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Todd Neller

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
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5

AAAI Conference 2018 Conference Paper

Model AI Assignments 2018

  • Todd Neller
  • Zack Butler
  • Nate Derbinsky
  • Heidi Furey
  • Fred Martin
  • Michael Guerzhoy
  • Ariel Anders
  • Joshua Eckroth

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning ex- perience, we here present abstracts of seven AI assign- ments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instruc- tor needs.

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AAAI Conference 2016 Conference Paper

A Survey of Current Practice and Teaching of AI

  • Michael Wollowski
  • Robert Selkowitz
  • Laura Brown
  • Ashok Goel
  • George Luger
  • Jim Marshall
  • Andrew Neel
  • Todd Neller

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering information about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

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AAAI Conference 2016 Conference Paper

Learning and Using Hand Abstraction Values for Parameterized Poker Squares

  • Todd Neller
  • Colin Messinger
  • Zuozhi Yang

We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. After introducing the game and research competition challenge, we describe our static board evaluation utilizing learned evaluations of abstract partial Poker hands. Next, we evaluate various time management strategies and search algorithms. Finally, we show experimentally which of our design decisions most significantly accounted for observed performance.

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