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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

Tag-based user modeling for social multi-device adaptive guides

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0’s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.

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.

On the co-operation between abductive and temporal reasoning in medical diagnosis

On of the basic (and often implicit) assumptions of most first generation diagnostic expert systems is that they operate in a static environment. However, the static domain assumption is very limiting since it requires that all manifestations are observable (and observed) at a unique time point in order to perform diagnosis (and this is unrealistic in medical applications). The adoption of deep and causal models in second generation expert systems provided some insights into how to deal with time in the diagnostic process. There is, in fact, a strong relationship between the notion of causation and the notion of time. In the paper we present an architecture for diagnostic problem solving based on the use of a pathophysiological model in which both causal and temporal relations are explicitly represented. In particular, the architecture is an extension of the causal component of CHECK which has been used to model pathophysiology in the fields of cirrhosis and leprosis. We show that in such an extended framework diagnostic problems can be solved correctly only by means of a strict co-operation between abductive and temporal reasoning. The complexity of such forms of reasoning is analysed and some sources of complexity are singled out. Possible restrictions of the representation formalism are presented and forms of temporal reasoning providing approximate solutions are discussed.

A configurable system for the construction of adaptive virtual stores

With the recent expansion of the Internet, the interest towards electronic sales has quickly grown and many tools have been built to help vendors to set up their Web stores. These tools offer all the facilities for building the store databases and managing the order processing and secure payment transactions, but they typically do not focus on issues like the personalization of the interaction with the customers. However, Web surfers are generally heterogeneous and have different needs and preferences; moreover, the trend of marketing strategies is to pay more and more attention to the specific buyers. So, the importance of personalizing the interaction with the user and the product presentation is increasing. In this paper, we describe the architecture of a configurable virtual Web store supporting personalized hypertextual interactions with users. Our system builds a user profile by applying user modeling techniques and stereotypical information about the characteristics of customer groups; this profile is used during the interaction in order to tailor the product descriptions and the selection of items to recommend to the users needs, varying the layout of the hypertextual pages and the detail of the descriptions accordingly. Tailoring the systems behavior requires the parallel execution of several complex tasks during the interaction (e.g., identifying the users preferences, selecting the products most suited to her, dynamically generating the hypertextual pages). Therefore, we have defined a multiagent architecture where these tasks are executed by different agents, which cooperate offering specific services to each other. In our system, the domain‐dependent knowledge, concerning information about products and customer features, is declaratively represented and clearly separated from the domain‐independent components, which represent the core of the virtual store. This separation has the advantage that our architecture can be easily instantiated on several sales domains, therefore obtaining different Web stores out of a single shell. Our system is developed in a Java‐based environment and the overall architecture includes the prototype of a virtual store and the configuration tools which can be used to set up a new store on a specific sales domain.

On the computational complexity of querying bounds on differences constraints

Abstract Given a consistent knowledge base formed by a set of constraints, efficient query answering (e.g., checking whether a set of constraints is consistent with the knowledge base or necessarily true in it) is practically very important. In the paper we consider bounds on differences (which are an important class of constraints based on linear inequalities) and we analyze the computational complexity of query answering. More specifically, we consider various common types of queries and we prove that if the minimal network produced by constraint satisfaction algorithms (and characterizing the solutions to a set of constraints) is maintained, then the complexity of answering a query depends only on the dimension of the query and not on the dimension of the knowledge base (which is usually much larger than the query). We also analyse how the approach can be used to deal efficiently with a class of updates to the knowledge base. Some applications of the results are sketched in the conclusion.

Extending Temporal Relational Databases to Deal with Imprecise and Qualitative Temporal Information

In several application domains, there is a need for historical databases supporting also imprecise and qualitative temporal information. We propose a formal theory for handling imprecise and qualitative temporal information in a relational temporal database. In particular, we introduce a relational algebra in which the traditional operators are extended in order to deal with imprecise and qualitative time; we examine the properties of the operators, showing that the algebra we introduce is a consistent extension of the snapshot algebra. Finally, we show how a temporal relational database based on this theory can be built on top of a conventional relational database.

UbiquiTO: a Multi-Device Adaptive Guide

This paper describes UbiquiTO, an adaptive tourist guide, conceived as a “journey companion” for mobile users in Turin, aimed, for the current prototype, at supporting mobile workers helping them to organize their late afternoon and evening in town. The paper is intended to emphasize the most relevant feature of the system, that is the integration of different adaptation strategies in order to allow high flexibility in terms of device used, localization technology, user preferences and context conditions.