Defeasible Logic Programming Research Articles

When is argumentation deductive?

This paper discusses and compares various answers to the question when argumentation is deductive. This includes an answer to the questions when argumentation is defeasible and whether defeasible argumentation is a subclass of deductive argumentation or whether it is a distinct form of argumentation. It is concluded that deductive and defeasible argumentation as conceived by Philosophers like Pollock and Rescher and as formalised in the ASPIC framework and systems like Defeasible Logic Programming, are semantically different categories. For this reason, purely syntactic base logic approaches to formal argumentation are unsuitable for characterising this distinction.

Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity

Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations.

Incremental computation for structured argumentation over dynamic DeLP knowledge bases

Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e.g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously.

An informant-based approach to argument strength in Defeasible Logic Programming

This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant agents. Moreover, we consider that the strength of an argument is not absolute, but it is relative to the resolution of the conflicts the argument is involved in. In other words, the strength of an argument may vary from one context to another, as it will be determined by comparison to its attacking arguments (respectively, the arguments it attacks). Finally, we equip agents with the means to express reasons for or against the consideration of any piece of information provided by a given informant agent. Consequently, we allow agents to argue about the arguments’ strength through the construction of arguments that challenge (respectively, defeat) or are in favour of their informant agents.

An approach to generalizing the handling of preferences in argumentation-based decision-making systems

As a practical mechanism for formalizing commonsense reasoning, argumentation has shown its potential for applications in diverse areas, many related to decision-making in knowledge-based systems. Following this line, and for helping users in making a better and informed decision, different recommender systems proposals have been developed in the argumentation literature. We will use recommender systems as a good example where to exercise our proposal. In particular, the role of preference criterion in argumentation-based recommender systems which is used to compare competing arguments is central to the user’s query answering process where if the criterion does not adjust to the represented domain, the system could fail by being undecided too often. Therefore, having tools that allow to select and change the argument comparison mechanism has to be used become a central issue. Argumentation-based recommender systems that offer these tools provide an interesting ability that can be used for improving the reasoning capabilities in this type of systems. This work introduces an approach to handle multiple argument preference criteria in argumentation-based recommender systems and general knowledge-based decision support systems. More precisely, the proposal allows changing the information that a criterion can use in the argument comparison process and specify how several criteria can be simultaneously used in such process as well; to achieve that goal, a set of operators to combine several criteria is presented. The knowledge representation and reasoning is performed in Defeasible Logic Programming, a defeasible argumentation formalism based on logic programming.

Argumentos Máximamente Específicos en Argumentación Rebatible

Resumen DeLP (Defeasible Logic Programming) is a defeasible argumentation system that captures common sense reasoning features. Examples proposed in the literature show that DeLP gets counterintuitive results. We suggest a possible cause of this problem and we propose an approach to neutralize it. The approach is based on the pre-selection criterion of arguments, called maximal specificity. The criterion establishes that an argument will be considered like maximally specific only if, for every argument based on more specific evidence which does not explain the same conclusion, it explains neither the contrary nor something more general.

A structured argumentation system with backing and undercutting

This work introduces Extended Defeasible Logic Programming (E-DeLP), a structured argumentation system enabling the expression of reasons for and against using defeasible rules. E-DeLPextends the formalism of Defeasible Logic Programming (DeLP) by incorporating two new kinds of rules: backing and undercutting rules. In that way, E-DeLPaccounts for Toulmin׳s notion of backing and Pollock׳s notion of undercut, two important contributions within the field of argumentation. Thus, E-DeLPconstitutes a novel approach, being the first structured argumentation system in providing a unified setting for modeling Toulmin׳s and Pollock׳s ideas simultaneously.

Assuring safety in air traffic control systems with argumentation and model checking

Although the continuous safety technology advances in fields like air traffic control (ATC) systems or medical devices, the crux of safety assurance still comes down to human decision makers, which, within the context of having to define priorities while simultaneously considering different contextual criteria, present a constant high risk of erroneous decisions. We illustrate in this article a recommender framework for assisting flight controllers, which combines argumentation theory and model checking in the evaluation of trade-offs and compromises to be made in the presence of incomplete and potentially inconsistent information. We view a Hybrid Kripke model as a description of an ATC domain and we apply a rational decision strategy based on Hybrid Logics and Defeasible Reasoning to assist the process of model update when the system has to accommodate new properties or norm constraints. When the model fails to verify a property, a defeasible logic program is used to analyze the current state and perform updating operations on the model. The introduced decision making framework is tested on a recommender system in ATC and model update is demonstrated with respect to the verification and adaption of unmanned aerial vehicles routes in the air traffic space. The results show an important potential for the presented framework to be integrated directly into existing decision-making routines for achieving higher accuracy in recommender system methods.

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.

