Daniele Theseider Dupré

On the Relationship Between Abduction and Deduction

n a The aim of this paper is at analyzing from various points of view the relationships betwee bduction and deduction. In particular, we consider a meta-level definition of abduction in l d terms of deduction, similar to various definitions proposed in the literature, and an object-leve efinition in which abductive conclusions are expressed as a logical consequence of the obser. T vations and of a simple transformation of the domain theory based on predicate completion he equivalence between the two definitions is proved for domain theories of considerable s expressive power. The object-level characterization we propose uses very simple forms of rea oning and the equivalence result allows us to make explicit some of the assumptions underly-

A spectrum of definitions for temporal model-based diagnosis

Abstract Model-based diagnosis (MBD) tackles the problem of troubleshooting systems starting from a description of their structure and function (or behavior). Time is a fundamental dimension in MBD: the behavior of most systems is time-dependent in one way or another. Temporal MBD, however, is a difficult task and indeed many simplifying assumptions have been adopted in the various approaches in the literature. These assumptions concern different aspects such as the type and granularity of the temporal phenomena being modeled, the definition of diagnosis, the ontology for time being adopted. Unlike the atemporal case, moreover, there is no general “theory” of temporal MBD which can be used as a knowledge-level characterization of the problem. In this paper we present a general characterization of temporal model-based diagnosis. We distinguish between different temporal phenomena that can be taken into account in diagnosis and we introduce a modeling language which can capture all such phenomena. Given a suitable logical semantics for such a modeling language, we introduce a general characterization of the notions of diagnostic problem and explanation, showing that in the temporal case these definitions involve different parameters. Different choices for the parameters lead to different approaches to temporal diagnosis. We define a framework in which different dimensions for temporal model-based diagnosis can be analyzed at the knowledge level, pointing out which are the alternatives along each dimension and showing in which cases each one of these alternatives is adequate. In the final part of the paper we show how various approaches in the literature can be classified within our framework. In this way, we propose some guidelines to choose which approach best fits a given application problem.

Model-based diagnosis meets error diagnosis in logic programs

A lot of attention has been paid in the last years in the logic programming community to the design of automatic declarative error diagnosers (after Shapiros seminal work [10]). Such diagnosers are declarative in the sense that they do not need any understanding of the computational behavior of the program but they need to know only the intended interpretation of the program (the set of answers that the program should compute). Information on such an intended behavior is obtained querying an oracle, usually corresponding to the

Enhancing Web services with diagnostic capabilities

Fault management in Web services composed by individual services from multiple suppliers currently relies on a local analysis that does not span across individual services, thus limiting the effectiveness of recovery strategies. We propose to address this limitation of current standards for Web service composition by employing model-based diagnosis to enhance fault analysis. We propose to add diagnostic Web services to the set of Web services providing the overall service, acting as supervisors of their execution, by identifying anomalies and explaining them in terms of faults to be repaired. This approach poses the basis for the development of specialized recovery and compensation techniques aimed at addressing different problems, which could not be otherwise discriminated.

Verifying business process compliance by reasoning about actions

In this paper we address the problem of verifying business process compliance with norms. To this end, we employ reasoning about actions in a temporal action theory. The action theory is defined through a combination of Answer Set Programming and Dynamic Linear Time Temporal Logic (DLTL). The temporal action theory allows us to formalize a business process as a temporal domain description, possibly including temporal constraints. Obligations in norms are captured by the notion of commitment, which is borrowed from the social approach to agent communication. Norms are represented using (possibly) non monotonic causal laws which (possibly) enforce new obligations. In this context, verifying compliance amounts to verify that no execution of the business process leaves some commitment unfulfilled. Compliance verification can be performed by Bounded Model Checking.

Adcuctive Reasoning with Abstraction Axioms

This paper deals with abductive reasoning on knowledge bases that are expressed at different levels of abstraction, but are not necessarily organized as a set of increasingly more abstract models, each one giving a complete (even if abstracted) description of a domain. We claim that the search for abductive explanations in such a context and, in particular, the choice of the “right” level at which explanations have to be determined, should be driven by the available observations in such a way that explanations involving low-level phenomena are allowed only if there are specific observations related to them, or higher-level explanations cannot be found. We present formal definitions following this principle and we discuss how explanations can be computed according to the definition.

An efficient algorithm for temporal abduction

Abduction is the process of generating explanations for an observation, starting from a domain theory. We consider an extension of the classical definition of abduction in order to deal with temporal knowledge both in the theory and in the observations. This form of abduction is the basis of many problem solving activities such as temporal diagnosis and reasoning about actions and events. We show how the locality property can be used in order to compute temporal explanations efficiently. In fact, they could be computed using first an abductive reasoner as a generator of candidates and then a temporal reasoner for checking the consistency of each candidate.

Reasoning about actions with Temporal Answer Sets

In this paper, we combine Answer Set Programming (ASP) with Dynamic Linear Time Temporal Logic (DLTL) to define a temporal logic programming language for reasoning about complex actions and infinite computations. DLTL extends propositional temporal logic of linear time with regular programs of propositional dynamic logic, which are used for indexing temporal modalities. The action language allows general DLTL formulas to be included in domain descriptions to constrain the space of possible extensions. We introduce a notion of Temporal Answer Set for domain descriptions, based on the usual notion of Answer Set. Also, we provide a translation of domain descriptions into standard ASP and use Bounded Model Checking (BMC) techniques for the verification of DLTL constraints.

Interacting with social networks of intelligent things and people in the world of gastronomy

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Semantic similarity in heterogeneous ontologies

Recent extensive usage of ontologies as knowledge bases that enable rigorous representation and reasoning over heterogenous data poses certain challenges in their construction and maintenance. Many of these ontologies are incomplete, containing many dense sub-ontologies. A need arises for a measure that would help calculate the similarity between the concepts in these kinds of ontologies. In this work, we introduce a new similarity measure for ontological concepts that takes these issues into account. It is based on conceptual specificity, which measures how much a certain concept is relevant in a given context, and on conceptual distance, which introduces different edge lengths in the ontology graph. We also address the problem of computing similarity between concepts in the presence of implicit classes in ontologies. The evaluation of our approach shows an improvement over Leacock and Chodorows distance based measure. Finally, we provide two application domains which can benefit when this similarity measure is used.