E 4 MAS 2005

EE44MAS 2005MAS 2005

Session I: Definition, Scope, Session I: Definition, Scope, ModelsModels

Chair: Jim OdellChair: Jim Odell 1. Responsibilities: What constitutes an 1. Responsibilities: What constitutes an

agent in your model? What are its agent in your model? What are its responsibilities? What is the environment in responsibilities? What is the environment in your model? What are its responsibilities?your model? What are its responsibilities?

2. Modeling: How do you model the 2. Modeling: How do you model the environment?environment?

3*. Dependencies: Can agents be modified 3*. Dependencies: Can agents be modified without modifying the environment? Can the without modifying the environment? Can the environment be modified without modifying environment be modified without modifying the agents?the agents?

Session I: Definition, Scope, Session I: Definition, Scope, ModelsModels

5' Holonic Modeling of Environments for 5' Holonic Modeling of Environments for Situated Multiagent SystemsSituated Multiagent Systems

10' Overhearing and Direct Interactions: 10' Overhearing and Direct Interactions: Point of View of an Active Environment, a Point of View of an Active Environment, a Preliminary Study Preliminary Study

10' Environments for Situated Multiagent 10' Environments for Situated Multiagent Systems: Beyond Infrastructure Systems: Beyond Infrastructure

10' The Environment: an Essential 10' The Environment: an Essential Abstraction for Managing Complexity in MAS-Abstraction for Managing Complexity in MAS-based Manufacturing Control based Manufacturing Control

Holonic Modelling of Environments for

Situated Multi-Agent Systems

Sebastian Rodriguez Vincent Hilaire Abder Koukam

presented by Olivier Simonin

Université de Technologie de Belfort-MontbéliardSystems and Transports Laboratory – Computer Science Team

http://set.utbm.fr/info

E4MAS’ 2005, July 26, Utrecht

5

Overhearing and Direct Interactions in MAS-Point of view of an Active Environment

E4MAS 2005 July 26th, 2005

Eric Platon [email protected]

Nicolas Sabouret [email protected]

Shinichi Honiden [email protected]

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Outline

• Issues of overhearing in open MAS

• Proposal: Understanding the Environment– Environment for Overhearing– Responsibilties & Features

• Issues & Outlook• Summary

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Overhearing

• Overhearing– Indirect interaction type– Overhearer agent in Multi-Party Dialogue

• Listen to a conversation• Known by conversation members• Not addressed by conversation members

– Opportunity for `more interaction’

Conversation

Overhearer

8

Issues with `current overhearing’

• Approaches– Multicast/Broadcast

– Environment mediation

• No application devoted to open system issues

Environment-based overhearing is the `correct’ approach in Open Systems

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Understanding the Environment

• Environment as `correct’ abstraction– Deal with dynamicity and openness

– Control and enforcement of overhearing

• What kind of environment?– Responsibilities

– Features• Control

• Configuration

10

Environment Responsibilities

• Purpose: Managing interactions– Overhearing (primary target)

– Usual direct interaction (required target)

• Methodology: Reflection from the environment

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Environment Responsibilities

• What E should process? Mediated Interaction

• All interactions are executed by the environment• Overhearing becomes a function

• Whom does E care of? Population and types

• How does E execute processes? Environmental Rules

• Where E should apply processes? Topology

12

Environment Model Features

• Population– Agent Direct interaction & Overhearing

– Elemental Direct interaction

• Environmental Rules– Interaction modes

– 2 modes in this work: `none’ and `overhearing’• Properties that must be verified in the system

• Enforcement by the environment

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Environment Model Features• Topology

– Communication Spaces• Logical and Physical (if necessary)

• Location-aware ruling

– Assignment of population and rules per CS

14

Evaluation

• Purpose– Validate the model

– Evaluate the cost of the environment process

• Methodology– One scenario with repeated runs (with Jade)

• No overhearing

• Multicast-based overhearing

• Environment-based overhearing

• Overall result in short– Valid in the MAS scenario

– Bottleneck with our environment implementation• 2500 agents up to 50% more time to completion

15

Issues and Outlook

• Environment trade-off– Implementation bottleneck vs. modelling concept

– Better implementation No bottleneck?

• When?– No time perspective from the environment

– Issues of propagation, action simultaneity

– When is it necessary?