Providing personalized recommendation for attending events based on individual interest profiles

In this article we present a framework to extract user interests from social network profiles such as Facebook to personalize recommendations about products and services. Matching users’ interests as keywords with product attributes as keywords, performed by currently available personalization systems, has a very low recall, so more general category-based frameworkis needed. It turns out that substantial reasoning about products and their categories is required to match a taxonomy of theowner of products and services, with that of a user, as expressed in a public profile. To handle inconsistencies between the setaxonomies, a mapping of one into another is expressed as a Defeasible Logic program (DeLP), where a potential mapping can bedefeated by other ones if relevant information becomes available. Events and things to do are recommended at StubHub.com and www.facebook.com/StubHub/ so that the reader can observe the system at a scale. Also, we present content management system which supports personalized recommendation is outlined.

A quantitative approach to belief revision in structured probabilistic argumentation

Many real-world knowledge-based systems must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn't mean that the information is worthless --- rather, these are hurdles that the knowledge engineer must learn to work with. In this paper, we continue work on an argumentation-based framework that extends the well-known Defeasible Logic Programming (DeLP) language with probabilistic uncertainty, giving rise to the Defeasible Logic Programming with Presumptions and Probabilistic Environments (DeLP3E) model. Our prior work focused on the problem of belief revision in DeLP3E, where we proposed a non-prioritized class of revision operators called AFO (Annotation Function-based Operators) to solve this problem. In this paper, we further study this class and argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account, such as how the probabilities of certain literals or formulas of interest change after the revision takes place. To the best of our knowledge, this problem has not been addressed in the argumentation literature to date. We propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and then go on to study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations.

Formalisation and logical properties of the maximal ideal recursive semantics for weighted defeasible logic programming

Possibilistic defeasible logic programming (P-DeLP) is a logic programming framework which combines features from argumentation theory and logic programming, in which defeasible rules are attached with weights expressing their relative belief or preference strength. In P-DeLP,a conclusion succeeds if there exists an argument that entails the conclusion and this argument is found to be undefeated by a warrant procedure that systematically explores the universe of arguments in order to present an exhaustive synthesis of the relevant chains of pros and cons for the given conclusion. Recently, we have proposed a new warrant recursive semantics for P-DeLP, called Recursive P-DeLP (RP-DeLP for short), based on the claim that the acceptance of an argument should imply also the acceptance of all its sub-arguments which reflect the different premises on which the argument is based. This paper explores the relationship between the exhaustive dialectical analysis-based semantics of P-DeLP and the recursive-based semantics of RP-DeLP, and analyses a non-monotonic inference operator for RP-DeLP which models the expansion of a given program by adding new weighted facts associated with warranted conclusions. Given the recursive-based semantics of RP-DeLP, we have also implemented an argumentation framework for RP-DeLP that is able to compute not only the output of warranted and blocked conclusions, but also explain the reasons behind the status of each conclusion. We have developed this framework as a stand-alone application with a simple text-based input/output interface to be able to use it as part of other artificial intelligence systems.

A possibilistic defeasible logic programming approach to argumentation-based decision-making

The development of symbolic approaches to decision-making has become an ever-growing research line in artificial intelligence; argumentation has contributed to that with its unique strengths. Following this trend, this article proposes a general-purpose decision framework based on argumentation. Given a set of alternatives posed to the decision-maker, the framework represents the agent's preferences and knowledge by an epistemic component developed using possibilistic defeasible logic programming. The reasons by which a particular alternative is deemed better than another are explicitly considered in the argumentation process involved in warranting information from the epistemic component. The information warranted by the dialectical process is then used in decision rules that implement the agent's general decision-making policy. Essentially, decision rules establish patterns of behaviour of the agent specifying under which conditions a set of alternatives will be considered acceptable; moreover, a methodology for programming the agent's epistemic component is defined. It is demonstrated that programming the agent's epistemic component following this methodology exhibits some interesting properties with respect to the selected alternatives; also, when all the relevant information regarding the agent's preferences is specified, its choice behaviour coincides with respect to the optimum preference derived from a rational preference relation.

Argument-based mixed recommenders and their application to movie suggestion

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combines a basic quantitative method with a qualitative approach, resulting in a family of mixed character recommender systems. The proposed framework incorporates the use of arguments in favor or against recommendations to determine if a suggestion should be presented or not to a user. In order to accomplish this, Defeasible Logic Programming (DeLP) is adopted as the underlying formalism to model facts and rules about the recommendation domain and to compute the argumentation process. This approach has a number features that could be proven useful in recommendation settings. In particular, recommendations can account for several different aspects (e.g., the cast, the genre or the rating of a movie), considering them all together through a dialectical analysis. Moreover, the approach can stem for both content-based or collaborative filtering techniques, or mix them in any arbitrary way. Most importantly, explanations supporting each recommendation can be provided in a way that can be easily understood by the user, by means of the computed arguments. In this work the proposed approach is evaluated obtaining very positive results. This suggests a great opportunity to exploit the benefits of transparent explanations and justifications in recommendations, sometimes unrealized by quantitative methods.

