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

Agent Factory Layers

AF-APL

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.

AF-APL

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 Environment

AF-IDE

VIPER

Agent Viewer

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

Detail Design Models

Agent Model

Protocol Model

Wireless Sensors Mica2 Motes

Blurring Boundaries

Ball Follow 1 Ball Follow 2

Virtual Robotic Workbench (VRW)

NEXUS: Embodied Intentional Agents

Embodied Intentional Agents

Mental State drives Avatar behaviour

The EasiShop Client GUI

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.

Downloading Agent Factory

An evaluation version of the Agent Factory Framework is available for download from:

http://www.agentfactory.com/

An open source version will be available soon!