Presently, most multiagent frameworks are typically programmed in Java. Since the JADE platform has been recently ported to .NET, we used it to create an adaptive multiagent system where the knowledge base of the agents is managed using the CLIPS language, also called from .NET. The multiagent system is applied to create seismic risk scenarios, simulations of emergency situations, in which different parties, modeled as adaptive agents, interact and cooperate.adaptive systems, risk management, seisms.

Florin LEON

Gabriela M. ATANASIU

Mihai Horia ZAHARIA

English

Research Papers in Economics

Management & Marketing (2008) Vol. 3, No. 4, pp. 73-80. 
 
ADAPTIVE MULTIAGENT SYSTEM FOR SEISMIC  
EMERGENCY MANAGEMENT 
 
Florin LEON, Mihai Horia ZAHARIA, Gabriela M. ATANASIU 
„Gh. Asachi” Technical University of Iaşi 
 
 
Abstract. Presently, most multiagent frameworks are typically programmed in Java. 
Since the JADE platform has been recently ported to .NET, we used it to create an adaptive 
multiagent system where the knowledge base of the agents is managed using the CLIPS 
language, also called from .NET. The multiagent system is applied to create seismic risk 
scenarios, simulations of emergency situations, in which different parties, modeled as adaptive 
agents, interact and cooperate.  
 
Key words: adaptive systems, risk management, seisms.  
 
 
1. Introduction 
 
The modern societies have many problems related to risk management of 
metropolitan areas. Due to the inherent number of inhabitants, any larger disaster 
either natural or artificial can have a deep impact on society. The information society 
gives the instruments than can reduce this impact. This is accomplished by using both 
prevention plans and disaster management plans. Probably, the most efficient way of 
minimizing losses is to create a model of the reaction, response or of the disaster itself. 
At community level, tool already exist to handle some problems. Unfortunately, great 
natural disasters like earthquakes have an impact at national or international levels. 
The only way of creating a global image from discrete images is to analyze all existent 
data about the disasters. However, the quantity of information is large and modern 
computing has two approaches to solve this problem. The first is a centralized manner 
that involves data to be stored into a few computing centers with great computing 
power. These centers usually have clusters of supercomputers which are very 
expensive. The second is given by the Internet growth in both quality and quantity of 
handled datasets, it is the distributed approach. Here we have many models but the 
most efficient ones from the point of view of the involved costs is to use intelligent 
agents (Atanasiu, Leon & Zaharia, 2008). They can travel to any point that has the 
needed data and will make exchanges of knowledge. As a result, even the countries 
with medium developed economies can use it to try to solve a part of this great 
problem.    
According to Wooldridge (2000), an agent  is a computer system that is 
situated in its environment and is capable of autonomous action in order to meet its 
design objectives.  
Agent Oriented Programming (AOP) is a fairly new programming paradigm 
that supports a societal view of computation. In AOP, objects known as agents interact Management & Marketing 
 
74 
to achieve individual goals. Agents can exist in a structure as complex as a global 
internet or one as simple as a module of a common program. Agents can be 
autonomous entities, deciding their next step without the interference of a user, or they 
can be controllable, serving as an intermediary between the user and another agent. 
One of the differences between traditional OOP and AOP is coupling. 
Interfaces are a way of improving good design which can decrease coupling, but 
usually the call of a specific method is made with specific arguments, thereby 
coupling the two classes in code. The same method invocation has to occur for agents, 
but with one difference: there is effectively just one method on each agent, with one 
argument, containing all the information of the call. 
Enhanced decoupling in an AO environment also comes from a semantics-
related reason. When an agent interacts with another, the recipient of the “call” is free 
to act as it wishes and should not be expected to blindly perform a request, like objects 
usually do. 
Intelligent agents retain the properties of autonomous agents, and in addition 
show a so-called „flexible” behavior (Wooldridge & Jennings, 1995): 
  reactivity  (the ability to perceive their environment, and respond in a 
timely fashion to changes that occur in it);  
  pro-activeness (the ability to exhibit goal-directed behavior by taking the 
initiative);  
  social ability (to interact with other agents and possibly humans). 
 