16

Summary

• Overhearing is a promising interaction model

• It requires an environment in open systems– Enact & rule overhearing

• Natural framework

• Systematic approach

– `Natural’ distribution of responsibilities• Conceptually

• Technically

17

Thank you for your attention

Eric Platon [email protected]

Nicolas Sabouret [email protected]

Shinichi Honiden [email protected]

Environments for Environments for Situated Multiagent Systems: Situated Multiagent Systems:

Beyond Infrastructure Beyond Infrastructure

o E4MAS, July 26th 2005, Utrecht

Danny Weyns, Giuseppe Vizzari, Tom Holvoet

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Modeling the EnvironmentModeling the Environment

• Goal: help to clarify the confusion between o the concept environment

o and the infrastructure on which the MAS is deployed

• Approach: 3-layer MAS model o standard deployment model for distributed systems

applied to MAS

o agents and the environment first-order abstractions

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Modeling the MASModeling the MAS

• 3-layerso Top: MAS application layer

o Middle: Software execution platform

o Bottom: Physical Infrastructure

• Agent, environment => crosscut the three layers!

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Modeling the MASModeling the MAS

MAS application layer

Execution Platform layer

Physical Infrastructure layer

Application Specific Logic

MAS Framework

Middleware

Operating System

Computer Hardware

Physical World

Agent Environment Agent Agent

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Example Example Mamei, Zambonelli, Leonardo, Percom 2004Mamei, Zambonelli, Leonardo, Percom 2004

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ResponsibilitiesResponsibilities

• We only consider MAS Application Layer

• Agento Autonomous entity

o Act according to its design goal

o Collectively solve a problem

• Environmento >>

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Responsibilities EnvironmentResponsibilities Environment

o Domain-specific representation of problem context

Provides a space in which agents can perform their job

Provides a representation of resources to agents

o Enabling entity

Enables agents to interact with domain resources

Enables agents to exchange information

Enables agents to coordinate behavior

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Responsibilities EnvironmentResponsibilities Environment

o Shields complexity to agents

Complexity of resource access

Complexity of interaction handling.

Complexity consistency management

o Manages dynamics external to agents

E.g., digital pheromones, gradient fields, etc.

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ConclusionConclusion

• Environment and infrastructure are no synonyms o Agents and the environment

Both have an application specific representation

Both exploit and run on an execution platform

Both are part of the physical infrastructure

• Environment is a powerful abstraction that can be used creatively in the design of a MAS solution

The Environment: an Essential Abstraction for Managing Complexity in MAS-based

Manufacturing Control

Paul Valckenaers & Tom Holvoet

K.U.Leuven

[email protected]

Responsibilities: Agents

• PROSA– Product agents are 'recipe' experts – Resource agents manage factory resources – Order agents manage ongoing tasks – Staff agents give advice – Agents should not be functions

• Ant agents – Exploring ants scout for solutions – Intention ants reserve capacity/slots on resources – Travel arrangement analogy

Responsibilities: Environment

• Manufacturing resources – Factory– Machining station– Operator– Conveyor– Pallet

• Decision-free aspects only – Pure reflection – Hotel analogy

Environment Modeling

• Resource graph– Peer-to-peer: exits & entries

• Conveyor belt feeding parts into a tunnel oven

– Parent & child • Conveyor belt in a factory • Pallet on a conveyor belt

– Lumped model • Position of a pallet on the conveyor belt • Supported operations/manufacturing processes/… • Attributes/methods/… handle non-graph aspects

– Virtual navigation– Information spaces (stigmergy)

Dependencies

• Modified agents without modifying the environment – Maintenance order/product agents– New product models– New resource mgt policies– …

• Modified environment without modifying the agents– New resources– Removal of resources– New layout, changed connections– …

• Incompleteness issue • New level of education

– E.g. Communicating in probabilistic terms

Session II: Engineering Session II: Engineering EnvironmentsEnvironments

Chair: Tom HolvoetChair: Tom Holvoet 1. Requirements: Does your approach target a 1. Requirements: Does your approach target a

particular domain? What kind of functionality does particular domain? What kind of functionality does your approach offer? What kind of quality properties your approach offer? What kind of quality properties does your approach aim to realize? does your approach aim to realize?

2. Design: What are the building blocks and their 2. Design: What are the building blocks and their relationships to design the environment? What kind relationships to design the environment? What kind of support does your approach offer?of support does your approach offer?

3*. Agent-Environment: How do agents perceive the 3*. Agent-Environment: How do agents perceive the environment? How do they act in the environment? environment? How do they act in the environment? How do agents send and receive messages? Is the How do agents send and receive messages? Is the environment in your approach (in)dependent of the environment in your approach (in)dependent of the architecture of agents? architecture of agents?

Session II: Engineering Session II: Engineering EnvironmentsEnvironments

10' Engineering MAS Environment with 10' Engineering MAS Environment with Artifacts Artifacts

5' An Environment-Based Principle to Design 5' An Environment-Based Principle to Design Reactive Multiagent Systems for Problem Reactive Multiagent Systems for Problem Solving Solving

5' Landscape Abstractions for Agent-based 5' Landscape Abstractions for Agent-based Biodiversity Simulation Biodiversity Simulation

10' An Architecture for MAS Simulation 10' An Architecture for MAS Simulation Environments Environments

Mirko Viroli, Andrea Omicini, Alessandro RicciDEIS – Cesena

Alma Mater Studiorum, Università di Bologna{mirko.viroli andrea.omicini a.ricci}@unibo.it

Engineering MAS Environments with Artifacts

Outline

We aim at developing a general framework for engineering MAS environments for cognitive agents

• Motivation

• Requirements

• Agent-Environment

• Design

Our motivation

• Filling the “MAS-environment gap” – Standard MAS research (cognitive agents): rooted on the intentional stance for agents– Studies on Environments (infrastructures): providing services to black-box agents

• We try to answer two fundamental questions– what is a fruitful way for agents to perceive the

environment?– how to design a good environment for cognitive agents?

• We develop on our previous work– Agent Coordination Contexts [Poster @ AAMAS2005]– Coordination Artifacts [AAMAS2004]

1. Requirements

• Does your approach target a particular application domain?

– Not really, we aim to be general! Some scenario:• coordination, organisation infrastructures• workflow-based systems

• What kind of (meta-)functionality does (will) your approach offer?

– Abstractions & methodologies to the rational exploitation of environments

• What kind of quality properties (non-functional requirements) does your approach aim to realize?

– None, yet. Really an orthogonal aspect..

Our perspective

• Environment as a set of tools or artifacts that agents exploit – to interact with sw & hw resources

• either legacy or not

– to participate to social activities• communication (e.g. message boxes, ...)• coordination (e.g. blackboards, schedulers,...)• inter-operation (e.g. dictionaries, yellow pages,...)• ....

– in all cases: mediation

• In other words, environment as a set of artifacts that agents use to achieve their individual as well as collective goals

A global picture• Any persistent entity is modelled and engineered

either as an agent or an artifact

• Artifacts play the role of the glue

2. Agent-Environment

• How do agents perceive the environment?– as a set of artifacts

• How do they act in the environment? – by invoking operations provided by artifacts

• How do agents send and receive messages? – messages?? more generally, interactions!!!

• Is the environment in your approach (in)dependent of the architecture of agents?– Independent. But it supports/promotes agent

rationality.

A Model of Artifact• Usage Interface

– set of operations that can be executed by agents to use the artifact

• Operating instructions– description of how the artifact is to be used to

obtain its services cognitive use by rational agents

• Service provided– description of what kind of service is provided by

the artifact cognitive selection by rational agents

• From formal aspects down to design/impl.

A picture

agents

an artifact

interface

3. Design• What are the building blocks (and their

relationships) to design the environment? – At the first level: a flat space of artifacts– More in detail

• artifacts can interact via linkability• an artifact can be something complex, e.g. itself a MAS

with agents and artifacts• we are working on a library/taxonomy of artifacts

• What kind of design support does your approach offer to engineers?– Under study (see SODA methodology @ AOSE)– Mostly, it relies on the very difference between

agents & artifacts

Example: tuple spaces

out

in

rd

...

Importing existing infrastructures

Example: pheromone infrastructure

put pheromone

perceive pheromone

...

Importing existing environments

Example: schedulers, workflow engines

get_task

task_done

...

