Reasoning about actions in a probabilistic setting

Nonmonotonic Reasoning, Action, and Change (NRAC’03), 2003

Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented (explicitly) and their impact is reasoned in most other papers in the literature. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reason about actions.

Artificial Intelligence, 2020

We propose the action language EPEC-Epistemic Probabilistic Event Calculus-that supports probabilistic, epistemic reasoning about narratives of action occurrences and environmentally triggered events, and in particular facilitates reasoning about future beliefconditioned actions and their consequences in domains that include both perfect and imperfect sensing actions. To provide a declarative semantics for sensing and belief conditioned actions in a probabilistic, narrative setting we introduce the novel concept of an epistemic reduct. We then formally compare our language with two established frameworks for probabilistic reasoning about action-the action language PAL by Baral et al., and the extension of the situation calculus to reason about noisy sensors and effectors by Bacchus et al. In both cases we prove a correspondence with EPEC for a class of domains representable in both frameworks.

2004

Abstract Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented explicitly in action languages. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reasoning about actions.

1995

To perform nontrivial reasoning an intelligent agent situated in a changing domain needs the knowledge of causal laws that describe effects of actions changing the domain, the ability to observe and record occurrences of these actions, and the truth values of fluents1 at particular moments of time. Discovery of methods of representing this kind of information in a form allowing various types of reasoning about the dynamic world and at the same time tolerant to future updates is one of the central problems of knowledge representation.

Theory and Practice of Logic Programming, 2009

We examine the relation between constructive processes and the concept of causality. We observe that causality has an inherent dynamic aspect, i.e., that, in essence, causal information concerns the evolution of a domain over time. Motivated by this observation, we construct a new representation language for causal knowledge, whose semantics is defined explicitly in terms of constructive processes. This is done in a probabilistic context, where the basic steps that make up the process are allowed to have non-deterministic effects. We then show that a theory in this language defines a unique probability distribution over the possible outcomes of such a process. This result offers an appealing explanation for the usefulness of causal information and links our explicitly dynamic approach to more static causal probabilistic modeling languages, such as Bayesian networks. We also show that this language, which we have constructed to be a natural formalization of a certain kind of causal statements, is closely related to logic programming. This result demonstrates that, under an appropriate formal semantics, a rule of a normal, a disjunctive or a certain kind of probabilistic logic program can be interpreted as a description of a causal event. * Research supported by GOA 2003/8 Inductive Knowledge Bases and by FWO Vlaanderen. † Joost Vennekens is a postdoctoral researcher of the FWO.