Working on the Argument Pipeline: Through Flow Issues between Natural Language Argument, Instantiated Arguments, and Argumentation Frameworks

In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and knowledge intensive to conduct policy consultations, where participants contribute statements to the policy discourse. Yet, from such a consultation, we want to derive policy positions, where each position is a set of consistent statements, but where positions may be mutually inconsistent. To address these problems and support policy-making consultations, we consider recent automated techniques in natural language processing, instantiating arguments, and reasoning with the arguments in argumentation frameworks. We discuss application and “bridge” issues between these techniques, outlining a pipeline of technologies whereby: expressions in a controlled natural language are parsed and translated into a logic (a literals and rules knowledge base), from which we generate instantiated arguments and their relationships using a logic-based formalism (an argument knowledge base), which is then input to an implemented argumentation framework that calculates extensions of arguments (an argument extensions knowledge base), and finally, we extract consistent sets of expressions (policy positions). The paper reports progress towards reasoning with web-based, distributed, collaborative, incomplete, and inconsistent knowledge bases expressed in natural language

Propositional Non-Monotonic Reasoning and Inconsistency in Symmetric Neural Networks

We define a notion of reasoning using world-rank-functions, independently of any symbolic language. We then show that every symmetric neural network (like Hopfield networks or Boltzman machines) can be seen as performing a search for a satisfying model of some knowledge that is wired into the network\u27s topology and weights. Several equivalent languages are then shown to describe symbolically the knowledge embedded in these networks. We extend propositional calculus by augmenting assumptions with penalties. The extended calculus (called penalty logic ) is useful in expressing default knowledge, preference between arguments, and reliability of assumptions in an inconsistent knowledge base. Every symmetric network can be described by this language and any sentence in the language is translatable to such network. A proof-theoretic reasoning procedure supplements the model-theoretic definitions and gives an intuitive understanding of the non-monotonic behavior of the reasoning mechanism. Finally we sketch a connectionist inference engine for penalty logic and discuss its capabilities and limitations

Postulates for logic-based argumentation systems

International audienceLogic-based argumentation systems are developed for reasoning with inconsistent information. Starting from a knowledge base encoded in a logical language, they define arguments and attacks between them using the consequence operator associated with the language. Finally, a semantics is used for evaluating the arguments. In this paper, we focus on systems that are based on deductive logics and that use Dung's semantics. We investigate rationality postulates that such systems should satisfy. We define five intuitive postulates: consistency and closure under the consequence operator of the underlying logic of the set of conclusions of arguments of each extension, closure under sub-arguments and exhaustiveness of the extensions, and a free precedence postulate ensuring that the free formulas of the knowledge base (i.e., the ones that are not involved in inconsistency) are conclusions of arguments in every extension. We study the links between the postulates and explore conditions under which they are guaranteed or violated

Conflict resolution when axioms are materialized in semantic-based smart environments.

International audienceIn SemanticWeb applications, reasoning engines that are data intensive commonly materialise inferences to speed up processing at query time. However, in evolving systems, such as smart environments, semantic-based context aware systems (SCAS) [6] or social software with user-generated data, knowledge does not grow monotonically: newer facts may contradict older ones, knowledge may be deprecated, discarded or updated such that knowledge must sometimes be retracted. We are describing a technique to retract explicit and inferred statements, when some information becomes obsolete, as well as retracting any statement that would lead to get back the removed explicit statements. This technique is based on OWL justifications and is triggered whenever a knowledge base becomes inconsistent, such that the system stays in a consistent state all the time, in spite of uncontrolled evolution.We prove termination and correctness of the algorithm, and describe the implementation and evaluation of the proposal

Understanding Inconsistency -- A Contribution to the Field of Non-monotonic Reasoning

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency and restoring consistency in a certain minimal way are frequently occurring issues in the research area of knowledge representation and reasoning. In a seminal paper Raymond Reiter proves a duality between maximal consistent subsets of a propositional knowledge base and minimal hitting sets of each minimal conflict -- the famous hitting set duality. We extend Reiter's result to arbitrary non-monotonic logics. To this end, we develop a refined notion of inconsistency, called strong inconsistency. We show that minimal strongly inconsistent subsets play a similar role as minimal inconsistent subsets in propositional logic. In particular, the duality between hitting sets of minimal inconsistent subsets and maximal consistent subsets generalizes to arbitrary logics if the stronger notion of inconsistency is used. We cover various notions of repairs and characterize them using analogous hitting set dualities. Our analysis also includes an investigation of structural properties of knowledge bases with respect to our notions. Minimal inconsistent subsets of knowledge bases in monotonic logics play an important role when investigating the reasons for conflicts and trying to handle them, but also for inconsistency measurement. Our notion of strong inconsistency thus allows us to extend existing results to non-monotonic logics. While measuring inconsistency in propositional logic has been investigated for some time now, taking the non-monotony into account poses new challenges. In order to tackle them, we focus on the structure of minimal strongly inconsistent subsets of a knowledge base. We propose measures based on this notion and investigate their behavior in a non-monotonic setting by revisiting existing rationality postulates, and analyzing the compliance of the proposed measures with these postulates. We provide a series of first results in the context of inconsistency in abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as skeptical acceptance. Our analysis includes the following problems regarding minimal repairs: existence, verification, computation of one and characterization of all solutions. The latter will be tackled with our previously obtained duality results. Finally, we investigate the complexity of various related reasoning problems and compare our results to existing ones for monotonic logics

Personalizable Knowledge Integration

Large repositories of data are used daily as knowledge bases (KBs) feeding computer systems that support decision making processes, such as in medical or financial applications. Unfortunately, the larger a KB is, the harder it is to ensure its consistency and completeness. The problem of handling KBs of this kind has been studied in the AI and databases communities, but most approaches focus on computing answers locally to the KB, assuming there is some single, epistemically correct solution. It is important to recognize that for some applications, as part of the decision making process, users consider far more knowledge than that which is contained in the knowledge base, and that sometimes inconsistent data may help in directing reasoning; for instance, inconsistency in taxpayer records can serve as evidence of a possible fraud. Thus, the handling of this type of data needs to be context-sensitive, creating a synergy with the user in order to build useful, flexible data management systems. Inconsistent and incomplete information is ubiquitous and presents a substantial problem when trying to reason about the data: how can we derive an adequate model of the world, from the point of view of a given user, from a KB that may be inconsistent or incomplete? In this thesis we argue that in many cases users need to bring their application-specific knowledge to bear in order to inform the data management process. Therefore, we provide different approaches to handle, in a personalized fashion, some of the most common issues that arise in knowledge management. Specifically, we focus on (1) inconsistency management in relational databases, general knowledge bases, and a special kind of knowledge base designed for news reports; (2) management of incomplete information in the form of different types of null values; and (3) answering queries in the presence of uncertain schema matchings. We allow users to define policies to manage both inconsistent and incomplete information in their application in a way that takes both the user's knowledge of his problem, and his attitude to error/risk, into account. Using the frameworks and tools proposed here, users can specify when and how they want to manage/solve the issues that arise due to inconsistency and incompleteness in their data, in the way that best suits their needs