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Re: [GP] Re: Agents and GA/GP

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  • steve uurtamo
    ... can you give an example? i m presuming that you don t mean constraints related to the motion or physical limitations of the aircraft. s.
    Message 1 of 5 , Jan 24, 2007
      > I found that by eliminating constraints required by
      > analytical solutions and simply letting evolution do its job, the GP
      > evolved a reactive control strategy that dramatically improved the
      > aircraft's chances:

      can you give an example? i'm presuming that you don't mean
      constraints related to the motion or physical limitations of the
      aircraft.

      s.






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    • Frank Moore
      Steve, you bring up an interesting issue. For many problems (for example, optimal control problems), if can be extremely difficult to derive a closed-form
      Message 2 of 5 , Jan 26, 2007
        Steve, you bring up an interesting issue. For many problems (for
        example, optimal control problems), if can be extremely difficult to
        derive a closed-form solution. The usual alternative is to make
        assumptions that simplify the model enough to allow an analytical
        solution to be developed. When I was studying the missile avoidance
        problem, most of the textbook solutions I saw made unrealistic
        assumptions (e.g., the missile and aircraft are traveling in a 2D
        space, the missile is traveling at constant speed, the missile's
        control laws are known to the aircraft, the missile's location is
        precisely available to the aircraft, etc.). In addition, they did not
        model uncertainty well at all, and could not incorporate the use of
        additional countermeasures.

        On the other hand, an evolutionary system that uses a high-fidelity
        simulator to evolve optimal strategies doesn't care about such
        restrictions. Of course, it still needs to account for other
        constraints, such as the physical model -- i.e., an aircraft that
        attempts to pull 35Gs should be given a pretty low fitness value! So,
        your assumption (below) is exactly correct.

        To elaborate slightly: the classic 2D analytical solution to maximize
        miss distance between an aircraft and a pursuing missile utilizing a
        specific set of guidance parameters might have the aircraft make three
        precisely timed turns. (See Zarchan 1992.) But the GP solution might
        (for example) incorporate accelerations with turns to increase miss
        distance. Further, the GP solution is easily extended to allow the
        aircraft to utilize chaff/flares, jamming, altitude advantages, etc. --
        something that cannot be said for the analytical solutions I've
        seen. The result I saw was an improvement in predicted survivability
        from something like 86% to 98%. I'm going to guess that there are a
        fairly large number of problems for which GPs -- using accurate
        simulations for fitness evaluation -- could evolve better control
        strategies than those used today. Hope this helps!

        ----- Original Message -----
        From: steve uurtamo <apoxonpoo@...>
        Date: Wednesday, January 24, 2007 1:05 pm
        Subject: Re: [GP] Re: Agents and GA/GP

        > > I found that by eliminating constraints required by
        > > analytical solutions and simply letting evolution do its job,
        > the GP
        > > evolved a reactive control strategy that dramatically improved
        > the
        > > aircraft's chances:
        >
        > can you give an example? i'm presuming that you don't mean
        > constraints related to the motion or physical limitations of the
        > aircraft.
        >
        > s.
        >
        >
        >
        >
        >
        >
        >
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        > Bored stiff? Loosen up...
        > Download and play hundreds of games for free on Yahoo! Games.
        > http://games.yahoo.com/games/front
        >


        [Non-text portions of this message have been removed]
      • I Jonyer
        ... Call for participation ICML-2007 Workshop on Challenges and Applications of Grammar Induction In conjunction with the International Conference on Machine
        Message 3 of 5 , Mar 30, 2007
          -----------------------------------------------------------------------
          Call for participation
          ICML-2007 Workshop on
          Challenges and Applications of Grammar Induction

          In conjunction with the International Conference on Machine Learning,
          Oregon State University, June 20 - June 24, 2007

          Sponsored by the Pascal Network
          -----------------------------------------------------------------------

          Description

          Grammar Induction (GI), also known as Grammatical Inference, is about
          learning grammars from data. A well-known important application of GI
          is natural language learning, but it is applicable in a much broader
          sense to the problem of learning structural models from data. The data
          typically consists of sequences of discrete events from various domains
          (such as text, DNA fragments, primary structure of proteins, sequential
          process log-files and musical scores), but can also include trees and
          arbitrary graphs (such as metabolic networks and social networks).
          Typical models include formal grammars (regular, context-free, context-
          sensitive, . . .), and statistical models in related formalisms such as
          probabilistic automata, hidden Markov models, probabilistic transducers
          or conditional random fields.

          The CAGI workshop aims at highlighting current challenges in grammar
          induction with a special focus on applicability issues including:
          - practical evaluations demonstrating the usefulness of the proposed
          techniques,
          - novel applications of grammar induction algorithms,
          - noise resistant approaches,
          - semi-supervised grammar learning,
          - learning from partial sequences or streams,
          - approximate induction and model optimization,
          - experimental assessments illustrating the current limit(s) of the GI
          field,
          - practical complexity and scalability issues (alphabet size, noise
          level, data sparseness, data inconsistency, . . .),
          - evaluation of similarity learning algorithms from structured data
          (pair-HMM learning, stochastic transducer learning, . . .).


          Workshop Format

          The workshop will include presentations of peer-reviewed papers. Each
          such paper will be assigned 30 minutes, including 10 minutes for
          discussion. Each half-day will start with an invited paper for 45
          minutes including the discussion. The day will be concluded with an
          open panel for discussing the key lessons learned and pointing at
          relevant research perspectives.


          Submission Information

          Prospective authors are invited to email their 8-page papers to
          cagi07@... by the due date in PDF format. Formatting
          instructions are given by the conference at
          http://oregonstate.edu/conferences/icml2007/icml_format_2007.zip.
          The workshop will not have a blind review process, and therefore
          author names, affiliations, and contact information should appear in
          the submission, including postal address, email address, telephone
          number, and fax number. Electronic versions of the final papers will
          be available on the workshop home page at www.cs.okstate.edu/cagi07/.

          Interested participants are also invited to submit 2-page position
          papers. These will also be peer-reviewed and appear in the workshop
          proceedings. If the workshop schedule allows, short presentations
          at the end of the day may be possible as well.

          Workshop home page: www.cs.okstate.edu/cagi07/
          Submit papers to: cagi07@...


          Important Dates

          Paper Submission May 7, 2007
          Acceptance Notification May 25, 2007
          Electronic Proceedings June 15, 2007
          Workshop date June 24, 2007


          Organizing Committee

          Istvan Jonyer, Oklahoma State University, USA
          Pierre Dupont, Universit� catholique de Louvain, Belgium
          Tim Oates, University of Maryland Baltimore County, USA
          Marc Sebban, Universit� de Saint-Etienne, France


          Program Committee

          Pierre Dupont (PC chair), Universit� catholique de Louvain, Belgium
          Pieter Adriaans, Universiteit van Amsterdam, The Netherlands
          Vasant Honavar, Iowa State University, USA
          Istvan Jonyer, Oklahoma State University, USA
          Laurent Miclet, Universit� de Rennes, France
          Tim Oates, University of Maryland Baltimore County, USA
          Rajesh Pareck, Iowa State University, USA
          Yasubumi Sakakibara, Keio University, Japan
          Marc Sebban, Universit� de Saint-Etienne, France
          Menno van Zannen, Macquarie University, Australia
          Enrique Vidal, Universidad Polit�cnica de Valencia, Spain

          _________________________________________________________________
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