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Design and Analysis Methods for Multi-Agent Systems

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Title: Design and Analysis Methods for Multi-Agent Systems


1
Design and Analysis Methods for Multi-Agent
Systems
  • California State University , Los Angeles
  • Dr. Jiang Guo
  • Fall 2010
  • Presented by
  • Behin Behdinian
  • Sanaz Bonakdar
  • Kate Dehbashi
  • Monali Bhavsar
  • Amee Joshi

2
Outline
  • Background of Multi Agent System
  • History
  • Background
  • Agents
  • Multi Agent System
  • Overview
  • Properties
  • Features
  • Applications
  • Advantage
  • Current Projects and Future Works
  • AOSE
  • AOSE Methodologies
  • Gaia
  • AAII
  • Agent UML
  • DPMAS
  • Available Tools

3
Outline
  • Technical Issues on MAS
  • Development process of MAS
  • key issues where the current state-of-the-art is
    lacking
  • Agent oriented methodologies weaknesses
  • The lack of attraction for methodology user to
    use the agent-oriented paradigm
  • The lack of attraction for methodology user to
    useexisting agent-oriented methodologies
  • Solutions
  • Solutions of three key issues where the current
    state-of-the-art is lacking
  • Agent oriented methodologies Solutions
  • Solution to agent-oriented paradigm
  • Solution to existing agent-oriented methodologies
  • Agent OPEN method
  • Feature-based method

4
Introduction
  • A multi-agent system (MAS) is a system composed
    of multiple interacting intelligent agents.
  • Multi-agent systems can be used to solve problems
    which are difficult or impossible for an
    individual agent or monolithic system to solve.

5
History
  • The idea of agent-based modeling was developed as
    a relatively simple concept in the late 1940s.
  • Since it requires computation-intensive
    procedures, it did not become widespread until
    the 1990s

6
Background
  • Von Neumann machine
  • Cellular Automata
  • Game of Life
  • Thomas Schellings segregation model(1971)
  • Prisoners Dilemma (early 1980)
  • Flocking models (late 1980)
  • John Holland John H. Miller (1991)

7
Background
  • StarLogo (1990)
  • SWARM and Netlogo (mid 1990)
  • Repast (2000)
  • Samuelson(2000, 2005)
  • Bonabeau(2002)
  • Samuelson and Macal(2006)

8
The study of multi-agent systems
  • Agent-oriented software engineering
  • Beliefs, Desires, and Intentions (BDI)
  • Cooperation and Coordination
  • Organization
  • Communication
  • Negotiation
  • Distribution problem solving
  • Multi-agent learning
  • Scientific communities
  • Dependability and fault-tolerance

9
What is an Agent
  • an agent is a computer system capable of
    autonomous action in some environment, in order
    to achieve its delegated goals.

10
Agent characteristic in a multi-agent system
  • Autonomy the agents are at least partially
    autonomous
  • Local views no agent has a full global view of
    the system
  • Decentralization there is no designated
    controlling agent

11
Type of Agent
  • 1956present Symbolic Reasoning Agents
    Its purest
    expression, proposes that agents use explicit
    logical reasoning in order to decide what to do.
  • 1985present Reactive Agents
    Problems with
    symbolic reasoning led to a reaction against this
    led to the reactive agents movement,
  • 1990-present Hybrid Agents
  • Hybrid architectures attempt to combine the best
    of symbolic and reactive architectures.

12
Simple reflex agent
13
Learning agent
14
Multi-agent systems
  • Typically multi-agent research refers to software
    agents
  • However, the agents in a multi-agent system could
    be
  • robots
  • humans
  • human teams
  • combined human-agent teams.

15
Overview Multi-agent
  • Multi-agent systems can manifest
    self-organization and complex behaviors even when
    the individual strategies of all their agents are
    simple.
  • Agents can share knowledge using any agreed
    language, within the constraints of the system's
    communication protocol.
  • Knowledge Query Manipulation Language (KQML)
  • FIPAs Agent Communication Language (ACL).

16
MAS Properties
  • MAS is "self-organized systems and tend to find
    the best solution for their problems "without
    intervention".
  • Physical phenomena, such as energy minimizing,
    where physical objects tend to reach the lowest
    energy possible, within the physical constrained
    world.

17
MAS Main feature
  • Flexibility
  • multi-agent system can be
  • Added to,
  • Modified
  • Reconstructed
  • Do not need to rewrite detailed of the
    application.
  • These systems also tend to be rapidly
  • Self-recovering
  • Failure proof
  • Self managed features

18
Applications of Multi-Agent Research
  • aircraft maintenance
  • electronic book buying coalitions
  • military demining
  • wireless collaboration and communications
  • military logistics planning
  • supply-chain management
  • joint mission planning
  • financial portolio management

19
Advantages of a Multi-Agent
  • MAS distributes computational resources and
    capabilities across a network of interconnected
    agents. Whereas a centralized system may be
    plagued by resource limitations, performance
    bottlenecks, or critical failures
  • MAS is decentralized and thus does not suffer
    from the "single point of failure" problem
    associated with centralized systems.

20
Advantages of a Multi-Agent
  • MAS efficiently retrieves, filters, and globally
    coordinates information from sources that are
    spatially distributed.
  • MAS provides solutions in situations where
    expertise is spatially and temporally
    distributed.
  • MAS enhances overall system performance,
    specifically along the dimensions of
    computational efficiency, reliability,
    extensibility, robustness, maintainability,
    responsiveness, flexibility, and reuse.

21
Current Projects Future Works
  • AOSE Agent Oriented Software Engineering
  • AOSE Methodologies
  • Gaia The Gaia Methodology for Agent-Oriented
    Analysis and Design
  • AAII Formal models and decision procedures for
    multi-agent systems
  • Agent UML A formalism for specifying multi-agent
    software systems
  • DPMAS A Design Method for Multi-agent System
    using Agent UML

22
AOSE
23
AOSE Agent Oriented Software Engineering
  • The agent-oriented (AO) the ability to construct
    flexible systems with complex behavior by
    combining highly modular components
  • Agent-oriented development toolkits mostly use in
    industry
  • Agent-orientation is a paradigm for analysis,
    design and system organization.
  • AOSE is a new field, methodologies far less
    established than object-oriented software
    engineering methods
  • AOSE Methodologies
  • Gaia
  • AAII

24
Knowledge Level
  • Agent-oriented modeling borrows from the study of
    human organizations and societies in describing
    the way in which agents in a Multi-Agent System
    work together
  • And from artificial intelligence (AI) to
    describe the agents themselves.
  • These additional concepts can be defined in terms
    of object-oriented ones which deal with ideas and
    structures at a higher level the knowledge
    level
  • Knowledge Level Categories
  • ConcreteEntity, Activity, and MentalStateEntity.

25
Concrete Entity Types
  • Agent An atomic autonomous entity that is
    capable of performing some useful function.
  • Organization An Organization is a group of
    Agents working together to a common purpose.
  • Role A Role describes the external
    characteristics of an Agent
  • Resource Resource is used to represent
    non-autonomous entities such as databases or
    external programs

26
Activity Types
  • Task A Task is a knowledge-level unit of
    activity with a single prime performer
  • Interaction Protocol Defines a pattern of
    Message exchange associated with an Interaction

27
Mental State Entity Type
  • Goal A Goal associates an Agent with a
    Situation.
  • Two other simple but important concepts used in
    AOSE using MESSAGE/UML are Information Entity
    and Message
  • A Message is an object communicated between
    Agents
  • Information Entity is a content of the Message

28
Figure 1 gives an informal agent-centric overview
of how these concepts are interrelated, showing
their relationship to the agent concept
29
AOSE Methodologies
30
AOSE Methodologies
  • Analysis and Design methodologies
  • Set of model and guidelines that aid in
    understanding the system
  • Two approaches
  • Adoption Extensions of OO approach
  • AAII
  • Adoption of other techniques
  • Gaia

31
Gaia
  • Inspired by OO concepts
  • Also provides agent-specific set of concepts
  • Concepts
  • Abstract used during conceptualization
  • Roles, Permissions, responsibilities,
  • Concrete direct counterparts in implementation
  • Agent types, Services,
  • Analyst moves from abstract to concrete concepts
  • Agent-based system artificial society

32
Gaia Abstract Concepts
33
Gaia Analysis
  • Role Schema
  • Identifies key roles in the system
  • Interaction model
  • Represents links between roles
  • Set of protocol definitions consisting of
  • Purpose
  • Initiator
  • Responder
  • Inputs/outputs
  • processing

