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The Multi-Agent System IDE : What it Should and Should not Support
Gregory O’Hare, Gregory O’Hare, Department of Computer Science, University College DublinDepartment of Computer Science, University College Dublin
Some Questions
What is unique about agents that necessitates a gaggle of new and differing tools, methodologies, ontologies, standards, protocols?
Can we identify and enumerate those needs that form the compliment of the existing development techniques and methods;
In the Design, Implementation, Debugging and Deployment of MAS what is the nature of the tools and functions that we want to support?
In the Design, Implementation, Debugging and Deployment of MAS what is the nature of the tools and functions that we ought not to even attempt to support?
New Challenges for Agent Systems
Computational devices that house and host agents are ever changing;
Mobile & Ubiquitous Computing;
Social Robotics;
Software Evolution – Autonomic Computing, Proactive Computing;
Interaction modalities necessarily are diversifying;
Scalability & Performance
Many MAS do not scale up!This is strange after all it is a distributed system
Need to provide simulation tools Many simulations prove to differ from reality
Autonomic System Characteristics
MAS are autonomic; Clone, die, mutate, compromise
MAS are organic and evolve; Agents evolve; Community evolves (agent sets, relationships)
MAS systems are open; MAS may in certain circumstances may be or
become
chaotic ?
What is Agent Factory?
Agent Factory is…“a cohesive framework that delivers structured support for the development and deployment of agent-oriented applications.”
Promotes the fabrication of strong,intelligent, mobile, and agile agents.
Organised over four layers: Programming Language Run-Time Environment Development Environment Software Engineering Methodology
Implemented in Java Personal Java, J2ME, and
J2SE Compliant
AF-APL
AF-APL is an Agent-Oriented Programming (AOP) language. Designed to simplify the implementation of complex agent
behaviours. Underpinned by a (multi-modal and temporal) logical model of
commitment.
An AF-APL Program = mental state + commitment rules
The mental state is comprised of: Beliefs – representation of the state of the environment. Commitments – the chosen activities (actions or plans) of the
agent. Goals – future states of the environment that the agent wishes to
bring about.
AF-APL Agents interact with their environment through a set of Perceptors and Actuators.
The Run-Time Environment
Distributed environment for the deployment of agent-oriented applications.
Focuses upon supporting interoperability between agents. Compliant with the FIPA Standards. Implemented as collection of Agent Platforms (AP).
The Development Methodology
Iterative Refinement Process
SystemBehaviour
Model
ActivityModel
InteractionModel
ProtocolModel
Agent Model
GenerateAgent
Classes
Build AgentComponents
BuildPlatformServices
ProtocolTesting
BehaviourTesting
VIPER
AF IDE
ConfigureAgent
Platforms
DeployApplication
DESIGN
IMPLEMENTATIONDefine
ApplicationOntologies
DEPLOYMENT
Conclusions
MAS are complex systems. Environments that assist in their development will by definition also be complex;
A variety of environments challenge the deployment of such systems these must be addressed within the IDE.
MAS IDEs need to support all stages within the development lifecycle not merely a focus on instantiation of pre-constructed containers.