In this work we address the problem of elaborating domain descriptions (alias action theories), in particular those that are expressed in dynamic logic. We define a general method based on contraction of formulas in a version of propositional dynamic logic with an incorporated solution to the frame problem. We present the semantics of our theory change and define syntactical operators for contracting a domain description. We establish

Abstract. We study resolving conflicts between an action description and a set of conditions (possibly obtained from observations), in the context of action languages. In this formal framework, the meaning of an action description can be represented by a transition diagram—a directed graph whose nodes correspond to states and whose edges correspond to transitions describing action occurrences. This allows us to characterize conflicts by means of states and transitions of the given action description that violate some given conditions. We introduce a basic method to resolve such conflicts by modifying the action description, and discuss how the user can be supported in obtaining more preferred solutions. For that, we identify helpful questions the user may ask (e.g., which specific parts of the action description cause a conflict with some given condition), and we provide answers to them using properties of action descriptions and transition diagrams. Finally, we discuss the computational complexity of these questions in terms of related decision problems. 1

Abstract. Incorporating new information into a knowledge base is an important problem which has been widely considered. In this paper, we study the problem in a formal framework for reasoning about action and change, in which action domains are described in an action language that has a transition-based semantics. Going beyond previous works, we consider (i) a richer action language that allows for non-deterministic, and concurrent actions, as well as the representation of indirect effects and dependencies between fluents, (ii) more general updates than elementary statements, and, most importantly, (iii) meta-level knowledge, such as observations, assertions, or general domain properties that remain invariant under change, expressed in an action query language. For this setting, we formalize a notion of update of an action domain description, relative to a generic preference relation on action domain descriptions that selects most preferred solutions. We study semantic and computational aspects of this notion, where we establish basic properties of updates and a decomposition result that gives rise to a divide and conquer approach to computing solutions under certain conditions. Furthermore, we study the computational complexity of decision problems around computing solutions, both for the generic setting and for two particular preference relations, viz. set-inclusion and weight-based preference. While deciding the existence of solutions

Classical logic Event Calculus, and the special purpose logical action language E, are both well established formalisms for describing actions and change. However, there is yet to be an account of ramifications in Event Calculus sufficiently general to represent the classes of domains expressible in E. Indeed, an adequately general ramification theory constructed in any general purpose logical language still awaits. Therefore, under the motivation of creating a flexible ramification theory in a universal language, suitable for integration into a rich action theory, a new enhanced version of classical logic Event Calculus named EC-R is proposed. EC-R supports representation and reasoning about domains containing ramifications for classes of domains more general than those possible under previous general purpose language formulations. This article makes two main contributions. The first, EC-R, is a narrative-based action formalism able to represent concurrent events, non-deterministic actions and indirect causal effects by virtue of an integrated solution to the frame and ramification problems. The formalism can reason about significant subclasses of domains containing both mutually interacting effects and cyclic causal dependencies. The formalism is elaboration tolerant and may be integrated with the standard variants of the Event Calculus. The second contribution is the definition of a semantic mapping between EC-R and E, and a proof of soundness and completeness of the EC-R theory with respect toE’s model theoretic specification.

Knowledge about domain dynamics, describing how certain actions affect the world, is called an action model, and constitutes the essential requirement for planning and goal-oriented intelligent behaviour. Action learning, as the automatic construction of action models, has become a hot research topic in recent years, and various methods employing wide variety of AI tools have been developed. Diversity of these methods naturally renders each of them usable under different conditions and in various kinds of domains. After the extensive analysis of related work, we have declared our goal to introduce a collection of tractable and online methods for probabilistic action learning in noisy and partially observable domains supporting the induction of action’s preconditions and complex conditional effects. Subsequently, we have proposed the first solution candidate, which embeds our compact representation structure called effect formula (EF) and a polynomial algorithm 3SG (Simultaneous Specification, Simplification, and Generalization),

Abstract — We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The framework features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper. I.

Like any other logical theory, domain descriptions in reasoning about actions may evolve, and thus need revision methods to adequately accommodate new information about the behavior of actions. The present work is about changing action domain descriptions in propositional dynamic logic. Its contribution is threefold: first we revisit the semantics of action theory contraction that has been done in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness w.r.t. our semantics. Finally we state postulates for action theory contraction and assess the behavior of our operators w.r.t. them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for