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					Intelligent Systems

      Lecture 25
 Multi-Agent Systems
             Main concepts of agent
• the beliefs that agents have—the
  information they have about their
  environment, which may be incomplete or
  incorrect;
• the goals that agents will try to achieve;
• the actions that agents perform and the
  effects of these actions;
• the ongoing interaction that agents have—
  how agents interact with each other and
  their environment over time.
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 Metamodel showing links between
        goals and tasks




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             Metamodel of concepts




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     Canonical view of a multi-agent system




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   ACL – Agent Communication Languages
• KQML (Knowledge Query and Manipulation Language) is an
  effort to standardise the communication language between software
  agents. The standard defines the semantical meaning of ASCII
  expressions, being exchanged in an agent community and makes
  abstraction of transport issues. From the outside, it appears as if
  each agent manages its own knowledge base, which typically
  consists of 'beliefs' and 'goals' (determining the agent's behavior).
  Part of this knowledge may be shared with other agents, which can
  query or manipulate it.
• KQML defines performatives for retrieval, insertion or deletion of
  knowledge ('ask' and 'tell', 'insert' and 'delete'), subscription to
  services ('subscribe'), publication of agent capabilities ('advertise')
  and others. Unfortunately many different flavours have been
  created, so there is little inter-operability between these systems.
• In the following example, agent 'john' tells some 'content' to agent
  'lisa' in reply to a previous message 'ref1', with the content
  expressed in the 'KIF' language using the 'family' ontology:
• (tell :sender john :receiver lisa :language KIF :ontology family
  :in-reply-to ref1 :content (<= (grandparent ?x ?z) (and (parent
  ?x ?y) (parent ?y ?z))))
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ACL – Agent Communication Languages (2)
• FIPA ACL (Foundation for Intelligent Physical
  Agents) The syntax of this FIPA ACL is very
  similar to KQML syntax rules. FIPA recognised
  the need for performatives that have well-
  defined semantics, but also that its proposed
  semantics or "pragmatics" need further testing
  before they really can be considered as
  normative. So, ACL hasn't yet proven its use in
  an open environment such as the Internet.
  Interoperability between FIPA compliant
  platforms is one of the key issues tackled by a
  European ACTS project FACTS (FIPA Agent
  Communication Technologies and Services)

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ACL – Agent Communication Languages (3)
• The Knowledge Interchange Format (KIF) defined by
  Stanford University .
• KIF is capable of representing first order predicate logic
  and its syntax is based on Common LISP.
• KIF facilitates the exchange of knowledge propositions
  between artificial intelligence systems and was used in
  conjunction with KQML in major American research
  projects on Knowledge Representation and collaborative
  multi-agent systems.
• As an example of KIF content, we've included a logic
  sentence "if x is a parent of y, and y is a parent of z, x is
  a grandparent of z", in the above example of a KQML
  message.


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ACL – Agent Communication Languages (4)
• The Semantic Language (SL). Like KIF, the
  Semantic Language recently proposed by FIPA
  has been defined to express first order predicate
  logic.
• It has been declined into three different profiles :
     – SL0 for the sake of agent management essentially,
       where only simple actions and propositions can be
       expressed,
     – SL1 with additional Boolean operations,
     – SL2 which only allows to express decidable
       propositions
• The eXtensible Markup Language (XML)
• This is advanced HTML for representation of
  structure of objects and relations between them
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                          Mobile agent
• Mobile objects, sometimes called mobile agents, are bits of code that
  can move to another execution site (presumably on a different machine)
  under their own programmatic control, where they can then eciently
  interact with the local environment. Commercial instantiations of this
  technology include Aglets from IBM, Concordia from Mitsubishi, and
  Voyager from ObjectSpace.
• Advantages:
     – Network bandwidth: for some database queries or electronic commerce
       applications, it is more ecient to perform tests on data by bringing the tests
       to the data than by bringing large amounts of data to the testing program.
     – Parallelism: mobile agents can be spawned in parallel to accomplish many
       tasks at once.
• Disadvantages:
     – In a fashion similar to that of DOOP programming, an agent developer must
       programmatically specify where to go and how to interact with the target
       environment.
     – There is generally little coordination support to encourage interactions
       among multiple (mobile) participants.
     – Agents must be written in the programming language supported by the
       execution environment, whereas many other distributed technologies
       support heterogeneous communities of components, written in diverse
       programming languages.
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             Swarm Intelligence
• www.swarm.org
• Swarm Intelligence (SI) is the property of a
  system whereby the collective behaviours of
  (unsophisticated) agents interacting locally with
  their environment cause coherent functional
  global patterns to emerge.
• SI provides a basis with which it is possible to
  explore collective (or distributed) problem
  solving without centralized control or the
  provision of a global model.
• Colony of ANT

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             Applications of MAS
• WEB-services
• Business distributed applications (planning
  and control of business, in particular,
  based on Internet/Intranet)
• Searching systems
• Cooperation of robots (for example, in
  soccer and battle field)
• Programming of AI in games

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        A Multi-Agent Platform for On-line
                Services (MAPOS)

Most initiatives that recently tried to use intelligent agents
for some kind of electronic commerce followed a similar
approach : a user contacts an intermediate platform
that hosts his Personal Assistant (PA) and some brokerage
services. The transaction life-cycle follows a variation
of the Contract-net Protocol between Personal Assistant,
Service Broker (SB) and On-line Provider (OLP) agents,
which represent the sellers or content providers.
After service completion, billing/after sales aspects
are controlled by the service broker.
This scenario is illustrated in Figure.