2. JADE.NET Framework 
 
JADE (Java Agent DEvelopment) framework has become one of the most 
wide spread agent-oriented middleware, based on the peer-to-peer intelligent 
autonomous agent approach. It is a completely distributed middleware system with a 
flexible infrastructure and its goal is to facilitate the development of complete agent-
based applications by means of a run-time environment implementing the life-cycle 
support features required by agents, and the core logic of agents themselves 
(Bellifemine, Caire & Greenwood, 2007). 
FIPA (the Foundation for Intelligent Physical Agents) is an IEEE Computer 
Society standards organization that promotes agent-based technology and the 
interoperability of its standards with other technologies. The FIPA standard is based 
on the principle that only the external behavior of system components should be 
specified, leaving internal architecture and implementation details to the developers of 
individual platforms. This ensures the interoperation between compliant platforms.  
JADE provides complete compatibility with the FIPA specifications 
(communication, management and architecture) that offer the framework within which 
agents can exist, operate and communicate, while adopting a unique internal Adaptive multiagent system for seismic emergency management 
 
 
75 
architecture and implementation of key agent services (Bellifemine, Caire & 
Greenwood, 2007). 
LEAP (Lightweight Extensible Agent Platform) is an extension of JADE to 
enable it to run on wireless devices and PDAs such as cell phones and palm 
computers. S. Rusitschka has ported the LEAP version of JADE to .NET using Visual 
J#.  Therefore, the resulting DLL can be used from any .NET language, such as Visual 
C#, to build multiagent systems. 
 
3. Clips 
 
CLIPS (C Language Integrated Production System) is an expert system shell 
developed by the Software Technology Branch, NASA. It is thus specifically designed 
to facilitate the development of software to model human knowledge or expertise 
(Giarratano, 2002).  
There are three ways of representing knowledge in CLIPS: rules (which are 
primarily intended for heuristic knowledge based on experience), generic functions 
(which are primarily intended for procedural knowledge), and objects-oriented 
programming (also intended for procedural knowledge, supporting classes, message-
handlers, abstraction, encapsulation, inheritance, and polymorphism).  
The CLIPS shell provides the basic elements of an expert system: the fact list 
and instance list, which represent the global memory for data, the knowledge base or 
the rule base that contains all the rules, and an inference engine that controls the 
overall execution of rules, based on an implementation of the Rete algorithm (Forgy, 
1982). Using the inference engine, rules match patterns on objects and facts. 
 
4. The Model of an Adaptive Multiagent System 
 
In JADE, a custom agent class is derived from the provided Agent base class. 
Agents run in the so-called containers, as shown in Figure 1. The methods that are 
automatically called by the platform during the agent lifecycle, such as setup(), must 
be overridden. For each agent a behavior can be defined, which describes the actions 
that the agent performs. Agent communication is implemented using ACL messages. 
Each agent defined in this way has its own knowledge base corresponding to 
its type and previous experience. We chose to encode it with the CLIPS language due 
to the high expressive power of representation using facts (data) and rules (conditions 
and actions). Beside its built-in pattern matching capabilities, another advantage is the 
simplicity with which a CLIPS program can modify itself. There are two possible 
types of changes. First, changes in facts – this is the usual way of controlling the 
workflow of the program: adding (asserting) new facts and deleting (retracting) old 
ones. 
 Management & Marketing 
 
76 
(defrule add-fact 
=> 
(assert (data 0))) 
 
(defrule delete-fact 
?f <- (data ?) 
=> 
(retract ?f)) 
 
 
 
Figure 1. The UML diagram of the adaptive multiagent system 
 
The second type refers to the changes in rules themselves. Most programming 
languages have a set of functions that remain unchanged during the execution of the 
program. Its behavior is given by the particular values of the variables, which define 
the program states. In CLIPS, rules can be dynamically added and deleted, i.e. the 
program can dynamically change itself at run-time. To add a new rule the following 
method can be used: 
 
(defrule add-rule 
(rule-text ?rt) 
=> 
(build ?rt)) 
 
The new rule is introduced as a string in a fact, and then it is converted into a 
rule per-se using the build function: 
 
(assert (rule-text „(defrule dynamic-rule => (printout t \„The dynamically 
created rule has fired\” crlf))")) 
 
Also, there is a simple function, undefrule, to delete a rule: 
 
(defrule delete-rule 
(rule-name ?rn) 
=> 
(undefrule ?rn)) 
 
The name of the rule to be deleted can be given as a fact: 
 
(assert (rule-name dynamic-rule)) Adaptive multiagent system for seismic emergency management 
 
 
77 
 
These features make CLIPS a good choice for implementing adaptive agents 
that can take into account the new information regarding a situation but also the new 
ways of reaching the same goal, the changes in behavior that can occur, for example, 
in case of a catastrophic seismic event. 
 
