Pietro Torasso

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-

User Modeling and Recommendation Techniques for Personalized Electronic Program Guides

This chapter presents the recommendation techniques applied in Personal Program Guide (PPG). This is a system generating personalized Electronic Program Guides for Digital TV. The PPG manages a user model that stores the estimates of the individual user’s preferences for TV program categories. This model results from the integration of different preference acquisition modules that handle explicit user preferences, stereotypical information about TV viewers, and information about the user’s viewing behavior. The observation of the individual viewing behavior is particularly easy because the PPG runs on the set-top box and is deeply integrated with the TV playing and the video recording services offered by that type of device.

Syntax and semantics in a word-sequence recognition system

A recognizer of meaningful sequences of isolated words spoken in the Italian language is presented. The system has been designed in a modular fashion and the current version has been obtained by adding the syntactic and semantic levels to the isolated-word recognizer previously developed by the same authors. The lexical, syntactic and semantic levels are integrated into a hierarchical knowledge source represented by a transition network grammar. The parsing is directed by the control unit and is performed by means of a combined strategy of bottom-up and top-down techniques, using a modified version of the Earley algorithm. Lexical hypotheses are verified at the phonemic level assigning them a score based on a distance measure evaluated by an error-correcting parsing algorithm. The protocol used in the preliminary experi - ments is a robot command language.

Evidence combination in expert systems

This paper discusses some of the problems related to the representation of uncertain knowledge and to the combination of evidence degrees in rule-based expert systems. Some of the methods proposed in the literature are briefly analysed with particular attention to the Subjective Bayesian Probability (used in PROSPECTOR) and the Confirmation Theory adopted in MYCIN. The paper presents an integrated approach based on Possibility Theory for evaluating the degree of match between the set of conditions occurring in the antecedent of a production rule and the input data, for combining the evidence degree of a fact with the strength of implication of a rule and for combining evidence degrees coming from different pieces of knowledge. The semantics of the logical operators AND and OR in possibility theory and in our approach are compared. Finally, the definitions of some quantifiers like AT LEAST n, AT MOST n, EXACTLY n are introduced.

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.

Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups

This paper describes the recommendation techniques exploited in INTRIGUE (INteractive TouRist Information GUidE), an adaptive recommender system that supports the organization of guided tours. This system recommends the places to visit by taking into account the characteristics of the group of participants and addressing the possibly conflicting preferences within the group. A group model is exploited to separately manage the preferences of heterogeneous subgroups of people and combine them, in order to identify solutions satisfactory for the group as a whole.

Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.

Personalized Recommendation of TV Programs

This paper presents the recommendation techniques applied in Personal Program Guide (PPG), a system generating personalized Electronic Program Guides for digital TV. The PPG recommends TV programs by relying on the integration of heterogeneous user modeling techniques.

ADAPtER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning

The aim of this paper is to describe the ADAPtER system, a diagnostic architecture combining case-based reasoning with abductive reasoning and exploiting the adaptation of the solution of old episodes, in order to focus the reasoning process. Domain knowledge is represented via a logical model and basic mechanisms, based on abductive reasoning with consistency constraints, have been defined for solving complex diagnostic problems involving multiple faults. The model-based component has been supplemented with a case memory and adaptation mechanisms have been developed, in order to make the diagnostic system able to exploit past experience in solving new cases. A heuristic function is proposed, able to rank the solutions associated to retrieved cases with respect to the adaptation effort needed to transform such solutions into possible solutions for the current case. We will discuss some preliminary experiments showing the validity of the above heuristic and the convenience of solving a new case by adapting a retrieved solution rather than solving the new problem from scratch.