Wrapping complex services

A First Taxonomy

Some building blocks are already there

• Boundary Artifacts

• Coordination Artifacts

• Resource Artifacts

Boundary Artifacts

• Artifacts that model the interface towards the environment for an individual agent– Agent Coordination Contexts

B

B

B

Coordination Artifacts

• Artifacts that automatise a coordination task among a group of agents in the MAS– E.g. TuCSoN tuple centres

C

Resource Artifacts

• Artifacts that wrap resources of different kinds

R

R

Mirko Viroli, Andrea Omicini, Alessandro RicciDEIS – Cesena

Alma Mater Studiorum, Università di Bologna{mirko.viroli andrea.omicini a.ricci}@unibo.it

Engineering MAS Environments with Artifacts

An environment-based principle

to design multi-agent systems for problem solving

Olivier Simonin and Franck Gechter

Université de Technologie de Belfort-MontbéliardSystems and Transports Laboratory – Computer Science

Team

{Olivier.Simonin}{Franck.Gechter}@utbm.fr

http://set.utbm.fr

E4MAS’ 2005, July 26, Utrecht

A Methodology for reactive MAS conception

• Application domain : Problem solving with situated MAS

• Objective : to propose a methodology for conception of reactive MAS

• Our approach relies on– reactive agents considered as a global resolution system

– the environment definition (problem’s representation)

Building blocks of the Solving System

The MAS can be seen as a regulation (or filtering) process :

– Input: problem dynamics / topology of the environment – Output: state of the system which must be stable MAS

organization

– Regulation mechanism : agents try to regulate the perturbations by individual actions and cooperative interactions

A Principle in 4 main steps

1. Define the Environment Problem’s

Topological and dynamical dynamics problem representation

2. Define the agents Perceptions

Agents must be able to perceive perturbations the env. states and dynamics

3. Agent Interaction Mechanisms(i) Individual reactions(ii) Local cooperative actions regulation(iii) Actions to regulate (i) & (ii)

4. Observation of the result emergent organization

Applications

The satisfaction-altruism model[Simonin 01]

Physics based reactive model for localization and tracking [Gechter 03]

Conclusion

• Proposition of an environment-based principle for building Reactive MAS with the Problem Solving issue The Environment has a key role It links the problem’s world and the reactive solving

process The approach contrasts with the emergentist one

• Applications Development of Generic kernels for family of problems Robust and open solutions

• Perspectives To refine the methodology Application to Image Processing and Robot soccer control

Landscape Abstractions for Agent-based Biodiversity

Simulation

Manuel FehlerFranziska Klügl

Michael NeumannUniversität Würzburg

Lehrstuhl für Künstliche Intelligenz und Angewandte Informatik

Interdisciplinary Research Center for Environmental Protection(IFZ) - Biometry and Agricultural Science

University of Giessen

Motivation

• Modeling and Simulation form well-known method for studying real world systems spatially explicit model

• Realism of environment mainly responsible for quality of answers produced by overall simulation and for generalizable results

• We need to put great care in development of simulated environment

RequirementsTarget Applications

– Spatially explicit multi agent simulation models where results produced depend strongly on simulated environment

– Focus of models lies on interaction between agents and their environment

– Main areas are ecological, socio-technical and socio-ecological models

– Example Application:• Studying biodiversity in different landscapes

• Here: Environment = landscape

• Landscapes determined by properties of landscape types and fragmentation

Requirements

Functional Requirements

– Exact representation of real world landscapes in simulation model Optimum for predictive simulations

– Automatic generation of abstract environments For generating results on classes of environments

– Transfer of abstract simulation results on real world environments

Requirements

Quality Properties

General:– Validity

– Usefulness for answering simulation questions

Technical:– Modularization of environmental properties

E.g. different files for:

• landscape composition

• landscape fragmentation

• change in landscape

Landscape Data File

Fragmentation Data File

Design

Overlay relevantOther environmental

propertiesSimulation

Results

e.g. Effects of landscape composition and fragmentation on biodiversity

General Application of Simulation Model for studying relationships between simulated MAS and simulated landscapes

Here: landscape composition

From:

•Import of GIS data

•Automatic generation

From:

