Main concepts of agent
• the beliefs that agents have—the
information they have about their
environment, which may be incomplete or
• 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.
Metamodel showing links between
goals and tasks
Metamodel of concepts
Canonical view of a multi-agent system
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))))
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)
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
• 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
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
• It has been declined into three different profiles :
– SL0 for the sake of agent management essentially,
where only simple actions and propositions can be
– SL1 with additional Boolean operations,
– SL2 which only allows to express decidable
• The eXtensible Markup Language (XML)
• This is advanced HTML for representation of
structure of objects and relations between them
• 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.
– 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.
– 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
– 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
• 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
Applications of MAS
• 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
A Multi-Agent Platform for On-line
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.
A Multi-Agent Platform for On-line
Services (MAPOS) (2)
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
The MAPOS architecture
MAS in operation planning
• Operation planning is the most prevalent task in
• 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
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
• Contract allocation task is solved on the basis of
auction-based negotiation protocol managed by
the meta-agent. Auction is used as a
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
Agent-contractor domains is specified in terms of
• Multitude of constraints to be met by it
• Scenarios of operation execution
• Plan and schedule of operation to executed by it
• Constraints satisfaction
• Maximal profit
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
• announcement of a service to be provided,
• receiving and assessment of the proposals (“bids”) of agent-
• 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
• returns decision to meta-agent,
• planes to provide a service if win.
Meta-agent. Knowledge base
Strategy and management of auction
Operation announcement order
Criteria for selection a bargain winner
Criteria for selection the final decision from generated ones
Agent-contractor. Knowledge base
Specification of the scenario of each single operation
Model of resources
Quality of service vector
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
• 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
One main principle of building of systems for
• 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
• Multi-agent systems are used both for technological and
• 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
as mobile agents.
• 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
• 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
• 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.