning process according to the
various strategies, for example:
- Control the sequence in which the exercises are proposed; the proposed sequence must to
facilitate the student abstraction process.
- Control the types of proposed exercises; the proposed situations must be typical and general
avoiding to insert all the facts that are not important and not critical for the situation itself.
Intelligent tutoring systems must utilize the both, the tutor and student models,
They needs:
- the reference abstract student model which includes main required preferences and domain
knowledge (according to the LRS conceptualization),
- the history record which memorize the correctnes of the student actions,
- the tutor's verification operational knowledge and tutor's training strategies , i.e. his meta operational tutoring knowledge.
Computerized
Support
Preference
Domain
Knowledge
Exercise
Library
scenario design
Historical
Records
ABSTRACT
STUDENT
1
informations acquisition
interaction
ABSTRACT
difficulty
TUTOR
level
modification
Simulation
ABSTRACT
STUDENT
2
ABSTRACT
STUDENT
3
observation
EXAMPLES
observation
COOPERATION COMMON KNOWLEDGE
conflicts solutions / negotiation strategies / resources allocation
Fig. 5 - Multi-Agents Intelligent computer training support
A more realistic framework is a multi-user groupware computer supported training: the relative
schema is presented in the Fig. 5. In this case, main training goal is not to increase the individual
and personal agent domain knowledge, but to construct cooperative preferences and strategies
related to the individual preferences, knowledge, and current intervention goals of the cooperating
managers. The intelligent tutoring system requires different cooperation patterns. Here, the
adequate heuristic rules bases must be prepared by a sociologist and psychologists team.
Taking under consideration that the cooperation training is organized for domain specialists, we
should stress that in practice, a realistic training of many emergency managers requires a tutor which
is rather expert in the above domains than which is expert in specific emergency field.
The general abstract emergency student, and the abstract tutor can be confronted analyzing the
Figures 6 and 7.
The main domains of modification and development are the individual student meta-preferences
and meta-knowledge systems in the context of fixed preferences of emergency organizations, i.e.
his role duties and directives included in the emergency procedures/instructions.
Preferences
related to
A, B, C
Domain of activity
Meta-preferences
inform ation
Duties
References
A Emergency
Case
Axiology
Preferences
meta-knowledge
Current Image
B of Emergency
Case
Image
C Current
of Cooperation
Students'
Cognitive
Models
inform ation
Kn owle dge
re late d to
A, B, C
De scriptive &
O pe ration al
m odification
Knowledge
meta-preferences
Em e rge n cy
dom ain fram e s
C oope ration
fram e s
S tu de n t
re fe re n ce m ode l
Meta-knowledge
Tu torin g
strate gy k n owle dge
Fig 6 - Abstract Tutor functional components represented in the framework of the IPK cognitive
intelligent agent architecture
Preferences
Meta-preferences
Emergency goal
Duties
Current image
of emergency
Axiology (local)
Current image
of cooperation
Preferences
Meta-knowledge
intervention goal
Knowledge
De scriptive
Layout
Re source s
O pe rational
Knowledge
Meta-preferences
Mapping
Re lations
Sce nario
C oope rative
strate gie s
First Level
D-O-A
C oope rative
strate gie s
Second Level
D-O-A
Meta-knowledge
O pe rative
De scriptive
- information flow
Fig 7 - Main elements of the cognitive architecture of the Abstract Emergency Student (AES)
6. Conclusions
In this paper only preliminary results obtained by the confrontation of AIA with the models of
intelligent agents involved in the training of management and cooperation in emergency
conditions, were presented.
The role of the abstraction mechanism in the generalization and meta levels hierarchy was specially
discussed.
During training activity human and artificial agents are employed. From TOGA perspective, both
of them can be represented according the same frame architecture. The obvious differences relates,
of course, to the particular role knowledge and role preferences but which may be structured and
operated in the frame of the same cognitive architecture.
The main serious differences between artificial and human intelligent agents are refered to the
following properties of these agents:
- the ITS is programmable and its preferences' systems is integrally available for the programmers.
The human knowledge and preferences are evolutive and only indirectly demonstrated.
- the top human agent preferences can be only verbally declared because humans have hidden
individual preferences absent in the case of the computer agent.
- interrelations among rational AIA and its carrier systems can be neglected in the computer agent
behavior analysis, but can have essential meaning in the human cases.
For example, variable human perception and mental capabilities depend on the state of his body.
The top preferences can also be modified by the evolutional biological mechanisms in unconscious
for the agent way (for ex. as an effect of human emotions).
Another aspect of the modeling of a strongly connected, hierarchical organization, as the
emergency management structures, is the temptation of its conceptualization as a distributed multiintelligent agent. In the future, this problem would be the field of interesting, and probably fruitful,
results.
In the practical perspective the definition of a software system for collaborative training in
emergency management will be the next specifications step in the MUSTER project.
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