Representing actions in logic-based languages

Knowledge about actions is an important part of commonsense knowledge studied in Artificial Intelligence. For decades, researchers have been developing methods for describing how actions affect states of the world and for automating reasoning about actions. In recent years, significant progress has been made. In particular, the frame problem has been solved using nonmonotonic knowledge representation formalisms, such as logic programming under the answer set semantics. New theories of causality have allowed us to express causal dependencies between fluents, which has proved essential for solving the ramification problem. It has been shown that reasoning about actions described by logic programs and causal theories can be automated using answer set programming. Action description languages are high level languages that allow us to represent knowledge about actions more concisely than when logic programs are used. Many action description languages have been described in the literature, including B, C, and C+. Reasoning about dynamic domains described in languages C and C+ can be performed automatically using the Causal Calculator (CCalc), which employs SAT solvers for search, and the systems coala and cplus2asp, which employ answer set solvers such as clingo. The dissertation addresses problems of three kinds. First, we study some mathematical properties of expressive action languages based on nonmonotonic causal logic that were not well understood until now. This includes causal rules expressing synonymy, nondefinite causal rules, and nonpropositional causal rules. We generalize existing translations from nonmonotonic causal theories to logic programming under the answer set semantics. This makes it possible to automate reasoning with a wider class of causal theories by calling answer set solvers. Second, we design and study a new action language BC, which is more expressive in some ways than the existing and previously proposed languages. We develop a framework that combines the most useful expressive features of the languages B and C+, and use program completion to characterize the effects of actions described in these languages. Third, we illustrate the possibilities of the new action language by two practical applications: to the dynamic domain of the Reactive Control System of the space shuttle, and to the task planning of mobile robots.