Negative information plays an important role in the way we express our preferences and desires. However, it has not received the same attention as positive feedback in recommender systems. Here we show how negative user preferences can be exploited to generate recommendations. We rely on a logical semantics for the recommendation process introduced in a previous paper and this allows us to single out three main conceptual approaches, as well as a set of variations, for dealing with negative user preferences. The formal framework provides a common ground for analysis and comparison. In addition, we show how existing approaches to recommendation correspond to alternatives in our framework.

This paper describes a novel methodology to evaluate a social media application in its formative phase of design. Taking advantage of the experiences developed in the Alternate Reality Games, we propose to insert game mechanics in the test setting of a formative evaluation of a prototypical social system. As a use case, we present the evaluation of WantEat, a prototypical social mobile application in the gastronomical domain. The evaluation highlighted how the gamification of a field trial can yield good results when evaluating social applications in prototypical status. From a methodological point of view, gamifying a field trial overcomes the cold start problem, caused by the absence of active communities, which can prevent the participation of users and therefore the collection of reliable data. Our experience showed that the gamification of a field evaluation is feasible and can likely increase the quantity of both browsing actions and social actions performed by users. Based on these results, we then are able to provide a set of guidelines to gamify the evaluation session of an interactive system.

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.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

The automatic generation of decision trees based on off-line reasoning on models of a domain is a reasonable compromise between the advantages of using a model-based approach in technical domains and the constraints imposed by embedded applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal decision tree, which is designed to make use of relevant information as long as it is acquired, and we present an algorithm for compiling such trees from a model-based reasoning system.

Generating abductive explanations is the basis of several problem solving activities such as diagnosis, planning, and interpretation. Temporal abduction means generating explanations that do not only account for the presence of observations, but also for temporal information on them, based on temporal knowledge in the domain theory. We focus on the case where such a theory contains temporal constraints that are required to be consistent with temporal information on observations. Our aim is to propose efficient algorithms for computing temporal abductive explanations. Temporal constraints in the theory and in the observations can be used actively by an abductive reasoner in order to prune inconsistent candidate explanations at an early stage during their generation. However, checking temporal constraint satisfaction frequently generates some overhead. We analyze two incremental ways of making this process efficient. First we show how, using a specific class of temporal constraints (which is expressive enough for many applications), such an overhead can be reduced significantly, yet preserving a full pruning power. In general, the approach does not affect the asymptotic complexity of the problem, but it provides significant advantages in practical cases. We also show that, for some special classes of theories, the asymptotic complexity is also reduced. We then show how, compiled knowledge based on temporal information, can be used to further improve the computation, thus, extending to the temporal framework previous results in the case of atemporal abduction. The paper provides both analytic and experimental evaluations of the computational advantages provided by our approaches.

Many different applications in different areas need to deal with both: databases, in order to take into account large amounts of structured data; and quantitative and qualitative temporal constraints about such data. We propose an approach that extends: temporal databases and artificial intelligence temporal reasoning techniques and integrate them in order to face such a need. Regarding temporal reasoning, we consider some results that we proved recently about efficient query answering in the Simple Temporal Problem framework and we extend them in order to deal with partitioned sets of constraints and to support relational database operations. Regarding databases, we extend the relational model in order to consider also qualitative and quantitative temporal constraints both in the data (data expressiveness) and in the queries (query expressiveness). We then propose a modular architecture integrating a relational database with a temporal reasoner. We also consider classes of applications that fit into our approach and consider patient management in a hospital as an example.

Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. We show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational (i.e., easily evaluated) conditions that embed the abductive reasoning strategy and are used in addition to the original model, with the goal of ruling out parts of the search space or focusing on parts of it. The conditions are useful to solve most cases using less time for computing the same solutions, yet preserving all the power of the model-based system for dealing with multiple faults and explaining the solutions. Experimental results showing the advantages of the approach are presented.

An approach to the synthesis and use of operational knowledge in diagnostic problem solving is proposed. The approach significantly departs from previous approaches to knowledge compilation in the sense that the authors do not aim at compiling an autonomous heuristic problem solver from a deep one but only at deriving a set of conditions whose evaluation can focus and speed up diagnostic reasoning on casual models. In particular, it is argued that operational necessary conditions can be used to focus causal diagnostic reasoning by pruning significant portions of the search space to be considered. The process for synthesizing operational knowledge is performed by running a case-independent simulation on a casual model using constraint propagation techniques. This is a major difference with respect to approaches based on the use of examples. Many of the problems arising in the other approaches to the synthesis of heuristics from deep knowledge are solved in this system.>

In this paper, we analyze the logical definitions of model‐based diagnosis recently presented in the literature, and we propose a unified framework (based on the integration of abductive and consistency‐based reasoning) in which most of such definitions can be captured. This allows us to single out the existence of a spectrum of alternatives in the logical definition of diagnosis. A lot of attention in the paper is devoted to analyzing the differences among the definitions in the spectrum. In particular, we show that the definitions can be compared on the basis of their restrictive‐ness and we relate such a restrictiveness with the completeness of the model of the system to be diagnosed. Dans cet article, les auteurs analysent les définitions logiques de diagnostics basés sur un modèle dont il a été question récemment dans certains ouvrages. Ils proposent un cadre unifié (basé sur l'intégration du raisonnement abductif et du raisonnement basé sur la consistance) à l'interieur duquel la plupart de ces définitions peuvent ětre regroupées. Cette particularité permet de mettre en lumière l'existence d'un spectre d'alternatives dans la définition logique du diagnostic. Cet article accorde une attention toute particulière à l'analyse des différences entre les définitions du spectre. En outre, les auteurs démontrent que les définitions peuvent ětre compareées en fonction de leur caractère restrictif, qui est ensuite mis en relation avec la complétude du modèle du système faisant l'objet d'un diagnostic.

The aim of this paper is to analyse from various points of view the relationships between abduction and deduction. In particular, we consider a meta-level definition of abduction in terms of deduction, similar to various definitions proposed in the literature, and an object-level definition in which abductive conclusions are expressed as a logicalconsequence of the observations and of a simple transformation of the domain theory based on predicate completion. The equivalence between the two definitions is proved for domain theories of considerable expressive power. The object-level characterization we propose uses very simple forms of reasoning and the equivalence result allows us to make explicit some of the assumptions underlying meta-level definitions of abduction. The use of predicate completion in characterizing abductive explanations shows a relation between abduction and foundations of logic programming.