34
Gaia Role Schema
35
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36
Gaia Role Schema example
37
Gaia Interaction model example
38
Gaia Design
  • Create an agent model
  • Documents variant agent types and instances of
    each agent
  • Aggregates roles into agent types
  • Develop a service model
  • Specifies functions of an agent
  • Develop an acquaintance model
  • Document lines of communication between the
    agents
  • Purpose identify communication bottlenecks
  • Nodes agents
  • Arrows communication pathway

39
Gaia acquaintance model
40
Gaia Usage
  • Appropriate for large-scale real-world
    applications in which
  • Agents are coarse-grained computational systems
  • Agents are heterogeneous
  • System organization structure is static
  • Ability of agents and services are static
  • System contains small number of agent types

41
AAII
  • Extension of OO methods based on experience of
    Australian AI institute with BDI-like systems
  • Example air-traffic management system
  • Internal models internal detail of agents
  • Agents have mental attitudes
  • beliefs (informative)
  • desire (Objective to be accomplished)
  • intention (deliberative component)
  • External models
  • Concerns with interactions not internals of
    agents

42
AAII Analysis and Design
  • Identify roles and develop agent class hierarchy
  • Identify responsibilities, services and goals
  • Determine plans that can be used to achieve each
    goal
  • Determine information requirements necessary to
    represent and process plans and turn them into
    appropriate belief structure for the agents

43
AAII Decision Tree
  • AAII uses decision tree to model the behavior of
    the system
  • Choice nodes
  • Chance nodes

44
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45
Agent UML
46
Agent UML Why Created?
  • At the beginning, AOSE was not completely
    accepted in the industry
  • FIPA and OMG cooperated to increase the
    acceptance of AOSE in industry (1999-2000). How?
  • Relating to OO software development standard
  • Supporting the development environment for the
    software lifecycle
  • First result of the cooperation
  • Agent UML

47
Agent UML What is it?
  • An extension to the UML
  • Agent Active Object that can say go and no
  • More sophisticated capabilities
  • Mobility
  • Reasoning about knowledge
  • Promotes standard representations of UML to
    support agent software
  • Example Protocol diagrams
  • Protocol diagrams To show multi agent reaction

48
Agent UML Protocol Diagram
  • UML extension for the specification of Agent
    Interaction Protocol
  • AIP
  • Describes a communication pattern, with
  • Allowed sequence of messages between agents
  • Constraints on the content of the message
  • Example Ticket market
  • Uses FIPA English-Auction Protocol

49
Protocol Diagrams Elements
  • Agents/Roles
  • Lifeline/Interactions
  • May split up to show decisions

50
Protocol Diagrams Elements (Cont.)
  • Nested/Interleaved Protocols

51
Protocol Diagrams Elements (Cont.)
  • Asynchronous /Synchronous / Mobile messages
  • Massage Label
  • Communicative Act
  • Arguments for additional information

52
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53
DPMAS
54
DPMAS A Design Method for Multi-agent System
using Agent UML
  • DPMAS uses agent UML, an extension of the UML in
    Object Oriented domain, as its modeling language
  • DPMAS tells designers how to design a multi-agent
    system step by step, specially using agent unique
    concepts
  • It provides a group of graphs to help designers
    in modeling multi-agent systems
  • DPMAS suggests a design process that covers
    requirement specification, task specification,
    agent specification and deployment description
  • DPMAS is a design method closer to real software
    development.
  • A tool called AUMP has been developed to aid
    designers in drawing graphs used in DPMAS
  • AUMP is the design platform of the well known
    MAGE agent environments

55
DPMAS Concepts
  • An agent is an instance of an agent class.
  • Agent class is the software implementation of an
    autonomy entity and the basic unit of source
    codes organization.
  • An agent achieves its goal through decision,
    action and interaction.
  • An agent goal is relevant to a system task.
  • A task is an item of work that users may require
    the system to do. Usually, a task is performed by
    a sequence of actions.
  • A task needs the cooperation of several roles.
  • Each role can be played by an agent. An agent can
    play multiple roles because it can participate in
    multiple tasks and play different roles in
    different tasks.

