Pascal Nicolas

Possibilistic uncertainty handling for answer set programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.

The First Version of a New ASP Solver: ASPeRiX

We present the first version of our ASP solver ASPeRiX that implements a new approach of answer set computation. The main specifity of our system is to realize a forward chaining of first order rules that are grounded on the fly. So, unlike all others available ASP systems ASPeRiX does not need a pregrounding processing.

A First Order Forward Chaining Approach for Answer Set Computing

The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a Constraint Satisfaction Problem is to elaborate a first order logic program with default negation. In a preliminary step this program, with variables, is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this work, our major contribution is to introduce a new approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. Our methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced constants. We have implemented this strategy in our new ASP solver ASPeRiX . The first benefit of our work is to avoid the bottleneck of instantiation phase arising for some problems because of the huge amount of memory needed to ground all rules of a program, even if these rules are not really useful in certain cases. The second benefit is to make the treatment of function symbols easier and without syntactic restriction provided that rules are safe.

Early prediction of unexplained syncope by support vector machines

The goal of the present study was to develop and evaluate a new method for the prediction of unexplained syncope occurrences. Diagnosis of syncope is currently based on the reproduction of symptoms in combination with hypotension and bradycardia induced by a 45 min 60-70 degrees head-upright tilt test (HUTT). The main drawback of this widely used test concerns its duration that reaches 55 min if the patient does not faint. Our method is a first step in the avoidance of the HUTT. An electrocardiogram and a transthoracic impedance waveform were recorded for 10 min of supine rest of a HUTT in 128 patients with a history of unexplained recurrent syncope. Seven indices were computed on the transthoracic impedance and its first derivative. The prediction quality of every subset of these variables, mixed with age and sex, has been tested by a support vector machine in a retrospective group of 64 patients (100% of sensitivity and 100% of specificity was reached). The best subset obtained has been evaluated prospectively in a group of 64 patients (94% of sensitivity and 79% of specificity was reached). These results compare very favorably with published results for other unexplained syncope detectors.

The XRay system: An implementation platform for local query-answering in default logics

We present an implementation platform for query-answering in default logics, supporting local proof procedures. We describe the salient features of the corresponding system, called XRay, and provide some major theoretical underpinnings. The deductive power of XRay stems from its usage of Prolog Technology Theorem Proving Techniques (PTTP). This is supported by further enhancements, such as default lemma handling, regularity-based truncations of the underlying search space, and further configurable features. The computational value of these enhancements is backed up by a series of experiments that provide us with valuable insights into their inuence on XRay’s performance. The generality of the approach, allowing for a (simultaneous) treatment of different default logics, stems from a novel model-based approach to consistency checking.

An implementation platform for query-answering in default logics: The XRay system, its implementation and evaluation

We present an implementation platform for query-answering in default logics. The overall approach along with its implementation, the XRay system, allows for query-answering from default theories supporting local proof procedures. The deductive power of XRay stems from it susage of Prolog Technology Theorem Proving Techniques (PTTP) supported by further enhancements, such as default lemma handling and regularity-based truncations of the underlying search space. The generality of the approach, allowing for a (simultaneous) treatment of different default logics, stems from a novel model-based approach to consistency checking.An arrow rest assembly including a coil spring or equivalent having multi-directional flexibility including flexibility in various planes serves to more effectively dampen oscillations set up in an arrow when released from a bow.

A possibilistic inconsistency handling in answer set programming

Both in classical logic and in Answer Set Programming, inconsistency is characterized by non existence of a model. Whereas every formula is a theorem for inconsistent set of formulas, an inconsistent program has no answer. Even if these two results seem opposite, they share the same drawback: the knowledge base is useless since one can not draw valid conclusions from it. Possibilistic logic is a logic of uncertainty able to deal with inconsistency in classical logic. By putting on every formula a degree of certainty, it defines a way to compute, with regard to these degrees, a consistent subset of formulas that can be then used in a classical inference process. In this work, we address the treatment of inconsistency in Answer Set Programming by a possibilistic approach that takes into account the non monotonic aspect of the framework.

A new parallel architecture for QBF tools

In this paper, we present the main lines and a first implementation of an open general parallel architecture that we propose for various computation problems about Quantified Boolean Formulae. One main feature of our approach is to deal with QBF without syntactic restrictions, as prenex form or conjunctive normal form. Another main point is to develop a general parallel framework in which we will be able in the future to introduce various specialized algorithms dedicated to particular subproblems.

Dialectical Proofs for Constrained Argumentation

Constrained argumentation frameworks (CAF) generalize Dungs frameworks by allowing additional constraints on arguments to be taken into account in the definition of acceptability of arguments. These constraints are expressed by means of a logical formula which is added to Dungs framework. The resulting system captures several other extensions of Dungs original system. To determine if a set of arguments is credulously inferred from a CAF, the notion of dialectical proof (alternating pros and cons arguments) is extended for Dungs frameworks in order to respect the additional constraint. The new constrained dialectical proofs are computed by using Answer Set Programming.

XRay: A Prolog Technology Theorem Prover for Default Reasoning: A System Description

XRay is a theorem prover for default logics. Its deductive power is primarily due to our approach of integrating default reasoning into existing model elimination based provers using the well-known PTTP approach. We conceived and integrated a number of enhancements, such as lemma handling, regularity-based truncations of underlying search spaces and a model-based approach to consistency checking.