A Causal Model for Fluctuating Sugar Levels in Diabetes Patients

Background Causal models of physiological systems can be immensely useful in medicine as they may be used for both diagnostic and therapeutic reasoning. Aims In this paper we investigate how an agent may use the theory of belief change to rectify simple causal models of changing blood sugar levels in diabetes patients. Method We employ the semantic approach to belief change together with a popular measure of distance called Dalal distance between different state descriptions in order to implement a simple application that simulates the effectiveness of the proposed method in helping an agent rectify a simple causal model. Results Our simulation results show that distance-based belief change can help in improving the agent’s causal knowledge. However, under the current implementation there is no guarantee that the agent will learn the complete model and the agent may at times get stuck in local optima. Conclusion Distance-based belief change can help in refining simple causal models such as the example in this paper. Future work will include larger state-action spaces, better distance measures and strategies for choosing actions

Propositional update operators based on formula/literal dependence

International audienceWe present and study a general family of belief update operators in a propositional setting. Its operators are based on formula/literal dependence, which is more fine-grained than the notion of formula/variable dependence that was proposed in the literature: formula/variable dependence is a particular case of formula/literal dependence. Our update operators are defined according to the "forget-then-conjoin" scheme: updating a belief base by an input formula consists in first forgetting in the base every literal on which the input formula has a negative influence, and then conjoining the resulting base with the input formula. The operators of our family differ by the underlying notion of formula/literal dependence, which may be defined syntactically or semantically, and which may or may not exploit further information like known persistent literals and pre-set dependencies. We argue that this allows to handle the frame problem and the ramification problem in a more appropriate way. We evaluate the update operators of our family w.r.t. two important dimensions: the logical dimension, by checking the status of the Katsuno-Mendelzon postulates for update, and the computational dimension, by identifying the complexity of a number of decision problems (including model checking, consistency and inference), both in the general case and in some restricted cases, as well as by studying compactability issues. It follows that several operators of our family are interesting alternatives to previous belief update operators

Belief revision and computational argumentation: a critical comparison

This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reason ing they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and on the other hand pointing out open issues and potential directions for future research.info:eu-repo/semantics/publishedVersio