Nate Kohl's Publications

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An Integrated Neuroevolutionary Approach to Reactive Control and High-level Strategy

An Integrated Neuroevolutionary Approach to Reactive Control and High-level Strategy. Nate Kohl and Risto Miikkulainen. IEEE Transactions on Evolutionary Computation, 2011. To appear

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Abstract

One promising approach to general-purpose artificial intelligence isneuroevolution, which has worked well on a number of problems fromresource optimization to robot control. However, state-of-the-artneuroevolution algorithms like NEAT have surprising difficulty onproblems that are fractured, i.e. where the desired actions changeabruptly and frequently. Previous work demonstrated that bias andconstraint (e.g. RBF-NEAT and Cascade-NEAT algorithms) can improvelearning significantly on such problems. However, experiments in thispaper show that relatively unrestricted algorithms (e.g. NEAT) stillyield the best performance on problems requiring reactive control.Ideally, a single algorithm would be able to perform well on bothfractured and unfractured problems. This paper introduces such analgorithm called SNAP-NEAT that uses adaptive operator selection tointegrate strengths of NEAT, RBF-NEAT, and Cascade-NEAT. SNAP-NEATis evaluated empirically on a set of problems ranging from reactivecontrol to high-level strategy. The results show that SNAP-NEAT canadapt intelligently to the type of problem that it faces, thus layingthe groundwork for learning algorithms that can be applied to a widevariety of problems.

BibTeX Entry

@Article{kohl:ieeetec11,
  author = "Nate Kohl and Risto Miikkulainen",
  title = "An Integrated Neuroevolutionary Approach to Reactive Control and High-level Strategy",
  journal = "IEEE Transactions on Evolutionary Computation",
  year = "2011",
  note = "To appear",
  abstract = {
    One promising approach to general-purpose artificial intelligence is
neuroevolution, which has worked well on a number of problems from
resource optimization to robot control.  However, state-of-the-art
neuroevolution algorithms like NEAT have surprising difficulty on
problems that are fractured, i.e. where the desired actions change
abruptly and frequently.  Previous work demonstrated that bias and
constraint (e.g. RBF-NEAT and Cascade-NEAT algorithms) can improve
learning significantly on such problems.  However, experiments in this
paper show that relatively unrestricted algorithms (e.g. NEAT) still
yield the best performance on problems requiring reactive control.
Ideally, a single algorithm would be able to perform well on both
fractured and unfractured problems.  This paper introduces such an
algorithm called SNAP-NEAT that uses adaptive operator selection to
integrate strengths of NEAT, RBF-NEAT, and Cascade-NEAT.  SNAP-NEAT
is evaluated empirically on a set of problems ranging from reactive
control to high-level strategy.  The results show that SNAP-NEAT can
adapt intelligently to the type of problem that it faces, thus laying
the groundwork for learning algorithms that can be applied to a wide
variety of problems.
},
  bib2html_pubtype = {Journal},
  bib2html_rescat = {Machine Learning}
}

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