56
DPMAS Process
  • Domain description describes the system
    functions using Uses case graph.
  • Agent finding determines how many agents the
    system need, what types they are, and the
    interactive relationships between them.
  • Task specification describes how the system
    functions are fulfilled.
  • Activity task
  • needs more actions than interaction
  • activity graph is used to describe the actions
    firstly, and then protocol graph may be used to
    describe the interactions between involved roles
  • Interaction task
  • needs more interactions than actions
  • Interaction graph is used to describe protocol
    firstly and followed with activity graph.
  • Ontology specification defines the knowledge
    representation in the system.
  • The knowledge representation is used when an
    agent needs to make decisions and explain the
    messages of other agents.

57
DPMAS Process
  • Agent specification gives the detail definition
    of every agent class.
  • Code generation is performed automatically
  • Deployment shows the runtime configuration of
    the system using deployment graph, including the
    physical position, the possible clone and move of
    every agent.

58
DPMAS Process Figure
59
Future Works
  • Future works will be focused on model reuse and
    code reuse.
  • A model template library and a code template
    library are under development.
  • With these libraries, designers will be able to
    select an existing model template if it suits the
    functional requirement well.
  • This will save a lot of design time , many models
    dont need be design from very beginning

60
Available Tools
61
Multi-Agent Platforms
  • AgentBuilder is an integrated tool suite for
    constructing intelligent software agents
  • Jack is an environment for building, running and
    integrating commercial JAVA-based multi-agent
    systems using a component-based approach
  • Zeus is an integrated environment for the rapid
    building of collaborative agents applications
  • MAGE ( Multi-AGent Environment ) is an agent
    oriented programming environment

62
MAGE An Agent-Oriented Software Engineering
Environment
  • It provides complete tools to support
    agent-oriented requirement analysis, design,
    development and deployment
  • The idea is to create a relatively general
    purpose and customizable toolkit that could be
    used by software users

63
Modeling tool AUMP
  • Step1 Use Case Model to describe system
    functions
  • Step 2 behavior modeling which is used to
    resolve how to achieve the Use Cases .There are
    six types of agent behavior model
  • Activity Model, State Chart Model, Interaction
    Protocol Model, Plan Model, Inference Model and
    Reactive Rule Model
  • Step 3 agent modeling which is used to
    demonstrate the agent system structure
  • Step 4 system deployment modeling describes how
    agents are distributed and how the system runs

64
AUMP A DPMAS Tools Support
65
Development tool VAStudio
  • A visual and integrated development environment
    for developing MAS
  • Agent-based programming environment
  • Friendly and easy-to-use interface
  • It directly loads the result of design process of
    AUMP and generate the corresponding agent or MAS
  • It provides plenty of agent templates
  • It provides plenty of behavior components which
    can be used to buildup agents.

66
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67
Deployment tool MDeployer
  • It provides powerful functions for users to use
    Start New Agent, Kill Agent, Send
    Message
  • It provides a tool through which users can deploy
    and manage the whole system through web

68
Future Works
  • MAGE is a powerful development environment for
    autonomous computing
  • Future works will be focused on autonomic
    computing and agent-based grid

69
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70
Application e-Business
71
  • Technical Issues on Multi Agent system

72
Development process of MAS
  • We consider the state-of-the-art development
    process to be as follows
  • Designing organization and individual agents
    using an AOSE methodology
  • Taking the resulting design and (manually) coding
    the agents in some AOPL, based on the design
  • Debugging the system using message tracing and
    agent inspectors
  • Possibly using model checking on agent code

73
Key Issues
  • There are three key issues where the current
    state-of-the-art is lacking,
  • The implementation is developed completely
    manually from the design.
  • This creates the possibility for the design and
    implementation to diverge, which tends to make
    the design less useful for further work in
    maintenance and comprehension of the system.
  • Code and design are completely different beasts.
  • Current state-of-art is lacking in linking design
    and code.
  • So, we need to remove inconsistencies between
    design and code. i.e. eliminate the gap between
    them.
  • Integrating code and design we need the common
    language. And for that we need the many AOPLs.
    Having to develop link between each possible
    design tool and each possible language, need of
    single methodology and single AOPL.
  • More practical approach is to standardize
    interchange formats and APIs to remain diverse
    so it will require more efforts and collaboration
    within the AOSE and ProMAS communities.

