Design of MultiAgent Systems

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Design of Multi-Agent Systems

• Teacher
• Bart Verheij
• Student assistants
• Albert Hankel
• Elske van der Vaart
• Web site
• http//www.ai.rug.nl/verheij/teaching/dmas/
• (Nestor contains a link)

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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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When is an agent-based solution appropriate?

• When the environment is open, dynamic, uncertain,
  complex
• When agents are a natural metaphor
• When data, control or expertise are distributed
• In case of legacy systems
• Legacy technologically obsolete, but
  functionally essential software

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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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Agent-oriented analysis and design techniques

• Goal
• To assist in understanding a system
• To assist in designing a system
• Techniques consist of models and guidelines
• Kinds
• Inspired by object-oriented development
  methodologies
• Adapted from knowledge engineering or other
  techniques

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AAII Methodology

• Australian AI Institute (1990s)
• Based on object-oriented methodologies
• Aimed at the construction of a set of models
  which define an agent system specification

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AAII Methodology
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AAII Methodology

• Identify roles in the application domain and from
  there develop an agent class hierarchy
• Identify the responsibilities, the services
  needed and provided by each role and determine
  the goals of each service
• Determine plans that can be used to achieve goals
  and context conditions for the plans
• Determine the belief structure of the system,
  i.e., the information requirements for each plan
  and goal

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Gaia

• Gaias goal is to allow a systematical route from
  system requirements to a detailed design that can
  be implemented directly
• A system is analyzed as an organization, which is
  viewed as a collection of interrelated roles.
• A role is defined by its responsibilities,
  permissions, activities, and protocols.
• Responsibilities determine system functionality
  and can be divided in liveness and safety
  properties.
• Something good happens vs. nothing bad happens

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Agent UML

• Based on the Unified Modelling Language (UML) for
  object-oriented modelling.
• The associated methodology is called Rational
  Unified Process
• Agent modifications
• Expression of concurrent interaction threads
• Roles
• To allow that agents can play several roles

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Desire

• Framework for the design and formal specification
  of compositional systems
• Specifies a graphical notation
• Support tools, such as a graphical editor

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Cassiopeia

• Bottom-up start with required behaviors
• Steps
• Identify elementary behaviors
• Identify relationships between elementary
  behaviors
• Identify organizational behaviors of the system,
  e.g., group formation

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Agents in Z

• An agent specification framework
• Entities with attributes
• Objects entities with capabilities
• Agents objects with goals
• Autonomous agents agents with motivations
• Emphasizes agents acting for one another instead
  of agents as rational systems

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Agent-oriented analysis and design techniques

• Dominant technique adapt object-oriented
  approaches
• Disadvantages
• Decomposition in objects at odds with
  decomposition in agents
• Many aspects of agent systems are not allowed in
  object-oriented methodologies
• (By the way where is the environment?)

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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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Pitfalls of agent development

• You oversell agents
• Agents are not magic!
• If you cant do it with ordinary software, you
  probably cant do it with agents
• You get religious
• Agents are not a universal solution
• You dont know why you want agents
• Agents new technology lots of hype!

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Pitfalls of agent development

• You want generic solutions to 1-off problems
• Occurs in many software projects
• Specific solution may be best
• You confuse prototypes with systems
• Prototypes are easy, field tested production
  systems are hard
• You believe agents are a silver bullet
• Silver bullet an order of magnitude
  improvement in software development
• Good reasons to believe that agents are useful
  way of tackling some problems, but these
  arguments are largely untested in practice

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Pitfalls of agent development

• You forget its software
• Developing any agent system is essentially
  experimentation. No tried and trusted techniques
• Mundane software engineering (requirements
  analysis, specification, design, verification,
  testing) is forgotten
• You forget its distributed
• Distributed systems one of the most complex
  classes of computer system to design and
  implement
• Typical multi-agent system will be more complex
  than a typical distributed system
• Make use of DS expertise
• You dont exploit concurrency
• If you dont exploit concurrency, why have an
  agent solution?

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Pitfalls of agent development

• You want your own architecture
• Great temptation to imagine you need your own
• Driving forces behind this belief
• not designed here mindset
• intellectual property
• But architecture development takes years and has
  no clear payback
• You use too much AI
• Temptation to focus on the AI aspects of the
  application
• Fuelled by feature envy, where one reads about
  agents that have the ability to learn, plan,
  talk, sing, dance
• You see agents everywhere
• Dont call your on-off switch an agent!
• Agents for addition, subtraction,

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Pitfalls of agent development

• You use too many agents
• More than 10 agents big system
• Large number of agents
• emergent functionality
• chaotic behavior
• Lessons
• keep interactions to a minimum
• keep protocols simple
• You use too few agents
• Fails software engineering test of cohesion
  software modules have a single coherent function
• You want to implement your own infrastructure
• By the time this is developed, project resources
  gone!

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Pitfalls of agent development

• Your system is anarchic
• Most agent systems require system-level
  engineering
• For large systems, or for systems in which the
  society is supposed to act with some commonality
  of purpose, this is particularly true
• Organization structure (even in the form of
  formal communication channels) is essential

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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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Mobile agents

• Mobile agents can transmit themselves

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Mobile agents

• Why mobile agents?
• Low-bandwidth networks (hand-held PDAs)
• Efficient use of network resources
• Issues
• Serialization (what is sent over the network? The
  agent program? Its data? Both? In what form?)
• Hosting and remote execution
• Security for hosts and agents
• Heterogeneity of hosts

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Telescript

• Commercial language-based environment for
  constructing multi-agent systems
• Aim palm-tops
• Main concepts places and agents

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Telescript

• Tickets specify a journey
• The journeys destination
• The journeys completion time
• Communication between agents
• Across a network
• Meeting
• Agents have a permit and resources, such as
  money, lifetime, size

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Telescript

• Agents and places are executed by a Java-style
  virtual machine
• Object-oriented language
• Interpreted, not compiled
• Two language levels high (what programmers
  see)/low (for efficient execution)
• Persistent execution continues after switching a
  system off and on

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Aglets

• Java-based mobile agents platform
• Some important methods of the Aglet class
• onCreation()
• run()
• dispatch()
• e.g., this.dispatch(new URL("atp//some.host.com/c
  ontext1"))

Contexts are hosts for agents
Agent Transfer Protocol
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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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Open issues

• Relationship with other software paradigms
• Specifically agent-oriented methodologies
• Engineering for open systems
• Engineering for scalability

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Overview

• When is an agent-based solution appropriate?
• Agent-oriented analysis and design techniques
• Pitfalls of agent development
• Mobile agents
• Open issues

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Student presentations