The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of BC on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning.

The action description languages B and C have significant common core. Nevertheless, some expressive possibilities of B are difficult or impossible to simulate in C, and the other way around. The main advantage of B is that it allows the user to give Prolog-style recursive definitions, which is important in applications. On the otherhand, B solves the frame problem by incorporating the commonsense law of inertia in its semantics, which makes it difficult to talk about fluents whose behavior is described by defaults other than inertia. In C and in its extension C+, the inertia assumption is expressed by axioms that the user is free to include or not to include, and other defaults can be postulated as well. This paper defines a new action description language, called BC, that combines the attractive features of B and C. Examples of formalizing commonsense domains discussed in the paper illustrate the expressive capabilities of BC and the use of answer set solvers for the automation of reasoning about actions described inthis language.