74
Continue
  • Although present AOPLs provide powerful features
    for specifying the internals of a single agent,
    they mostly only provide messages as the
    mechanism for agent interaction. AOPLs are weak
    in allowing the developer to model the
    environment within which the agents will execute.
  • AOPLs focus on internals of agents but neglect
    the social and organizational aspects.
  • Reason for neglecting these issues is the lack of
    clear and computational semantics.
  • Work on those areas does not focus on designing
    agent programming languages.
  • Current implementation of AOPLs at message level
    gives brittle interactions also, designing and
    implementing at this level makes it very hard to
    verify /debug modify interaction between
    agents.

75
Continue
  • In most of the practical approaches for
    verification of multi-agent systems, verification
    is done on code. More work is required in
    verification of agent design artifacts.
  • As verification validation done on code, MAS
    are hard to debug also be difficult to detect or
    produce the exact circumstances that cause
    problem.
  • Large number of agents makes things more
    difficult to consider the consequences of changes
    for participating agents. Because each agent has
    complex structure which not only inspected but
    need to be understood. So typically a large
    number of messages analyzed in conjunction with
    agents.
  • In fact, all the work on model checking for
    multi-agent systems is still in early stages so
    not really suitable for use on large and
    realistic systems.

76
Agent oriented methodologies weaknesses
  • Agent-oriented methodologies weaknesses can be
    considered in two different aspects
  • The lack of attraction for methodology user to
    use the agent-oriented paradigm
  • The lack of attraction for methodology user to
    use existing agent-oriented methodologies

77
The lack of attraction for methodology user to
use the agent-oriented paradigm
  • Lack of agent-oriented programming languages
  • Although programming languages are only part of
    the development story, industry is reticent to
    adopt a new paradigm at the conceptual level if
    it is impossible to implement these ideas in a
    currently acceptable, commercially viable
    programming language
  • Lack of explicit statement of agent-orientation
    advantages
  • The benefits of agent technology must be declared
    by introducing the cases where AOSE paradigm
    succeeds and other existing paradigms fail

78
Continue
  • Relative difficulty of learning concept related
    to agent oriented paradigm (AI)
  • As an example the usage of Gaia agent-oriented
    methodology requires learning logic, which
    decreases the adoption of this methodology, since
    usually methodology users are not familiar with
    logic and do not tend to learn it.
  • High cost of AO acquisition
  • The acquisition of this paradigm by software
    development organizations requires a high cost
    for training the development team.

79
The lack of attraction for methodology user to
useexisting agent-oriented methodologies
  • Relative immaturity
  • The AO paradigm immaturity, which is a relative
    matter compared to other paradigms, is clearly
    because of it newness.
  • Marketing of multiple AO methodologies
  • As long as the availability and marketing of
    multiple agent-oriented methodologies are in
    competitive manner, this feature is an obstacle
    to their widespread industrial adoption, since it
    leads to confusion of methodology users

80
Continue
  • Lack of confrontation with wrong expectation of
    one size- fits-all methodology
  • No unique specific methodology can be general
    enough to be useful to every project without some
    level of personalization.
  • Users usually think a unique methodology has
    general usage and ignore the fact that each
    methodology is designed for some specific goals .
    Thus when a specific methodology does not fit
    their requirements and leads to project failure
    they conceive the problem from the side of
    methodology whereas the problem is with the wrong
    methodology selection . Agent-oriented paradigm
    should support its user with the awareness and
    facilities to find the proper methodology for his
    project from existing methodologies or to change
    the existing instances in order to fit the
    project.

81
Continue
  • Lack of confrontation with user willingness to
    setup an owned project-specific methodology
  • The high number of existing AO methodologies can
    be seen as a proof that methodology users, often
    prefer to setup an owned methodology specially
    tailored for their needs instead of reusing
    existing ones. AO paradigm should support its
    user with the awareness and facilities to avoid
    setting up his methodology from the scratch, but
    to change the existing instances in order to fit
    the project.

82
Solutions of Technical issues on Multi Agent
Systems
83
Solutions of three key issues where the current
state-of-the-art is lacking
  • Working on developing techniques and tools that
    allow for designs and code to be strongly
    integrated with consistency checking and change
    propagation.
  • Developing better integrated designs and code
    would be facilitated by AOPLs being closer to the
    design in terms of covered concepts.
  • Develop better techniques and tools for debugging
    and verification.

