Model ai assignments 2016

Todd W. Neller; Laura E. Brown; James B. Marshall; Lisa Torrey; Nate Derbinsky; Andrew A. Ward; Thomas E. Allen; Judy Goldsmith; Nahom Muluneh

Model ai assignments 2016

Todd W. Neller, Laura E. Brown, James B. Marshall, Lisa Torrey, Nate Derbinsky, Andrew A. Ward, Thomas E. Allen, Judy Goldsmith, Nahom Muluneh

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

Original language	English
Title of host publication	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Pages	4139-4140
Number of pages	2
ISBN (Electronic)	9781577357605
State	Published - 2016
Event	30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016

Publication series

Name	30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/Territory	United States
City	Phoenix
Period	2/12/16 → 2/17/16

Bibliographical note

Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence

Cite this

@inproceedings{4f9c977a4a34441295ceccf20b4249d4,

title = "Model ai assignments 2016",

abstract = "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 experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.",

author = "Neller, {Todd W.} and Brown, {Laura E.} and Marshall, {James B.} and Lisa Torrey and Nate Derbinsky and Ward, {Andrew A.} and Allen, {Thomas E.} and Judy Goldsmith and Nahom Muluneh",

note = "Publisher Copyright: {\textcopyright} Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 17-02-2016",

year = "2016",

language = "English",

series = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

pages = "4139--4140",

booktitle = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

}

Neller, TW, Brown, LE, Marshall, JB, Torrey, L, Derbinsky, N, Ward, AA, Allen, TE, Goldsmith, J & Muluneh, N 2016, Model ai assignments 2016. in 30th AAAI Conference on Artificial Intelligence, AAAI 2016. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 4139-4140, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 2/12/16.

TY - GEN

T1 - Model ai assignments 2016

AU - Neller, Todd W.

AU - Brown, Laura E.

AU - Marshall, James B.

AU - Torrey, Lisa

AU - Derbinsky, Nate

AU - Ward, Andrew A.

AU - Allen, Thomas E.

AU - Goldsmith, Judy

AU - Muluneh, Nahom

PY - 2016

Y1 - 2016

N2 - 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 experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

AB - 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 experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

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UR - http://www.scopus.com/inward/citedby.url?scp=85007180046&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85007180046

T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

SP - 4139

EP - 4140

BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

Y2 - 12 February 2016 through 17 February 2016

ER -

Model ai assignments 2016

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Other files and links

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Cite this