RP-DeLP: a weighted defeasible argumentation framework based on a recursive semantics

In this article, we propose a recursive semantics for warranted formulas in a general defeasible logic argumentation framework by formalizing a notion of collective (non-binary) conflict among arguments. The recursive semantics for warranted formulas is based on the intuitive grounds that if an argument is rejected, then further arguments built on top of it should also be rejected. The main characteristic of our recursive semantics is that an output (or extension) of a knowledge base is a pair consisting of a set of warranted and a set of blocked formulas. Arguments for both warranted and blocked formulas are recursively based on warranted formulas but, while warranted formulas do not generate any collective conflict, blocked conclusions do. Formulas that are neither warranted nor blocked correspond to rejected formulas. Then we extend the framework by attaching levels of preference to defeasible knowledge items and by providing a level-wise definition of warranted and blocked formulas. After we consider the warrant recursive semantics for the particular framework of Possibilistic Defeasible Logic Programming (RP-DeLP for short). Since RP-DeLP programmes may have multiple outputs, we define the maximal ideal output of an RP-DeLP programme as the set of conclusions which are ultimately warranted, and we present an algorithm for computing it in polynomial space and with an upper bound on complexity equal to P NP . Finally, we propose an efficient and scalable implementation of this algorithm using SAT encodings, and we provide an experimental evaluation when solving test sets of instances with single and multiple preference levels for defeasible knowledge.

Defeasible logic programming: DeLP-servers, contextual queries, and explanations for answers

Argumentation represents a way of reasoning over a knowledge base containing possibly incomplete and/or inconsistent information, to obtain useful conclusions. As a reasoning mechanism, the way an argumentation reasoning engine reaches these conclusions resembles the cognitive process that humans follow to analyse their beliefs; thus, unlike other computationally reasoning systems, argumentation offers an intellectually friendly alternative to other defeasible reasoning systems. Logic Programming is a computational paradigm that has produced computationally attractive systems with remarkable success in many applications. Merging ideas from both areas, Defeasible Logic Programming offers a computational reasoning system that uses an argumentation engine to obtain answers from a knowledge base represented using a logic programming language extended with defeasible rules. This combination of ideas brings about a computationally effective system together with a human-like reasoning model facilitating its use ...

A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION

Although the importance of trust in recommender systems is widely recognized, the actual mechanisms of trust propagation and trust preservation are poorly understood. This is partly due to the fact that trust is a complex notion, which is typically context dependent, subjective, dynamic and not always transitive or symmetrical. This paper presents a theoretical analysis of the notion of trust in news recommendation and discusses the advantages of modeling this notion using Defeasible Logic Programming, a general-purpose defeasible argumentation formalism based on logic programming. In the proposed framework, users can express explicit trust statements on news reports, news sources and other users. Trust is then modeled and propagated using a dialectical process supported by a Defeasible Logic Programming interpreter. A set of basic postulates for trust and their representation by means of defeasible rules is presented. The suitability of the approach is investigated with a set of illustrative examples and then analyzed from a formal perspective. The obtained results indicate that the proposed framework provides a solid foundation for building trust-based news recommendation services.

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.

Editorial introduction to the special issue

The information available to intelligent agents is often uncertain, inconsistent and incomplete. It is then crucially important to develop logics for uncertainty to achieve different complex reasoning tasks. Uncertainty is present in many applications and may be represented using different frameworks: plausibility orderings, probability theory frameworks, argumentation systems, etc. This special issue gathers five contributions that cover different aspects of reasoning under uncertainty using non classical logics. Four of the five papers are fully revised and extended versions of contributions initially presented at the Fifth International Conference on Scalable Uncertainty Management (SUM’11) The two first papers concern extensions of logic-based programming languages to deal with uncertain information. The paper by Pere Pardo, Lluis Godo entitled “t-DeLP: an argumentation-based Temporal Defeasible Logic Programming framework” deals with handling temporal information using defeasible logic. The proposed framework, called t-DeLP, allows to extend argumentation-based defeasible logic in order to represent temporal processes. The idea is to associate temporal information to literals and to define temporal logic programs are sets of basic temporal facts and (strict or defeasible) durative rules. Concepts of argumentation systems, such as the one of undefeated arguments, are redefined in this t-DeLP framework. A procedure for determining a set of warranted literals is proposed. The paper also discusses rational properties satisfied by the t-DeLP framework. The paper by Georg Gottlob,Thomas Lukasiewicz, Maria Vanina Martinez and Gerardo I. Simari entitled “Query Answering under Probabilistic Uncertainty in Datalog+/ Ontologies” concerns an extension of Datalog+/− to deal with uncertainty. The authors use Markov Logic Networks to provide a probabilistic semantics for the probabilistic Datalog+/−. They provide scalable and sound algorithms for

Modelling argument accrual with possibilistic uncertainty in a logic programming setting

Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have emerged which incorporate the treatment of possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary formalizations have been advanced, they do not take into account possibilistic uncertainty when accruing arguments. In this paper we present a novel approach to model argument accrual with possibilistic uncertainty in a constructive way. The formalization proposed uses P-DeLP’s representation language and notion of argument as a basis.