Farid Nouioua

Bipolar Argumentation Frameworks with Specialized Supports

We generalize in this paper the Dung’s abstract argumentation theory in order to represent, in addition to the attack relation, a particular kind of support relation which captures knowledge of the form : “argument a is necessary to obtain argument b”. Unlike a general unspecified support, the necessity relation has the advantage to ensure that its interaction with direct attacks generates new (indirect) attacks having exactly the same nature of the direct ones. We discuss the advantageous consequences of this specialization of the support relation on the acceptability semantics of the underlying bipolar framework. Then, we show a suitable translation of any such bipolar framework into a logic program without passing by a preliminary translation into the classical framework of Dung.

Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems

The firefly algorithm is a recent meta-heuristic inspired from nature. It is based on swarm intelligence of fireflies and generally used for solving continuous optimization problems. This paper proposes a new algorithm called “Quantum-inspired Firefly Algorithm with Particle Swarm Optimization (QIFAPSO)” that among other things, adapts the firefly approach to solve discrete optimization problems. The proposed algorithm uses the basic concepts of quantum computing such as superposition states of Q-bit and quantum measure to ensure a better control of the solutions diversity. Moreover, we use a discrete representation for fireflies and we propose a variant of the well-known Hamming distance to compute the attractiveness between them. Finally, we combine two strategies that cooperate in exploring the search space: the first one is the move of less bright fireflies towards the brighter ones and the second strategy is the PSO movement in which a firefly moves by taking into account its best position as well as the best position of its neighborhood. Of course, these two strategies of fireflies’ movement are adapted to the quantum representation used in the algorithm for potential solutions. In order to validate our idea and show the efficiency of the proposed algorithm, we have used the multidimensional knapsack problem which is known as an NP-Complete problem and we have conducted various tests of our algorithm on different instances of this problem. The experimental results of our algorithm are competitive and in most cases are better than that of existing methods.

Diagnosability verification with Petri net unfoldings

Complex systems increasingly require safety and robustness with regards to faults occurrences, and diagnosability is a key property to ensure this at design stage. This paper demonstrates how Petri net unfoldings, which have been proven to elevate the state explosion problem, can be applied to verify diagnosability by adapting the twin plant method.

AFs with Necessities: Further Semantics and Labelling Characterization

The Argumentation Frameworks with Necessities (AFNs) proposed in [17] are a kind of bipolar AFs extending Dung AFs with a support relation having the particular meaning of necessity. This paper is a continuation of this work in two respects. First, we complete the acceptability semantics picture by defining the well-founded, the complete and the semi-stable semantics for AFNs. We show that the proposed semantics keep the same properties as those given for Dung AFs and represent proper generalizations of them (in absence of the necessity relation, the classical semantics are recovered). Then, we show how to generalize Caminada’s labelling algorithms in presence of a necessity relation to compute the extensions under the studied semantics for AFNs.

A Comparative Study of Six Formal Models of Causal Ascription

Ascribing causality amounts to determining what elements in a sequence of reported facts can be related in a causal way, on the basis of some knowledge about the course of the world. The paper offers a comparison of a large span of formal models (based on structural equations, non-monotonic consequence relations, trajectory preference relations, identification of violated norms, graphical representations, or connectionism), using a running example taken from a corpus of car accident reports. Interestingly enough, the compared approaches focus on different aspects of the problem by either identifying all the potential causes, or selecting a smaller subset by taking advantages of contextually abnormal facts, or by modeling interventions to get rid of simple correlations. The paper concludes by a general discussion based on a battery of criteria (several of them being proper to AI approaches to causality).

Predictability analysis of distributed discrete event systems

Predictability is an important system property that determines with certainty the future occurrence of a fault based on a model of the system and a sequence of observations. The existing works dealt with predictability analysis of discrete-event systems in the centralized way. To deal with this important problem in a more efficient way, in this paper, we first propose a new centralized polynomial algorithm, which is inspired from twin plant method for diagnosability checking and more importantly, is adaptable to a distributed framework. Then we show how to extend this algorithm to a distributed one, based on local structure. We first obtain the original predictability information from the faulty component, and then check its consistency in the whole system to decide predictability from a global point of view. In this way, we avoid constructing global structure and thus greatly reduce the search space.

Removed Set-Based Revision of Abstract Argumentation Frameworks

Argumentation frameworks have aroused intense interest from the AI community over the past years. Dynamic aspects of argumentation frameworks have received some interest from the community, but none of these works tries to address the recovering problem, that is, what shall we do when the new addition leads to the loss of all extensions. Such problem is typically a belief revision problem. In this paper, we propose a revision operator to revise an argumentation framework by another one, with the guarantee that the result of the operation will be an argumentation framework which has at least one stable extension. We also propose an algorithm to compute the revision operation outcome.

Diagnosability of Input Output Symbolic Transition Systems

Diagnosability checking of discrete-event systems hasbeen extensively studied in the framework of classical nonsymbolic models such as Labeled Transition Systems. Ithappens that in practice such models tend to need too muchspace to be efficiently processed. By opposition, symbolic approachesoffer an expressive, easy and concise way to modelsystems, and checking diagnosability from such symbolicmodels can benefit from this reduction of space complexity.Indeed, though this will generally translate into time complexity,such a tradeoff is advantageous, as diagnosabilitychecking is something that is usually done at design stage.This is why this paper proposes a theoretical frameworkto check diagnosability of Input Output Symbolic TransitionSystems (IOSTS) by adapting the twin plant approach to thesymbolic case and relying on the use of a symbolic modelchecker. This theoretical work is being currently applied toembedded functions inside a vehicle in the context of anindustrial project and a simplified version of this problemwill serve as a running example throughout the presentation.

Undercutting in Argumentation Systems

Rule-based argumentation systems are developed for reasoning about defeasible information. They take as input a theory made of a set of strict rules, which encode strict information, and a set of defeasible rules which describe general behaviour with exceptional cases. They build arguments by chaining such rules, define attacks between them, use a semantics for evaluating the arguments, and finally identify the plausible conclusions that follow from the rules. One of the main attack relations of such systems is the so-called undercutting which blocks the application of defeasible rules in some contexts. In this paper, we show that this relation is powerful enough to capture alone all the different conflicts in a theory. We present the first argumentation system that uses only undercutting and fully characterize both its extensions and its plausible conclusions under various acceptability semantics.

Generalizing naive and stable semantics in argumentation frameworks with necessities and preferences

In [4] [5], the classical acceptability semantics are generalized by preferences. The extensions under a given semantics correspond to maximal elements of a relation encoding this semantics and defined on subsets of arguments. Furthermore, a set of postulates is proposed to provide a full characterization of any relation encoding the generalized stable semantics. In this paper, we adapt this approach to preference-based argumentation frameworks with necessities. We propose a full characterization of stable and naive semantics in this new context by new sets of adapted postulates and we present a practical method to compute them by using a classical Dung argumentation framework.