Abstract
The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.
TopBefore going into the middle of the problem it is not with out reason to have a short look over the main topic of the current chapter.
Nowadays agents are encountered almost every where, or at least the term agent. There are several types of agents: intelligent agents, information agents, mobile agents, personal assistant agents and other types of agents.
It is claimed that agent technologies facilitate software development based on their interaction’s high level of abstractions.
There might be raised several possible questions such: What is an agent? Do agents have something in common? And so on.
In Russell & Norvig, 1995 it is argued that agents are entities perceiving their environment through sensors and acting upon that environment through effectors. Agents are not isolated entities but they are able to communicate and collaborate with other entities. There are several group efforts towards the standardization of multi agents systems such as: Foundation for intelligent Physical Agents (FIPA), the Object Management Group, the Knowledge-able Agent-oriented System (KAoS).
The Multi Agents Systems (MAS) have generated many works and many systems were implemented in order to put in practice theory behind MAS. MAS’ base concepts are introduced in Wooldridge, 2000.
Bradshaw, 1997 presents a list of common agent attributes:
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Adaptivity: Being able to learn and improve with experience
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Autonomy: Goal oriented, proactive and self-starting behavior
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Collaborative behavior: Working together with other agents to achieve common goal
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Inferential Capability: The ability to act on abstract task specification using prior knowledge of general goals
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“Knowledge-level” communication ability: The ability to communicate with other agents
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Mobility: Being able to migrate in a self oriented way from one host platform to another
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Personality: The capability of manifesting the attributes of a believable character
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Reactivity: Ability to sense and act selectively
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Temporal continuity: Persistence of identity and state over time
Key Terms in this Chapter
DES: Discrete event simulation
Agent: Entity perceiving the environment through sensors and acting upon that environment through effectors
MAS: Multi agent systems
Business Rules: Business rules or business rule sets describe the operations, definitions and constraints that apply to an organization in achieving its goals.
ABS: Agent based simulation
AOR Modeling: Agent oriented relationship modeling
AORS: Agent oriented relationship simulation