Jandson S. RibeiroFernUniversität in Hagen
Jandson S. Ribeiro
Doctor of Philosophy
About
13
Publications
205
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25
Citations
Citations since 2017
Introduction
I am a Postdoctoral Researcher at University Koblenz-Landau (Germany), since 2020. I have been addressing the problem of Belief Change in non-classical logics. This includes a number of logics such as temporal logics which are very important to both Artificial Intelligence (AI) and Formal Methods. My research revolves around Knowledge Representation, Logics, Non-Monotonic Reasoning and Formal Methods.
Additional affiliations
February 2014 - January 2016
Publications
Publications (13)
The postulate of relevance provides a suitable and general notion of minimal change for belief contraction. Relevance is tightly connected to smooth kernel contractions when an agent's epistemic state is represented as a logically closed set of formulae. This connection, however, breaks down when an agent's epistemic state is represented as a set o...
Most approaches for repairing description logic (DL) ontologies aim at changing the axioms as little as possible while solving inconsistencies, incoherences and other types of undesired behaviours. As in Belief Change, these issues are often specified using logical formulae. Instead, in the new setting for updating DL ontologies that we propose her...
Restoring consistency of a knowledge base, known as consolidation, should preserve as much information as possible of the original knowledge base. On the one hand, the field of belief change captures this principle of minimal change via rationality postulates. On the other hand, within the field of inconsistency measurement, culpability measures ha...
Dealing with dynamics is a vital problem in Artificial Intelligence (AI). An intelligent system should be able to perceive and interact with its environment to perform its tasks satisfactorily. To do so, it must sense external actions that might interfere with its tasks, demanding the agent to self-adapt to the environment dynamics. In AI, the fiel...
We consider the problem of quantitatively assessing the conflict between knowledge bases in knowledge merging scenarios. Using the notion of Craig interpolation we define a series of disagreement measures and analyse their compliance with properties proposed in previous work by Potyka. We study basic complexity theoretic questions in that scenario...
The main paradigms of belief change require the background logic to be Tarskian and finitary. We look at belief update when the underlying logic is not necessarily finitary. We show that in this case the classical construction for KM update does not capture all the rationality postulates for KM belief update. Indeed, this construction, being fully...
Belief change and non-monotonic reasoning are arguably different perspectives on the same phenomenon, namely, jettisoning of currently held beliefs in response to some incompatible evidence. Investigations in this area typically assume, among other things, that the underlying (background) logic is compact, that is, whatever can be inferred from a s...
Belief change and non-monotonic reasoning are arguably different perspectives on the same phenomenon, namely, jettisoning of currently held beliefs in response to some incompatible evidence. Investigations in this area typically assume, among other things, that the underlying (background) logic is compact, that is, whatever can be inferred from a s...
Poster presented on the 33rd AAAI conference.
Poster presented on the KR-18 Doctoral Consortium.
In the AGM paradigm of belief change the background logic is taken to be a supra-classical logic satisfying compactness among other properties. Compactness requires that any conclusion drawn from a set of propositions X is implied by a finite subset of X.There are a number of interesting logics such as Computational Tree Logic (CTL, a temporal logi...
In this work we address the problem of model checking a desired property specified in Computation Tree Logic (CTL) in the presence of partial information. The KripkeModal Transition System (KMTS) is used for modelling due its capacity to represent indefinitions explicitly which enables a KMTS interpretation as a set of Kripke structures. In this in...
Projects
Project (1)
Dealing with noisy and inconsistent information adequately is one of the core challenges in knowledge-driven AI applications. In scenarios where experts share their knowledge in order to build a joint knowledge base or sensor information is to be added, inconsistencies easily occur. In the field of Knowledge Representation and Reasoning (KRR), the formal framework for addressing such problems is belief merging (and its related areas such as belief change and information fusion), which provides computational approaches for automatically resolving these issues in some sensible way. The field of belief merging bears a close relationship with the fields of judgement and preference aggregation and also features its own version of Arrow's impossibility result, insofar that there cannot be any "rational" belief merging approach. This calls for semi-automatic methods that take human background knowledge into account when knowledge has to be merged, in order not to remove important pieces of information. However, classical belief merging approaches usually work in a way that is hard to interpret by users, choosing the pieces of information to be removed based on, e.g., notions of distances of interpretations.
In this project, we address the above challenge of "explainable belief merging" by developing new belief merging operators that are able to explain their results and allow for the semi-automatic repair of knowledge-driven systems. Our method for this endeavour will be based on the computation and analysis of "Craig Interpolants". Informally, given two knowledge bases, an interpolant is a formula which can be derived from one of the knowledge bases, such that its insertion into the other will lead to an inconsistency. Therefore, an interpolant provides a concise explanation of why a particular conflict between two knowledge bases occurs. We believe that using the information obtained from the analysis of interpolants will allow us to extend existing approaches to belief merging with better explanation capabilities - and even develop new formal approaches to belief merging. In fact, preliminary work by the grant applicants already showed that interpolants can be used to measure the conflicts between multiple knowledge bases in a sensible fashion. A further aspect we wish to explore in this project is the application of the belief merging framework to more expressive logics. Knowledge-driven scenarios usually require functionalities as arithmetics and first-order reasoning, but the bulk of the work on belief merging is concerned with the setting of propositional logic. We will therefore also lay the foundations for using belief merging in more expressive logics and investigate the use of interpolants therein.
