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Choosing Tool For Experimental Paper

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  • Noha Greiss
    Hello All, I have to choose from the following tools to do an Experimental Paper as part of my Advanced AI Course. Can anyone please help me choosing based on
    Message 1 of 2 , Mar 26, 2004
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      Hello All,

      I have to choose from the following tools to do an Experimental
      Paper as part of my Advanced AI Course.

      Can anyone please help me choosing based on which is the most used
      and currenlty recent technology as I am new in this field.

      1. Hugins (Distributed Belief Networks)
      2. Grass Hopper (Mobile Agents)
      3. ILOG (Planner)
      4. CBR
      5. KQML - XML (Agent Communication Language)
      6. Matlab Toolboxes (Neural Networks, Image Processing ...)
      7. NETICA - Norsys (Uncertainty Reasoning)

      I found some of the commerical sites related to these tools but not
      all of them, if you may know the related site for 3,4 or 5 Please
      send them to me.

      I know that they may tackle different aspects in AI, but the Idea is
      that I want the most used and recent topic.. ALso I would appreciate
      if you can provide me with some related experimental papers or links
      to papers or to good search engines in the related fields.

      Thanks a lot and really appreciate your help.
      Noha.
    • E etech058
      Hello Noha, 5. KQML - XML (Agent Communication Language) Because recently, I am researching for VRP problem and decide to use the multi-agent to solve the
      Message 2 of 2 , Apr 20 11:05 PM
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        Hello Noha,
        5. KQML - XML (Agent Communication Language)
        Because recently, I am researching for VRP problem and decide to use the multi-agent to solve the problem. Therefore I have found related documents about ACL and KQML.

        1. For ACL, it is defined by FIPA organization. (foundation for intelligent physical agents).
        1.1 FIPA website: www.fipa.org
        1.2 FIPA's documents, such as "SC0061G,SC00037J", are the specification of ACL.
        1.3 ACL is standard communication language between many agents.

        2. For KQML, it is one implementation of ACL by XML method. I just know this.

        3. In addition, FIPA-ACL is another implementation of ACL.

        4. If you want to try to use multi-agent, you can try to use "jade3.1" software, written by java, free software.

        5. Because the modern business requires flexible production, flexible management. Under this background, new flexible, high-extensibility system is required. Multi-agent can satisfy this requirement. This is important reason of multi-agent development.

        I hope the above information is useful to you.

        Thank you for your attention.

        Kind regards
        Chenyu (shanghai, china)

        -----Original Message-----
        From: Noha Greiss [mailto:ngreiss@...]
        Sent: 2004年3月26日 16:39
        To: aima-talk@yahoogroups.com
        Subject: [aima-talk] Choosing Tool For Experimental Paper

        Hello All,

        I have to choose from the following tools to do an Experimental
        Paper as part of my Advanced AI Course.

        Can anyone please help me choosing based on which is the most used
        and currenlty recent technology as I am new in this field.

        1. Hugins (Distributed Belief Networks)
        2. Grass Hopper (Mobile Agents)
        3. ILOG (Planner)
        4. CBR
        5. KQML - XML (Agent Communication Language)
        6. Matlab Toolboxes (Neural Networks, Image Processing ...)
        7. NETICA - Norsys (Uncertainty Reasoning)

        I found some of the commerical sites related to these tools but not
        all of them, if you may know the related site for 3,4 or 5 Please
        send them to me.

        I know that they may tackle different aspects in AI, but the Idea is
        that I want the most used and recent topic.. ALso I would appreciate
        if you can provide me with some related experimental papers or links
        to papers or to good search engines in the related fields.

        Thanks a lot and really appreciate your help.
        Noha.




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