View
15
Download
1
Category
Preview:
DESCRIPTION
E 4 MAS 2005. 1. Responsibilities: What constitutes an agent in your model? What are its responsibilities? What is the environment in your model? What are its responsibilities? 2. Modeling: How do you model the environment? - PowerPoint PPT Presentation
Citation preview
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 platon@nii.ac.jp
Nicolas Sabouret nicolas.sabouret@lip6.fr
Shinichi Honiden honiden@nii.ac.jp
6
Outline
• Issues of overhearing in open MAS
• Proposal: Understanding the Environment– Environment for Overhearing– Responsibilties & Features
• Issues & Outlook• Summary
7
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
9
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
11
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
13
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 platon@nii.ac.jp
Nicolas Sabouret nicolas.sabouret@lip6.fr
Shinichi Honiden honiden@nii.ac.jp
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
18
Apr
il 21
, 202
3
19
Apr
il 21
, 202
3
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
20
Apr
il 21
, 202
3
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!
21
Apr
il 21
, 202
3
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
22
Apr
il 21
, 202
3
Example Example Mamei, Zambonelli, Leonardo, Percom 2004Mamei, Zambonelli, Leonardo, Percom 2004
23
Apr
il 21
, 202
3
ResponsibilitiesResponsibilities
• We only consider MAS Application Layer
• Agento Autonomous entity
o Act according to its design goal
o Collectively solve a problem
• Environmento >>
24
Apr
il 21
, 202
3
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
25
Apr
il 21
, 202
3
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.
26
Apr
il 21
, 202
3
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
valckenaersp@acm.org
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)
E4MAS @ AAMAS 2005, Utrecht (NL)
Overview
• Our Experiments
• E4MAS Questions and Answers
E4MAS @ AAMAS 2005, Utrecht (NL)
Experiments
E4MAS @ AAMAS 2005, Utrecht (NL)
II.
I.
pure evolutionary
messageboard
p2p
Setup – 4 dimensions
performance diagrams
mss
III. IV.msh
E4MAS @ AAMAS 2005, Utrecht (NL)
pure evolutionary
messageboard
p2p
Interim results
performance diagrams
E4MAS @ AAMAS 2005, Utrecht (NL)
Definitions
E4MAS @ AAMAS 2005, Utrecht (NL)
environment
(= outside system)
system
( god’s eye view,but set of agents)
Definitions
E4MAS @ AAMAS 2005, Utrecht (NL)
• 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
E4MAS @ AAMAS 2005, Utrecht (NL)
• 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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
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
E4MAS05 – July 26th 2005
Giuseppe Vizzari
Artificial Intelligence Laboratory
L.INT.AR
Department of Computer Science, Systems and Communication
University of Milan-Bicocca
giuseppe.vizzari@disco.unimib.it
Thank you!
99
Apr
il 21
, 202
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
100
Apr
il 21
, 202
3
DomainDomain
101
Apr
il 21
, 202
3
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
102
Apr
il 21
, 202
3
ArchitectureArchitecture
103
Apr
il 21
, 202
3
Collision Avoidance using HullsCollision Avoidance using Hulls
104
Apr
il 21
, 202
3
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
105
Apr
il 21
, 202
3
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
E4MAS05 – July 26th 2005
Giuseppe Vizzari
Artificial Intelligence Laboratory
L.INT.AR
Department of Computer Science, Systems and Communication
University of Milan-Bicocca
giuseppe.vizzari@disco.unimib.it
Thank you!
Recommended