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        A Multi-Agent Platform for On-line
              Services (MAPOS) (2)




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        A Multi-Agent Platform for On-line
              Services (MAPOS) (3)
• The MAPOS platform has been completely
  written in Java and has been based on the Java
  Agent Template (JAT), which uses KQML mainly
  for agent management purposes. As content
  language we used XML embedded within KQML
  messages. As the OLP and its SCMs belong to
  the same entity, the interfaces between are
  normally more proprietary, which we reflected in
  choosing here RMI as communication
  mechanism. An overall picture of the MAPOS
  architecture is given in Figure
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             The MAPOS architecture




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        MAS in operation planning
• Operation planning is the most prevalent task in
  commercial operations
• The tasks of Operation Planning and Scheduling (OPS)
  under multiple resource and temporal constraints are of
  the keenest interest among other tasks
• Although OPS problem was studied for several decades,
  it remained to be a subject of the permanent research
  aiming at development of an approach and architecture
  that allow to cope with its high computational complexity.
• At present new perspectives emerge due to new
  accomplishments and achievements in the in the area of
  intelligent agent-based technology


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   MAS in operation planning (2)
• The model of OPS task is formulated in terms of
  the contract allocation, when contractors are
  meant as service providers.
• Each agent is implemented as an autonomous
  intelligent software agent.
• In this metaphor, each contract is considered as
  a single operation scheduled for a service
  action.
• Contract allocation task is solved on the basis of
  auction-based negotiation protocol managed by
  the meta-agent. Auction is used as a
  coordination mechanism
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         Meta-agent domain specified in terms of
         • Entire list of operations to be executed within a schedule
         • Plan and schedule of operation execution
         • Quality of Service vector assessing the plan
         The task of meta-agent
         Coordination of the particular decisions of agent-contractors


                                           AC 1                          AC 4

                     MA                    AC 2

                                                                         AC 5
                                           AC 3


                        Agent-contractor domains is specified in terms of
                        • Resources
                        • Multitude of constraints to be met by it
                        • Scenarios of operation execution
                        • Plan and schedule of operation to executed by it
                        Agent’s task
                        • Constraints satisfaction
                        • Maximal profit


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              Agents’ interaction protocol
• An “auction” is a metaphor of agents’ negotiation protocol to
  coordinate particular decisions to meet the entire batch of
  constraints and to maximize total benefit of OPS. An “auction”
  consists of a sequence of particular phases, which are called
  “bargains” and aimed at allocation a single operation (single
  “service” providing) to an agent-contractor. Each bargain scenario
  consists of several meta-agent’s action that are like the following
  ones:
• announcement of a service to be provided,
• receiving and assessment of the proposals (“bids”) of agent-
  contractors,
• selection of a winner, i.e. an agent-contractor which is supposed to
  provide the respective service.
• An agent-contractor performs the following tasks:
• receives announcement,
• evaluates its capabilities to provide service announced and makes
  decision,
• returns decision to meta-agent,
• planes to provide a service if win.
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       Meta-agent. Knowledge base
              components
                    Strategy and management of auction


                       Operation announcement order


                    Criteria for selection a bargain winner



         Criteria for selection the final decision from generated ones




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             Agent-contractor. Knowledge base
                       components

                            Constraints satisfaction


             Specification of the scenario of each single operation


                                    Actions


                                  Scenarios


                             Model of resources


                           Quality of service vector



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The architecture of basic technology for
business based on MAS (A.N.Terekhov, A.M.Kudinov,
S.I.Makarov, R.A.Boudagov “Basic Technology for creating Mobile
Distributed Systems”, Proc. of Int. Workshop of Management, 2002)

 • Methodologies, which have to be used in different stages
   of system’s life cycle.
 • The referent model of data domain as different
   representations and the unified method of description of
   company.
 • The ontological basis, defining basic terms of data
   domain and its interdependence.
 • Language tools for model analysis of specifications,
   formalization and programming.
 • Toolkits automating basic process of information system
   construction life cycle (specifications and analysis,
   engineering, testing, projects management).
 • The structure of technological platform includes FIPA
   and OMG M
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      One main principle of building of systems for
                       business
• Multi-agent construction of toolkits and applicable tools.
• The main feature of engineering and reengineering of mobile
   information systems is a dynamic of construction of new
   business rules and effective modeling of network
   organizations.
• Multi-agent systems are used both for technological and
   application-oriented problems.
• Multi-agent infrastructure is in fact a multilevel shell of the
   information system, which represent business rules and
   communication of its participants.
• The main characteristic of multi-agent system is its mobility.
   The mobility is represented in four aspects – mobile users,
   computers, programs and data.
• Hereinafter the mobile global information system will be
   considered as a set of technological and software tools
   mounted on mobile and stationary objects (cars, airplanes,
   ships, trains) and executed software modules, represented
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   as mobile agents.
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                     Agent's types
• Adapter-agent. The agents of this type are designed for using on
  the nodes of corporative network, containing data sources These
  agents are responsible for getting information from databases, its
  conversion to the type required by used ontology and its transferring
  to collectoragent. This agents can be dynamically configured for
  particular data storage by means of request vocabulary in XML
  format.
• Collector-agent. These agents are responsible for collecting
  information from distributed sources and its storing in common
  corporative database represented as a General Ledger Facility. The
  mobility of collector-agents increases the efficiency and reliability of
  intercommunication with adapter-agents. They can be moved to the
  network node, witch contains data storage and corresponding
  adapter-agent..
• Administrator-agent. This agent provides user’s interface for
  remote monitoring and agent’s system control. Administrator-agent
  may activate the full system of agents by means of only one
  terminal. Therefore, the information system administrator, using the
  access to local area network can get information about all agents
  and CORBA-servers and also configure them without interruption of
  the work of the system.
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posted:10/6/2012
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