Belief Dynamics in Complex Social Networks

People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Belief Dynamics in Complex Social Networks

People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) 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 the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

Multiple Revision on Horn Belief Bases

In logic programming, Horn clauses play a basic role, and in many logical constructs their consideration is important. In this paper we study the multiple revision of a belief base where the underlying logic is composed by Horn clauses. The main di culties as to restricting to the Horn fragment for revision operators by a single sentence are analyzed, and general results are presented about multiple revision operators on belief bases. We de ne prioritized multiple revision operators under a more restricted logic than classical propositional logic, i.e. Horn logic. We propose a set of postulates and representation theorems for each operation. This work is relevant for multiple revision in areas that employ Horn clauses, such as logic programming and deductive databases applications.XVII Workshop Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

Multiple Revision on Horn Belief Bases

In logic programming, Horn clauses play a basic role, and in many logical constructs their consideration is important. In this paper we study the multiple revision of a belief base where the underlying logic is composed by Horn clauses. The main di culties as to restricting to the Horn fragment for revision operators by a single sentence are analyzed, and general results are presented about multiple revision operators on belief bases. We de ne prioritized multiple revision operators under a more restricted logic than classical propositional logic, i.e. Horn logic. We propose a set of postulates and representation theorems for each operation. This work is relevant for multiple revision in areas that employ Horn clauses, such as logic programming and deductive databases applications.XVII Workshop Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

A Rational and Efficient Algorithm for View Revision in Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In this paper, we argue that to apply rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first of all, it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented, along with the concept of a generalized revision algorithm for Horn knowledge bases. We show that Horn knowledge base dynamics has interesting connection with kernel change and abduction. Finally, we also show that both variants are rational in the sense that they satisfy certain rationality postulates stemming from philosophical works on belief dynamics

A New Rational Algorithm for View Updating in Relational Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515

Belief Dynamics in Complex Social Networks

People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.Sociedad Argentina de Informática e Investigación Operativa (SADIO