The Multi-Agent System IDE : What it Should and Should not Support Gregory O’Hare, Department 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 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 VIPERAgent Viewer

The Development Methodology

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: An open source version will be available soon!