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SPLASH 2017
Sun 22 - Fri 27 October 2017 Vancouver, Canada
Sun 22 Oct 2017 11:15 - 11:37 at Regency A - Session 2 Chair(s): Nada Amin

Writing software that employs artificial intelligence (AI) is complex because the algorithms that must be implemented in general purpose programming languages are complex. One solution to this problem is to embed AI algorithms in domain specific languages (DSLs). DSLs are the ``ultimate abstraction'' for creating programs for a particular domain, but the question of how or even why to do this is not easily answered. We have developed a language with integrated reinforcement learning designed for writing intelligent agents. AFABL (A Friendly Adaptive Behavior Language), is implemented as an internal DSL shallowly embedded in the Scala programming language. We discuss the development of AFABL, the basic elements of AFABL, the way AFABL captures domain knowledge, the benefits of integrating reinforcement learning into a programming language and report the results of a programmer study which confirms and quantifies the usefulness of integrating reinforcement learning into a programming language.

Sun 22 Oct

dsldi-2017
10:30 - 12:00: DSLDI 2017 - Session 2 at Regency A
Chair(s): Nada AminUniversity of Cambridge
dsldi-2017150866100000010:30 - 10:52
Talk
Wode NiColumbia University, Katherine Ye, Joshua SunshineCarnegie Mellon University, Jonathan AldrichCarnegie Mellon University, Keenan CraneCarnegie Mellon University
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dsldi-2017150866235000010:52 - 11:15
Talk
Xiangqi LiUniversity of Utah, Matthew FlattUniversity of Utah
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dsldi-2017150866370000011:15 - 11:37
Talk
Christopher SimpkinsGeorgia Institute of Technology, Spencer RugaberGeorgia Institute of Technology, Charles Isbell, Jr.Georgia Institute of Technology
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dsldi-2017150866505000011:37 - 12:00
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Bart van MerriƫnboerUniversity of Montreal, Alexander B. WiltschkoGoogle Brain
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