 
 
Figure 2. The general architecture of the agent-based risk management system 
 
 
5. Creating Scenarios for Seismic Emergency Management 
 
A fundamental principle of risk assessment is that risk due to natural hazards 
such as earthquakes, hurricanes and floods is location dependent. The process of risk 
assessment involves hazard evaluation and vulnerability analysis of built 
infrastructure. The probability of earthquake occurrence varies depending on location, 
and local site conditions also play a vital role in determining the intensity of the 
earthquake. Seismic risk scenarios are intended to provide local, state and regional 
officials with the tools necessary to assess the risks from earthquakes. GIS 
(Geographical Information Systems) can be used for all the disaster prevention phases, 
and especially for physical planning in the mitigation phase, by taking the disaster risk 
into consideration (Atanasiu, Brătianu & Leon, 2008; Atanasiu, Leon, et al., 2008). 
The general architecture of the agent-based risk management system is 
presented in Figure 2. For the simulations of the seismic risk scenarios, three classes 
of agents were used: 
  vulnerability assessment agents; 
  transport network agents; 
  emergency response agents. Management & Marketing 
 
78 
5.1. Agents for Vulnerability Assessment  
 
A key concept for the evaluation of vulnerability, developed primarily for 
seismic events, is the structural fragility curve. Fragility curves can be used for 
modeling the effects of a possible natural hazard event on structures, as a method of 
analyzing the behavior of built existing infrastructure in urban areas under different 
hazard scenarios. The fragility curve is defined as the mathematical expression that 
relates the conditional probability of reaching or exceeding a particular structural 
safety level, given a particular level of the hazard. HAZUS (FEMA, 1999) specifies 
the safety levels in terms of four damage states: slight, moderate, severe, and complete 
damage state. The GIS map of the vulnerability of buildings in a urban sample of Iaşi 
city, Romania, is displayed in Figure 3. Given a certain seismic event, green stands for 
minor damage, blue means moderate damage, yellow represents major damage, and 
red stands for near-collapse. 
 
 
 
Figure 3. Using GIS mapping for the assessment of building vulnerability 
 
The vulnerability assessment agents have information related to the fragility 
curves of buildings and critical infrastructure. These can be considered as starting 
points for the beginning of the emergency operations. As more real data come from 
the agents involved in emergency response, the information regarding the damage of 
the buildings is updated and the knowledge base of the agents is changed. 
 
 Adaptive multiagent system for seismic emergency management 
 
 
79 
5.2. Agents for Transport Network 
 
Similarly to the vulnerability assessment agents, the transport network agents 
evaluate the status of the transport channels, such as roads. They compute and 
communicate the shortest path between two locations, taking into account the current 
state of degradation. Beside the updated on-site information, their algorithms may be 
changed as well in order to offer better solutions, which is accomplished by 
dynamically changing their rules. 
 
5.3. Agents for Emergency Response 
 
Emergency response agents model the intervention forces that are active 
during emergency: fire squads, police cars, civil protection teams. They rely on the 
information from the vulnerability assessment and the transport network agents, and 
compute a risk map by identifying the critical areas. Such information can be the 
geographical locations of gas stations and gas pipelines, that may be affected after an 
earthquake (figure 3). The possible changes that can occur for an emergency response 
agent are both in the facts (information received from other agents and perceived on-
site), and rules (better ways of action in emergency situations). 
 
 
 
Figure 4. GIS visualization of urban risk control facilities 
 
This class of agents provide in turn actual information to the other two classes 
of agents based on their field experience. A cooperation mechanism for the subtypes 
of the agents in this class may be devised. They may also exchange rules of actions, 
based on a common ontology that defines the goals of the agents. Management & Marketing 
 
80 
6. Conclusions 
 
This paper describes the model of a multi-agent system with complex and 
heterogeneous agents. Each type of agent was designed with an adaptive knowledge-
based and different goals. An alternative approach to changing the behavior of the 
agents is to use inductive learning or reinforcement learning methods. The underlying 
learnt models have to be transformed into proper rules for the inference engine, as a 
pre-processing step. In this respect, the representation of the decision trees, 
reinforcement learning matrices and generalized exemplars (Leon, 2006; Leon, 
Atanasiu & Gâlea, 2006) can be converted into first-order logic rules. The multi-agent 
approach is particularly useful for simulating the emergency response actions for a set 
of seismic scenarios linked with urban locations, organized in a modular and 
cooperative manner.  
 
References 
 
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A Computational Approach for the Assessment of Seismic Vulnerability Based on the National Data Infrastructure,

Decision based risk assessment model for existing damaged infrastructure, application to Iasi city,

Intelligent agents: Theory and practice,

Adaptive multiagent system for seismic emergency management

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