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ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM]. MULTI-AGENT SYSTEMS DESIGN. Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design

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ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM]

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ARTIFICIAL INTELLIGENCE[INTELLIGENT AGENTS PARADIGM]

MULTI-AGENT SYSTEMS DESIGN

Professor Janis Grundspenkis

Riga Technical University

Faculty of Computer Science and Information Technology

Institute of Applied Computer Systems

Department of Systems Theory and Design

E-mail: Janis.Grundspenkis@rtu.lv


Agent development

  • Practical agent development includes:

    • Requirements analysis

    • Design

    • Implementation

    • Testing

    • Deployment

    • Maintenance


Agent oriented software

  • New software engineering paradigm proposed by Schoham

  • Initial idea is to program agents directly in agents based concepts like belief, goal plan, etc.

    • Now agent interactions are becoming central concept

  • Agents are basic software components in AO approach like objects in OO


Agent development

  • Separate research direction – agent oriented software engineering (AOSE)

    • Tries to answer the question – how to develop software systems whose components are agents


Concepts

  • Agent oriented (AO) software – software that consists of agents

  • All steps are similar to OO

    • OO programming  AO programming

    • OO analysis  AO analysis

    • etc.


When the agents based solution is suitable?

  • Agent based solution is suitable if:

    • The environment is open or at least dynamic, undeterministic and complex

      • Systems capable of autonomous actions usually is the only solution in such environments

    • Agents are natural metaphor

      • Many systems can be naturally modelled as agent societies, that either cooperate to solve complex problems or compete

      • Including organizations and any commercial or competitive environment

    • Distributed data control or competences

      • In many systems centralized solution is ineffective or impossible

    • Legacy systems


AOSE methodologies

  • AOSE is a complex process. It is hard to handle it without methodological support

  • Definition: AOSE methodology is a set of methods used in agent oriented software development


What should good methodology provide?

  • Precisely defined development process

  • Techniques to carry out each step

  • Corresponding AOSE concepts

  • Notation for diagrams/models

  • CASE tools

  • Mechanisms/algorithms to get the program code from the design


Life cycle

  • Essentially the same as OO

  • Some methodologies use modified RUP, others (majority) use ~ iterative waterfall

  • We will analyse the following phases

    • Analysis

    • Design

    • Implementation

    • Testing

    • Deployment

    • Maintenance


Analysis

  • The requirements must bedefinedin the form needed in the following phases

  • The following techniques are used in the analysis phase:

    • Use case modelling

    • Goal hierarchy

    • Task hierarchy

    • Domain modelling/Environment analysis

      Ontologies


Use case modelling

  • Various techniques are used:

    • Use case diagram

    • Use case scenarios

    • Use case maps

    • Internal use cases

  • Application similar to OO approach


Example of use case diagram


Example of use case description


Use case map

  • Shows how the use case is executed through agents

  • It is used to identify the need for communications between agents

  • If two consecutive steps are done by different agents, communications between these agents are needed


Internal use cases

  • Shows how agents use other agents

The corresponding message sequence chart(MSC)


Goal modelling

  • Especially useful in case of goal based agents

  • Well understandable by domain experts

  • Goals rarely change during the development

  • Goal hierarchy is created as a result


Task decomposition

  • Similar to goal modelling

  • Tasks usually are less abstract and in lower levelthan goals

  • Goal model is more suitable for BDI (Belief, Desire, Intention) and similar agents

  • Task modelling is more suitable for reactive behaviour based agents

  • Corresponding actions/behaviours can be created for each task

  • Roles usually correspond to goals

  • Use cases can be created according to goals


Organisational modelling

  • Suitable for systems that must be well integrated into organizations

  • Organization’s structure is modelled

  • Stakeholders, organizations, their units and roles as well as interactions among them are determined


Domain modelling

  • The environment where the agents will act is analysed

  • Domain class model is obtained

  • Domain ontology is usually created from the class model

  • Ontology then isused in communications and to describe agent’s knowledge


Design

  • Design is the phase that differs the most from OO

  • Usually is split into two stages:

    • 1st stage answers the question: what the agents must do and how do they interact?

      • External design of agents

      • High level design

      • Architectural design

      • It can be considered as a design of multi-agent system

    • 2nd stage answers the question: how will the agents achieve their functionality?

