Reasoning about change is a central issue in research on human and robot planning. We study an approach to reasoning about action and change in a dynamic logic setting and provide a solution to problems which are related to the Frame problem. Unlike most work on the frame problem the logic described in this paper is monotonic. It (implicitly) allows for the occurrence of actions of multiple agents by introducing non-stationary notions of waiting and test. The need to state a (...) large number of frame axioms is alleviated by introducing a concept of chronological preservation to dynamic logic. As a side effect, this concept permits the encoding of temporal properties in a natural way. We compare the relative merits of our approach and non-monotonic approaches as regards different aspects of the frame problem. Technically, we show that the resulting extended systems of propositional dynamic logic preserve (weak) completeness, finite model property and decidability. (shrink)

ABSTRACT One major problem in the process of knowledge assimilation is how to deal with inconsistency of new knowledge and the existing knowledge base. In this paper we present a formal, provably correct and yet computational methodology for assimilation of new knowledge into knowledge bases about actions and changes based on the slogan: what is believed is what is explained. Technically, we employ Gelfond and Lifschitz' action description language A to describe domains of actions. The knowledge bases on domains of (...) actions are defined and obtained by a new translation from domain descriptions in A into abductive normal logic programs, where a time dimension is incorporated. The knowledge bases are shown to be both sound and complete with respect to their domain descriptions. In particular, we propose a possible causes approach (PCA) to knowledge assimilation about evolving domains of actions, in contrast to Ginsberg's possible worlds approach (PWA) and Winslett's possible models approach (PMA). A possible cause of new knowledge consists of abduced occurrences of actions and value propositions about the initial state of the domain of actions, that would allow to derive the new knowledge. We show how to compute possible causes with abductive logic programming, and present some techniques to improve search efficiency. We use examples to compare our possible causes approach with syntax-based approaches, such as Ginsberg's possible worlds approach, and semantics-based approaches, such as Winslett's possible models approach, to belief revision/update. (shrink)

There are various contexts in which it is not pertinent to generate and attend to all the classical consequences of a given premiss—or to trace all the premisses which classically entail a given consequence. Such contexts may involve limited resources of an agent or inferential engine, contextual relevance or irrelevance of certain consequences or premisses, modelling everyday human reasoning, the search for plausible abduced hypotheses or potential causes, etc. In this paper we propose and explicate one formal framework for a (...) whole spectrum of consequence relations, flexible enough to be tailored for choices from a variety of contexts. We do so by investigating semantic constraints on classical entailment which give rise to a family of infra-classical logics with appealing properties. More specifically, our infra-classical reasoning demands (beyond ${\alpha\models\beta}$ ) that Mod(β) does not run wild, but lies within the scope (whatever that may mean in some specific context) of Mod(α), and which can be described by a sentence ${\bullet\alpha}$ with ${\beta\models\bullet\alpha}$ . Besides being infra-classical, the resulting logic is also non-monotonic and allows for non-trivial reasoning in the presence of inconsistencies. (shrink)

A fundamental question in reasoning about change is, what information does a reasoning agent infer about later times from earlier times? I will argue that reasoning about change by an agent is to be modeled in terms of the persistence of the agent''s beliefs over time rather than the persistence of truth and that such persistence is explained by pragmatic factors about how agents acquire information from other agents rather than by general principles of persistence about states of the world. (...) AI accounts of persistence have focused on closed world examples of change, in which the agent believes that the truth of a proposition is unaltered so long as he or she has no evidence that it has been changed. AI principles of persistence seem plausible in a closed world where one assumes the agent knows everything that is happening. If one drops the assumption of omniscience, however, the analysis of persistence is implausible. To get a good account of persistence and reasoning about change, I argue we should examine open world examples of change, in which the agent is ignorant of some of the changes occurring in the world. In open world examples of change, persistence must be formulated, I argue, as a pragmatic principle about the persistence of beliefs. After elaborating my criticisms of current accounts of persistence, I examine how such pragmatic principles fare with the notorious examples of reasoning about action that have collectively characterized the so-called frame problem. (shrink)