In order to avoid ambiguity and be efficient, the context in which a query is made can help to better target the relevant pieces of information from the knowledge base to be processed by the inference system. In this paper, we are interested in the notion of dynamical compartmentalization where the knowledge base that will be used for reasoning is dynamically extracted from the original base. Compartmentalization is a selection of a sub-base which is done according to a function, called refiner, and depending on this function some properties are satisfied. We introduce a particular syntactic refiner that uses a similarity symbol-based distance between a context (a multiset of variable symbols) and a formula of a knowledge base. We prove that the inference operator based on this refiner, called contextual inference, satisfies a series of desirable axioms

This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features.

In this paper we consider a bipolar graph structure encoding conflicting knowledge with logic formulas.Gradual semantics provide a way to assign strength values in the unit interval to nodes (i.e.logical inference steps) in the bipolar graph.Here, we introduce a new class of semantics based on the notions of T-norms and T-conorms and show that they handle circular reasoning and satisfy desirable properties such as provability and rewriting.

International audience

In this demonstration we focus on the task of a data engineer choosing what tool to perform defeasible reasoning with a first order logic knowledge base.

To address the needs of the EU NoAW project, in this paper we introduce a new modular framework that generates viewpoints (i.e. extensions) based on ranking argumentation semantics by considering a selection function, a ranking on arguments and a lifting function as its input parameters. We study the different combinations of the input parameters and introduce a set of postulates investigated for the framework's different classes of output.

In this paper, we place ourselves in the Ontology Based Data Access (OBDA) setting and investigate reasoning with inconsistent existential rules knowledge bases. We use the notion of inconsistency measures on sets of facts to rank and filter repairs. We propose a generic framework to answer queries by using the best repairs and study productivity and properties of such a framework.

In this paper we approach both procedural and substantive irrationality of artificial agent cognitive systems and consider that when it is not possible for an agent to make a logical inference (too expensive cognitive effort or not enough knowledge) she might replace certain parts of the logical reasoning with mere associations.

This paper defines a new framework for dynamics in argumentation. In this framework, an agent can change an argumentation system (the target system) in order to achieve some desired goal. Changes consist in addition/removal of arguments or attacks between arguments and are constrained by theagent's knowledge encoded by another argumentation system. We present a software that computes the possible change operations for a given agent on a given target argumentation system in order to achieve some given goal.