      • Internal design of agent

      • Low level design

      • Detailed design


Agent definition

  • Can be included in

    • Analysis phase if the agents are requirements

    • In design phase if the agents are just a way to implement requirements

  • Agents can be defined for

    • Users

    • Organizations/stakeholders

    • Legacy systems

    • Roles

    • Use cases

    • Tasks

    • Types of knowledge


Interaction design

  • Specifies how the agents interact

  • 3 possible levels

    • Acquaintance level

    • Messages sent among agents

    • Formal interaction protocols

      • Request Protocol

      • FIPA Contract Net


Acquaintance model

  • Only the interacting pairs of agents are defined

  • Very simple

  • In many cases insufficient


Messages sent

  • Shows what messages are sent among agents

  • No ordering and context of messages


Interaction protocols

  • Specify order of messages sent and thus the context of every message

  • For every message it is defined how the agent can respond to it

  • De facto standard – UML protocol diagram

  • Protocols are reusable


The 2nd stage of the design

  • For each agent designer must specify:

    • Percepts(including messages received)

    • Actions in the enviroment (including mesages sent), plans

    • Knowledge, beliefs

    • Reasoning process

    • Architecture

      Possibly: roles, capabilities, tasks goals


Abstract internal design of agents


Implementation

  • Choose the implementation platform

  • Convert the concepts used in the design to ones used in theimplementation platform

  • Implement the system in the chosen platform

    • Generate code from the design (if possible)

    • Complete the generated code


Development tools

  • Tools for diagram drawing

  • Crosschecking among diagrams

  • Diagram transformations/partial generation of diagrams and/or elements

  • Main function: code generation


Examples of tools: PDT and agentTool


MASITS and IDK


Testing

  • The most weakly developed phase

  • At the same time, testing of distributed systems is complex

  • Usually (adapted) classical methods are used

    • For example, black box methods can be used for any system

  • Specific tools like JADE Test Suite alreadyexist


Deployment

  • Define particular instances of each agent, their location and migration

  • Allows to easily change the system without changing the design


Maintenance

  • Essentially the same as in OO approach

  • Additionally, agents give openness and high modularity to the systems

    • Openness allows to change functionality by adding/removing agents

    • High modularity simplifies change implementation into separate parts of the system


Summary of current situation

  • Many different methodologies exist

  • No single methodology is usable for all kinds of agents

  • Weak coupling with the implementation platforms

  • Many steps are still unclear, for example, testing

  • Specific purpose methodologies are developed to fit needs of specific types of systems


Mobile agents

  • Agents that are capable to move themselves over the network

    • Code

    • Internal state

  • The idea is to provide an alternative to remote procedure calls

  • Example of remote procedure call

    V=B.m(args)

    • Communication is synchronous

    • What happens if the process B does not return value?

    • Network connection remains open?

  • Alternative: send mobile agent to the process B to execute the needed operations in the common address space


Mobile agents

  • Remote procedure calls(a) in comparison to mobile agents (b):


Mobile agents

  • Why needed?

    • Effective usage of low bandwidth networks (smartphones, tablets, etc.)

  • Lots of problems must be solved to develop software platforms for mobile agents

    • Security for both hosts and agents

    • Heterogeneity of hosts

    • Dynamic coupling


Host security

We do not want to execute unknown software on our computers, because it is dangerous:

  • If the programming language supports pointers, then there is a risk to damage the computers address space

  • Access rights to the host PC must be defined

  • Many actions may be safe in one case and malicious in other. For example, sending an e-mail usually, but not always, is safe


Host security

  • Many agent languages (like TELESCRIPT) limit the amount of memory and CPU that is available to mobile agents

  • Safe parallel processor is a solution. It can be given to an agent that executes in the separate address space i.e. in quarantine

  • Some languages allow to check security characteristics of the agent upon recieving

    Hosts must process with crashed software. What to tell the owner if his software has crashed?


Agent security

  • Agent’s code is private

  • We want to sent our code without allowing the receiver to determine its goal and thus our objectives

  • Agent can be modified without the owner knowing it

  • Cryptography can protect agent during the migration

  • Various digital signatures based on checksums are used to check if the agent is modified


Host heterogenity

  • If the agents capable to execute only on one kind of machines (Mac, PC, etc.) are not sufficient then we need an infrastructure allowing them to execute in different environments

  • Thus we need

    • InterpretablelanguagesCompiled languages use machine code that is platform dependant

    • Dynamic linking

      Local resource access libraries must provide common interface for different environments


Types of mobile agents

  • Mobile agents can be divided in at least 3 types

    • Autonomous

    • Upon request

    • Active mail agents


Autonomous mobile agents

  • Autonomous mobile agents can choose by themselves where to migrate as well as when and what to do at the destination based on the available resources (something like e-money)

  • Such agents can be implemented in languages that offer command go. The best known example of such language is TELESCRIPT


Mobility upon request

  • The host runs the agent only if it explicitly requests it

  • The best known example is Java object included in the HTML code

  • The browser opens html page that contains applets – small Java software. These applets are downloaded together with the webpage and are executed in the client PC


‘Active’ mail agents

  • Scripts are sent by e-mail messages

  • Upon receiving the e-mail the agent is unpacked and executed. E-mail becomes active instead of passive


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