•Import of GIS data

•Artificial fragmentation

Modular design of simulated environment

Here: landscape fragmentation

Design

Import GIS datain Simulation Model

Simulating detailed real world landscapes

Development and calibration of agent models based on empirical data

Model validation •Using real data

Exact Prediction•For given detailed landscape

Design

Simulating classes of landscapes

Development and calibration of agent models based on empirical data

Model validation •Using real data

Exact Prediction•For given detailed landscape

Simulating detailed real world landscapes

Design of generation algorithm

Simulation of abstract landscapes

•Analysis of landscape classes

•Predictions for areas with common properties

e.g. dependencies between landscape types and species frequencies

Import GIS datain Simulation Model

Design

Export from Simulation Model

•Import in GIS System

•Analysis of metrics of generated landscapes

• Characterization of generated landscapes

•Algorithm validation

Designing artificial landscape generation algorithms

Classification of designed

generation algorithm•Algorithm design

Systematic generation of examples for landscape classes

Summary

• Valid agent-based models should be based on empirical data from a particular real world environment

• Development of agent models needs to be done for valid environmental modelsGIS-based landscapes

• Abstracted environmental models are necessary– To derive general statements– To work with fuzzy empirical dataAlgorithm for describing classes of environments and

artificial generation algorithm

DIVAs

The Living Environment

Introduction• DIVAs (Distributed Information Visualization of

Agent systems)

• social simulation

• environment = world

• agents = social entities

DIVAs Architecture

• Modular components

• Agent decoupled from Environment

Environment Cell Architecture

• Supports– Conceptual integrity– Separation of concern– Information hiding– Modularity

• Cells are– Autonomous– Interactive– Reactive– Proactive

Agent vs Environment

• Has a goal• Communicates• Possesses resources• Has limited sight• Performs tasks • Offers services• Is mobile

• Has a goal• Communicates• Possesses resources• Has limited sight• Performs tasks • Offers services• Possesses passive objects

Agent vs Environment

• Information passed on ‘need to know’ basis only

Bob MacMahonAgent

Location: 24long, 38latId: agent2143

•Environment only needs to know minimal information about the agent

•Agent population = agents currently in the cell

•Agent only needs to know about the current cell

• local graph• local passive objects

Interactions

Agent Agent

EnvironmentEnvironment

• 3 Types of Interactions

Cell Hierarchy

• Levels represent varying degrees of abstraction

• Layers are independent of each other– Maintain separate

graph models– Maintain separate

agent data

Conclusion

• Design and Implementation – Environment is fully decoupled from Agent– Environment functions without the Agent

• Applications– Results

• apply to application domains where the environment can be represented as a graph

• apply when it is impractical for the agents to have a complete view of the environment

Acknowledgements

• DIVAs team

• yWorks

Session III: ApplicationsSession III: ApplicationsChair: John SauterChair: John Sauter

1. Domain: What does your system do? What 1. Domain: What does your system do? What problem does it solve? What were the main problem does it solve? What were the main functional and quality requirements of your functional and quality requirements of your application? application?

2. Architecture: What are the responsibilities of the 2. Architecture: What are the responsibilities of the agents and the environment in the system? How agents and the environment in the system? How does your solution satisfy the requirements? does your solution satisfy the requirements?

3*. Process: What kind of engineering process have 3*. Process: What kind of engineering process have you followed? Have you used particular tools?you followed? Have you used particular tools?

Session III: ApplicationsSession III: Applications

5' Comparing Communication Protocols 5' Comparing Communication Protocols under Cooperative Pressure under Cooperative Pressure

10' Web Sites as Agents' Environments 10' Web Sites as Agents' Environments 10' Exploiting a Virtual Environment in a 10' Exploiting a Virtual Environment in a

Real-World Application Real-World Application

E4MAS @ AAMAS 2005, Utrecht (NL)

Comparing Communication Protocolsunder Cooperative Pressure

A.E. EibenM.C. Schut

N. Vink

Artificial Intelligence SectionDepartment of Computer ScienceVrije Universiteit Amsterdam (NL)


Overview

• Our Experiments

• E4MAS Questions and Answers


Experiments


II.

