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CMU-CS-99-175
Computer Science Department
School of Computer Science, Carnegie Mellon University
CMU-CS-99-175
Classifying Adversarial behaviors in a Dynamic
Inaccessible Multi-Agent Environment
Patrick Liley
November 1999
CMU-CS-99-175.ps
CMU-CS-99-175.pdf
Keywords: Artificial intelligence, expert systems, games, adversary adaptation and classification
This paper introduces the design and use of the Simple Language Generator
(SLG). SLG allows the user to construct small but interesting stochastic
context-free languages with relative ease. Although context-free grammars are
convenient for representing natural language syntax, they do not easily
support the semantic and pragmatic constraints that make certain combinations
of words or structures more likely than others. Context-free grammars for
languages involving many interacting constraints can become extremely complex
and cannot reasonably be written by hand. SLG allows the basic syntax of a
grammar to be specified in context-free form and constraints to be applied
atop this framework in a relatively natural fashion. This combination of
grammar and constraints is then converted into a standard stochastic
context-free grammar for use in generating sentences or in making context
dependent likelihood predictions of the sequence of words in a sentence.
16 pages
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