Marcelo Alejandro Falappa

Explanations, belief revision and defeasible reasoning

We present different constructions for nonprioritized belief revision, that is, belief changes in which the input sentences are not always accepted. First, we present the concept of explanation in a deductive way. Second, we define multiple revision operators with respect to sets of sentences (representing explanations), giving representation theorems. Finally, we relate the formulated operators with argumentative systems and default reasoning frameworks.

Credibility limited revision

Five types of constructions are introduced for non-prioritized belief revision, i.e., belief revision in which the input sentence is not always accepted. These constructions include generalizations of entrenchment-based and sphere-based revision. Axiomatic characterizations are provided, and close interconnections are shown to hold between the different constructions.

The Added Value of Argumentation

We discuss the value of argumentation in reaching agreements, based on its capability for dealing with conflicts and uncertainty. Logic-based models of argumentation have recently emerged as a key topic within Artificial Intelligence. Key reasons for the success of these models is that they are akin to human models of reasoning and debate, and their generalisation to frameworks for modelling dialogues. They therefore have the potential for bridging between human and machine reasoning in the presence of uncertainty and conflict. We provide an overview of a number of examples that bear witness to this potential, and that illustrate the added value of argumentation. These examples amount to methods and techniques for argumentation to aid machine reasoning (e.g. in the form of machine learning and belief functions) on the one hand and methods and techniques for argumentation to aid human reasoning (e.g. for various forms of decision making and deliberation and for the Web) on the other. We also identify a number of open challenges if this potential is to be realised, and in particular the need for benchmark libraries.

Review: on the evolving relation between belief revision and argumentation

Research on the relation between Belief Revision and Argumentation has always been fruitful in both directions: some argumentation formalisms can be used to define belief change operators, and belief change techniques have also been used for modeling the dynamics of beliefs in argumentation formalisms. In this paper, we give a historical perspective on how belief revision has evolved in the last three decades, and how it has been combined with argumentation. First, we will recall the foundational works concerning the links between both areas. On the basis of such insights, we will present a conceptual view on this topic and some further developments. We offer a glimpse into the future of research in this area based on the understanding of argumentation and belief revision as complementary, mutually useful disciplines.

Prioritized and Non-prioritized Multiple Change on Belief Bases

In this article we explore multiple change operators, i.e., operators in which the epistemic input is a set of sentences instead of a single sentence. We propose two types of change: prioritized change, in which the input set is fully accepted, and symmetric change, where both the epistemic state and the epistemic input are equally treated. In both kinds of operators we propose a set of postulates and we present different constructions: kernel changes and partial meet changes.

Stratified Belief Bases Revision with Argumentative Inference

We propose a revision operator on a stratified belief base, i.e., a belief base that stores beliefs in different strata corresponding to the value an agent assigns to these beliefs. Furthermore, the operator will be defined as to perform the revision in such a way that information is never lost upon revision but stored in a stratum or layer containing information perceived as having a lower value. In this manner, if the revision of one layer leads to the rejection of some information to maintain consistency, instead of being withdrawn it will be kept and introduced in a different layer with lower value. Throughout this development we will follow the principle of minimal change, being one of the important principles proposed in belief change theory, particularly emphasized in the AGM model. Regarding the reasoning part from the stratified belief base, the agent will obtain the inferences using an argumentative formalism. Thus, the argumentation framework will decide which information prevails when sentences of different layers are used for entailing conflicting beliefs. We will also illustrate how inferences are changed and how the status of arguments can be modified after a revision process.

Relational databases as a massive information source for defeasible argumentation

Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.

Selective revision by deductive argumentation

The success postulate of classic belief revision theory demands that after revising some beliefs with by information the new information is believed. However, this form of prioritized belief revision is not apt under many circumstances. Research in non-prioritized belief revision investigates forms of belief revision where success is not a desirable property. Herein, selective revision uses a two step approach, first applying a transformation function to decide if and which part of the new information shall be accepted, and second, incorporating the result using a prioritized revision operator. In this paper, we implement a transformation function by employing deductive argumentation to assess the value of new information. Hereby we obtain a non-prioritized revision operator that only accepts new information if believing in the information is justifiable with respect to the beliefs. By making use of previous results on selective revision we prove that our revision operator satisfies several desirable properties. We illustrate the use of the revision operator by means of examples and compare it with related work.

An argumentation machinery to reason over inconsistent ontologies

Widely accepted argumentation techniques are adapted to define a non-standard description logic (DL) reasoning machinery. A DL-based argumentation framework is introduced to reason about potentially inconsistent ontologies. Arguments in this framework can handle different DL families like ALC, eL, and DL-Lite. Afterwards, we propose an algorithm based on debugging techniques and classical tableau-based ALC satisfiability to build arguments, and discuss about the computational cost of reasoning through the proposed machinery.

Belief revision in structured probabilistic argumentation

In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.