Belief Revision in Expressive Knowledge Representation Formalisms

We live in an era of data and information, where an immeasurable amount of discoveries, findings, events, news, and transactions are generated every second. Governments, companies, or individuals have to employ and process all that data for knowledge-based decision-making (i.e. a decision-making process that uses predetermined criteria to measure and ensure the optimal outcome for a specific topic), which then prompt them to view the knowledge as valuable resource. In this knowledge-based view, the capability to create and utilize knowledge is the key source of an organization or individual’s competitive advantage. This dynamic nature of knowledge leads us to the study of belief revision (or belief change), an area which emerged from work in philosophy and then impacted further developments in computer science and artificial intelligence. In belief revision area, the AGM postulates by Alchourrón, Gärdenfors, and Makinson continue to represent a cornerstone in research related to belief change. Katsuno and Mendelzon (K&M) adopted the AGM postulates for changing belief bases and characterized AGM belief base revision in propositional logic over finite signatures. In this thesis, two research directions are considered. In the first, by considering the semantic point of view, we generalize K&M’s approach to the setting of (multiple) base revision in arbitrary Tarskian logics, covering all logics with a classical model-theoretic semantics and hence a wide variety of logics used in knowledge representation and beyond. Our generic formulation applies to various notions of “base”, such as belief sets, arbitrary or finite sets of sentences, or single sentences. The core result is a representation theorem showing a two-way correspondence between AGM base revision operators and certain “assignments”: functions mapping belief bases to total — yet not transitive — “preference” relations between interpretations. Alongside, we present a companion result for the case when the AGM postulate of syntax-independence is abandoned. We also provide a characterization of all logics for which our result can be strengthened to assignments producing transitive preference relations (as in K&M’s original work), giving rise to two more representation theorems for such logics, according to syntax dependence vs. independence. The second research direction in this thesis explores two approaches for revising description logic knowledge bases under fixed-domain semantics, namely model-based approach and individual-based approach. In this logical setting, models of the knowledge bases can be enumerated and can be computed to produce the revision result, semantically. We show a characterization of the AGM revision operator for this logic and present a concrete model-based revision approach via distance between interpretations. In addition, by weakening the KB based on certain domain elements, a novel individual-based revision operator is provided as an alternative approach

An comparative analysis of different models of belief revision using information from multiple sources

In this work we analyze the problem of knowledge representation in a collaborative multi-agent system where agents can obtain new information from others through communication. Namely, we analyze several approaches of belief revision in multi-agent systems. We will describe different research lines in this topic and we will focus on Belief Revision using Information from Multiple Sources. For this, we are going to accomplish a comparative analysis of different models of belief revision that use information from multiple sources.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Embedding defeasible argumentation in the semantic web: an ontology-based approach

The SemanticWeb is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web by means of ontology definitions. Ontologies intended for knowledge representation in intelligent agents rely on common-sense reasoning formalizations. Defeasible argumentation has emerged as a successful approach to model common-sense reasoning. Recent research has linked argumentation with belief revision in order to model the dynamics of knowledge. This paper outlines an approach which combines ontologies, argumentation and belief revision by defining an ontology algebra. We suggest how different aspects of ontology integration can be defined in terms of defeasible argumentation and belief revision.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Case Adaptation with Qualitative Algebras

This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given. (The paper on which this extended abstract is based was the recipient of the best paper award of the 2012 International Conference on Case-Based Reasoning.

Ontology revision on the semantic web: integration of belief revision theory

The vision of the Semantic Web is to enable content of web resources to be interpreted and processed by software agents. Ontology provides a means to share and reuse data associated with web resources in a manner that can be autonomously performed by software agents. In the context of knowledge representation, ontology represents the abstract world of web resources in the Semantic Web. The Semantic Web will comprise of small, simple ontologies constructed by individual users. It is unlikely that ontology will be built from scratch each time. On the other hand, it is more likely that ontology will be adopted and modified from existing ontology. Why is ontology revision important? Very often, ontology exists in a particular period of timeline is designed based on the purpose of a specific domain of interest at that instance of time. Over time, ontology needs to be revised due to changes in domain, content, requirements, or structural representation. In this regard, ontology is the beliefs that the agents need to reference to in order to perform task in an autonomous way. As ontology evolves, beliefs in agents also evolve and knowledge gained by agents must be reflected in the ontology. This research investigates issues of ontology revision from the theoretical foundation of the belief revision theory. The AGM model of the coherence theory in belief revision is of particular relevant in this research. The AGM model uses three operations of expansion, contraction and revision in conjunction with the concept of epistemic entrenchment to revise the belief set. This research develops an ontology revision framework to manage the ontology revision process. The research will also illustrate a vision in which the practicability of this approach can be applied in e-commerce

Towards a belief revision based adaptive and context sensitive information retrieval system

In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections

Non prioritized answer set revision

In this paper, we build on previous work on Belief Revision operators based on the use of logic programming with Answer Set semantics as a representation language. We present a set of postulates for Answer Set Revision with respect to a set of sentences and with respect to explanations. We focus on the non-prioritized revision operator with respect to explanations, or arguments, which is intended to model situations in which agents revise their knowledge as a result of dialogues with other agents in a multi-agent setting.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Belief dynamics and explanations in ansprolog

Knowledge representation models are very important in the design of intelligent agents because they provide with mechanisms to manage beliefs and their dynamics. In this paper, we propose the use of AnsProlog* as a knowledge representation language, and develop a Non Prioritized Belief Revision operator based on the Answer Set semantics and the use of explanations. This operator is suitable for multiagent environments, in which agents can exchange information by having dialogues which explain their respective beliefs. A simple, yet complete example follows the presentation of this operator.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

An comparative analysis of different models of belief revision using information from multiple sources

In this work we analyze the problem of knowledge representation in a collaborative multi-agent system where agents can obtain new information from others through communication. Namely, we analyze several approaches of belief revision in multi-agent systems. We will describe different research lines in this topic and we will focus on Belief Revision using Information from Multiple Sources. For this, we are going to accomplish a comparative analysis of different models of belief revision that use information from multiple sources.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI