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, rather 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. / text

abstract: Modeling dynamic systems is an interesting problem in Knowledge Representation (KR) due to their usefulness in reasoning about real-world environments. In order to effectively do this, a number of different formalisms have been considered ranging from low-level languages, such as Answer Set Programming (ASP), to high-level action languages, such as C+ and BC. These languages show a lot of promise over many traditional approaches as they allow a developer to automate many tasks which require reasoning within dynamic environments in a succinct and elaboration tolerant manner. However, despite their strengths, they are still insufficient for modeling many systems, especially those of non-trivial scale or that require the ability to cope with exceptions which occur during execution, such as unexpected events or unintended consequences to actions which have been performed. In order to address these challenges, a theoretical framework is created which focuses on improving the feasibility of applying KR techniques to such problems. The framework is centered on the action language BC+, which integrates many of the strengths of existing KR formalisms, and provides the ability to perform efficient reasoning in an incremental fashion while handling exceptions which occur during execution. The result is a developer friendly formalism suitable for performing reasoning in an online environment. Finally, the newly enhanced Cplus2ASP 2 is introduced, which provides a number of improvements over the original version. These improvements include implementing BC+ among several additional languages, providing enhanced developer support, and exhibiting a significant performance increase over its predecessors and similar systems. / Dissertation/Thesis / M.S. Computer Science 2014

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abstract: Reasoning about actions forms the basis of many tasks such as prediction, planning, and diagnosis in a dynamic domain. Within the reasoning about actions community, a broad class of languages, called action languages, has been developed together with a methodology for their use in representing and reasoning about dynamic domains. With a few notable exceptions, the focus of these efforts has largely centered around single-agent systems. Agents rarely operate in a vacuum however, and almost in parallel, substantial work has been done within the dynamic epistemic logic community towards understanding how the actions of an agent may effect not just his own knowledge and/or beliefs, but those of his fellow agents as well. What is less understood by both communities is how to represent and reason about both the direct and indirect effects of both ontic and epistemic actions within a multi-agent setting. This dissertation presents ongoing research towards a framework for representing and reasoning about dynamic multi-agent domains involving both classes of actions. The contributions of this work are as follows: the formulation of a precise mathematical model of a dynamic multi-agent domain based on the notion of a transition diagram; the development of the multi-agent action languages mA+ and mAL based upon this model, as well as preliminary investigations of their properties and implementations via logic programming under the answer set semantics; precise formulations of the temporal projection, and planning problems within a multi-agent context; and an investigation of the application of the proposed approach to the representation of, and reasoning about, scenarios involving the modalities of knowledge and belief. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2018