84
Agent oriented methodologies Solutions
  • Agent-oriented methodologies solutions can be
    considered in two different aspects
  • Solution to agent-oriented paradigm
  • Solution to existing agent-oriented methodologies

85
Solution to agent-oriented paradigm
  • Software development organizations use an
    evaluation framework for agent-oriented
    methodologies.
  • This approach would
  • (i) help to improve existing methodologies by
    identifying their weaknesses,
  • (ii) make the availability of multiple
    methodologies an advantage (having wide range of
    method fragment options),
  • (iii) do away with the wrong expectation on
    one-size-fits-all methodology, and
  • (iv) answer to user willingness to setup an
    owned project-specific methodology.

86
Continue
  • In CMM organizational maturity framework 5
    maturity levels are distinguished
  • Initial
  • Repeatable
  • Defined
  • Managed
  • Optimizing

87
Solution to existing agent-oriented methodologies
  • Agent OPEN method
  • OPEN, which stands for Object-oriented Process,
    Environment and Notation.
  • OPEN Process Framework (OPF) consists of
  • (i) a process metamodel of framework from which
    can be generated an organizationally specific
    process
  • (ii) a repository and
  • (iii) a set of construction guidelines.
  • The major elements in OPF metamodel are Work
    Units (Activities, Tasks and Techniques), Work
    Products, Producers and two auxiliary ones
    (Stages and Languages)

88
Continue
  • Feature-based method
  • proposed a modular approach enabling developers
    to build customized project-specific
    methodologies from AOSE features.
  • Differing from Agent OPEN approach, this method
    does not regard it necessary to rely on the
    formal metamodel of method fragments.
  • This method identifies and standardizes the
    common elements of the existing methodologies.

89
Continue
  • The common elements could form a generic agent
    model on which specialized features might be
    based.
  • The remaining parts of the methodologies would
    represent added-value that the methodologies
    bring to the common elements, and should be
    componentized into modular features.
  • The small granularity of features allows them to
    be combined into the common models in a flexible
    manner. By conforming to the generic agent model
    in the common elements, it is expected that the
    semantics of the optional features remain
    consistent.

90
References
  • Zhikun ZHAO, Yinglei XU , "DPMAS A Design
    Method for Multi-agent System using Agent UML",
    2010 Third International Conference on
    Information and Computing
  • Bernhard Bauer, Jorg P. Muller, and James
    Odell, Agent UML A formalism for specifying
    multi agent software systems, In P. Ciancarini
    and M. Wooldridge, editors, Agent-Oriented
    Software Engineering Proceedings of the First
    International Workshop(AOSE-2000).
    Springer-Verlag Berlin, Germany, 2000.
  • Giovanni Caire et al, Agent Oriented Analysis
    using MESSAGE/UML, Second International
    Workshop, AOSE 2001,Montreal, Canada, May 29,
    2001.
  • Carole Bernon, Massimo Cossentino, Juan Pavón An
    Overview of Current Trends in European AOSE
    Research June 31, 2005
  • Wei Huang , Elia El-Darzi and Li JinSchool of
    Computer Science, University of Westminster
    Watford Road, London HA1 3TP, United Kingdom,
    Extending the Gaia Methodology forthe Design and
    Development of Agent-based Software Systems
  • Wooldridge, M., Jennings, N.R., and Kinny, D,
    The Gaia Methodology for Agent-Oriented Analysis
    and Design, Journal of Autonomous Agents and
    Multi-Agent Systems. 3,3 (2000), 285-312.
  • Zhongzhi Shi, Haijun Zhang, Yong Cheng, Yuncheng
    Jiang, QiujianSheng, Zhikung Zhao, MAGE an
    agent-oriented software engineering environment,
    Proceedings of the Third IEEE International
    Conference on Cognitive Informatics, 2004,
    pp250-257.

91
References
  • Rafael H. Bordini, Mehdi Dastani, and Michael
    Winikoff, Current Issues in Multi-Agent Systems
    Development(Invited Paper).
  • O. Zohreh Akbari, A survey of agent-oriented
    software engineering paradigm Towards its
    industrial acceptance, January 13, 2010.
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