This dissertation is about the design of a modular language for describing actions. The modular action description language, MAD, is based on the action language C+. In this new language, the possibility of "importing" a module allows us to describe actions by referring to descriptions of related actions introduced earlier, ather than by listing all effects and preconditions of every action explicitly. The use of modular action descriptions eliminates the need to reinvent theories of similar domains over and over again. Another advantage of this representation style is that it is similar to the way humans describe actions in terms of other actions. We first define the syntax of a fragment of MAD, called mini-MAD, and then extend it to the full version of MAD. The semantics of mini-MAD is defined by grounding action descriptions and translating them into C+. However, for the full version of MAD, it would be difficult to define grounding. Instead, we use a new approach to the semantics of variables in action descriptions, which is based on more complex logical machinery--first-order causal logic. Grounding is important as an implementation method, but we argue that it should be best avoided in the definition of the semantics of expressive action languages. We show that, in application to mini-MAD, the two semantics are equivalent. Furthermore, we prove that MAD action descriptions have some desirable, intuitively expected mathematical properties. We hope that MAD will make it possible to create a useful general-purpose library of standard action descriptions and will contribute in this way to solving the problem of generality in Artificial Intelligence.

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PhD Thesis, University of Texas at Austin.

@PHDTHESIS{ren09, title={A Modular Language for Describing Actions}, author={Wanwan Ren}, school={University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?ren09", year={2009} }