I.

pure evolutionary

messageboard

p2p

Setup – 4 dimensions

performance diagrams

mss

III. IV.msh


pure evolutionary

messageboard

p2p

Interim results

performance diagrams


Definitions


environment

(= outside system)

system

( god’s eye view,but set of agents)

Definitions


• What does your system do?

collective resource gathering

• What problem does it solve?

self-regulation of adaptive properties among the agents

• What were the main functional and quality requirements of your application?

to develop agent properties with respect to system requirement(s)

Domain


• What are the responsibilities of the agents and the environment in the system?

agents: selfish ‘optimisation’environment: resource management

• How does your solution satisfy the requirements?

by empirical evaluation (experiment results)

Architecture

E4MAS05 – July 26th 2005

Web Sites as Agents’ Environments:General Framework and Applications

Stefania Bandini, Sara Manzoni,

Giuseppe Vizzari

Department of Computer Science, Systems and Communication

University of Milan-Bicocca


Outline

• Web sites as agents’ environments: idea and possible applications– Gathering information on users– Adaptation of web pages– Context-aware user interaction

• Underlying model and architecture

• Current/future developments


Web Site as a SituatedAgents’ Environment

• Web site present an intrinsic graph-like spatial structure– Pages nodes– Hyperlinks edges

• This structure may represent an environment for situated agents– Users agents– Page request agent

movement or creation

Page

User

Web site

Agent

Place

Situated MAS


Gathering Information on Users’ Behaviours

• From a complex and expensive offline analysis of logfiles generated by the web server

• To a more dynamic exploitation of structured information on users’ activities

WebBrowser

Web Server

DocumentsLogfile

WebBrowser

Agent Server

Agents Space

Web Server

DocumentsTracker


Web

Ser

ver

Agent Server

InterfaceAgent

Users’ Traces

• Users may present recurrent patterns of navigation traces– Trace detection strategy

situated agents behaviour

• These patterns may be exploited to perform site customization or even global optimization– Emerging link selection

strategy interface agent behaviour


Sample Adaptive Website

• A block of the page is devoted to suggested links

• Anonymous users are proposed the most popular traces starting from the current page

• Authenticated users are proposed a mix of their own traces and generally popular ones


Context-aware User Interaction

• Supply users with awareness information on other visitors that are viewing the same page (or close ones)

• Allow them to interact– in a synchronous way with “close” visitors– by broadcasting messages which diffuse along the site structure

Agent ServerClient

Context-aware interaction applet


Underlying Agent Model and Architecture

• Multilayered Multi-Agent Situated System (MMASS) model was adopted to represent situated agents

• A platform for MMASS based system was adopted for the implementation of this part of the system

• User interface agent is represented by a simple JSP page

• Interaction among these element is indirect, and realized by means of a DB of detected traces

Agent

Place

MMASS

Web

Ser

ver

MMASS platform

InterfaceAgent(JSPpage)

Detectedtraces


Current/future developments

• Perform an experimentation of the implemented adaptive web system

• Design more complex behaviours for User Agent

• Effectively implement a context-aware interaction system

• Integrate this system with other approaches focused on the exploitation of semantics of site structure/document contents


Giuseppe Vizzari

Artificial Intelligence Laboratory

L.INT.AR



[email protected]

Thank you!

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3 Exploiting a Virtual Environment Exploiting a Virtual Environment in a Real-World Applicationin a Real-World Application

o Danny Weyns, Kurt Schelfthout, Tom Holvoet

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DomainDomain

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AGV System RequirementsAGV System Requirements

• Functional requirements: Doing Worko Transport assignment

o Collision avoidance

o Deadlock avoidance

o Traffic control

• Target quality requirementso Flexibility

o Openness

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ArchitectureArchitecture

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Collision Avoidance using HullsCollision Avoidance using Hulls

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ProcessProcess

• Team: currently 2 researchers, 2 developers

• Processo First phase: (functional) requirements gathering

o Second phase: jointly designing the architecture

o Third phase: Detailed design, implementation, testing done individually

Architecture:

Blueprint for implementation

Useful to divide work

Communication vehicle

• Recently: Architecture Trade-off Analysis Method o Stakeholders evaluate architecture

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ConclusionConclusion

• Application whereo Real environment is constrained

o No extra infrastucture possible or necessary

• How to use situated agents without environment?

• Answer: exploit a virtual environmento For information exchange

o For coordination

o To shield lower level details from agent

o To build more modular software


Giuseppe Vizzari

Artificial Intelligence Laboratory

L.INT.AR



[email protected]

Thank you!