Abstract: User profiling on the Web is a topic that has attracted a great number of technological approaches and applications. In most user profiling approaches the website learns profiles from data implicitly acquired from user behaviours, i.e. observing the behaviours of users with a statistically significant number of accesses. This paper presents an alternative approach. In this approach the website explicitly acquires data from users, user interests are represented in a Bayesian network, and user profiles are enriched and refined over time. The profile enrichment is achieved through a sequential asking algorithm based on the value‐of‐information theory using the Shannon entropy concept. However, what mostly characterizes the approach is the fact that the user is involved in a collaborative process of profile building. The approach has been tried out for over a year in a real application. On the basis of the experimental results the approach turns out to be particularly suitable for applications where the website is strongly based on deep domain knowledge (as for example is the case for scientific websites) and has a community of users that share the same domain knowledge of the website and produce a ‘low’ number of accesses (‘low’ compared to the high number of accesses of a typical commercial website). After presenting the technical aspects of the approach, we discuss the underlying ideas in the light of the experimental results and the literature on human–computer interaction and user profiling.

Abstract: The paper presents a methodology for building sequential decision support systems based on decision theory using value of information (for short, DT‐VOI based SDSSs). DT‐VOI based SDSSs support decision‐makers in difficult problems of sequential decision‐making. In particular we consider the problem of building DT‐VOI based SDSSs which are capable of supporting decisions in critical situations where (1) making a decision entails knowing the states of some critical hypotheses, and such knowledge is acquired by performing suitable tests; (2) test outcomes are uncertain; (3) performing a test entails, in general, some drawbacks, so that a trade‐off exists between such drawbacks and the value of the information provided by the test; (4) performing a test has the side‐effect that it changes the expected benefit from performing other tests; (5) exceptional situations alter probability and utility default values.

A formidable synergy can be obtained by putting expert system technology into the Internet. The modern trend of embedding expert systems into Websites turns out to be very promising, in particular for the field of marketing via the Web. The last two years have seen a growing interest in providing Websites with suitable embedded expert systems for one–to–one marketing. One–to– one marketing means marketing in a personalized way, i.e. marketing in a way that is adaptive to the personal needs of the user. A basic feature of this marketing framework consists in personalized prioritizing of news, i.e. presenting information in an order that is relevant to the specific needs of the current user. If the personalized prioritizing of news is a very useful feature in wired Web, it becomes essential in wireless Web, the promising next generation of the Web. The paper presents a general methodology for personalized prioritizing of news. The methodology integrates decision theory with a deep–knowledge–based user model (i.e. causal knowledge linking user preferences to user goals). The deep–knowledge model of the user is a source of power of the methodology because it allows the system to know (and possibly explain) why the user acts the way he/she acts. Another relevant aspect of the methodology is that the burden of personalization is not placed on the user, and in fact the user does not have to declare his/her needs or interests or goals: they are automatically inferred from his/her profile data. In order to investigate the ideas underlying the proposal, a methodology example has been implemented in a prototype and then tested on real cases in the context of a supercomputing portal.

Sequential decision models are an important component of expert systems since, in general, the cost of acquiring information is significant and there is a trade‐off between the cost and the value of information. Many expert systems in various domains (business, engineering, medicine etc.), needing costly inputs that are not known until the system operates, have to face this problem. In the last decade the field of sequential decision models based on decision theory (sequential decision‐theoretic models) have become more and more important due to both the continuous progress made by research in Bayesian networks and the availability of modern powerful tools for building Bayesian networks and for probability propagation. This paper provides readers (especially knowledge engineers and expert system designers) with a unified and integrated presentation of the disparate literature in the field of sequential decision‐making based on decision theory, in order to improve comprehensibility and accessibility. Besides the presentation of the general theory, a view of sequential diagnosis as an instance of the general concept of sequential decision‐theoretic models is also shown.

An intelligent diagnostic support system (I‐DSS) for decision‐making support in diagnostic processes is presented. I‐DSS can be placed between diagnosis carried out by a human diagnostician, without any automatic support, and diagnosis carried out in a fully automatic way. Fully automatic diagnosis may be appealing if used in very complex domains and if the user is non‐expert. However, in the case of an expert user, a fully automatic approach is not suitable. In the fully automatic approach the system should be equipped with a strategic knowledge base (the knowledge needed for making the ’best’ choice) and as a consequence the expert user is prevented from making decisions on the basis of his or her own experience. This restriction causes, in general, a sort of psychological rejection, on the part of the expert user, of the traditional fully automatic approach. This is particularly true in those domains, such as medicine, where there is more than one approach to the solution and it is seldom that one approach can be considered ’right’ and the others ’wrong’. Experience related to diagnostic expert systems applications shows that, whenever trade‐off problems arise in choosing between alternative actions, it is preferable to leave decisions to the expert. Starting from these considerations we present a system (I‐DSS) which, without being ’intrusive’, aims to be an effective support for the decision‐maker during the diagnostic process.

The paper presents a method for helping knowledge engineers in modelling the knowledge involved in sequential diagnosis. In particular, we consider sequential diagnosis as a process which occurs in the following type of scenario: (1) there exist some candidate hypotheses which are to be pursued; (2) for each hypothesis there are some alternative tests which might be executed to pursue it; (3) the available knowledge about the world precludes projecting into the future (in other words, the available knowledge about the world does not afford the information needed for planning sequences of tests, i.e. for projecting the consequences of possible sequences of actions and picking the best sequence); (4) the choice of the next test (or tests) is made on the basis of the current state of knowledge; (5) the outcome of a test execution is affected by uncertainty. The suggested method addresses the problem of engineering the knowledge experts use for making decisions under uncertainty. A practical example of the method is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him or her in the decision‐making problem about which test to execute next.

In general, expert system applications to real cases involve making decisions, i.e. selecting a suitable action among a set of possible alternative actions. A well‐known standard method for modelling decision problems is the so‐called multi‐attribute utility theory (MAUT), a method in which the alternatives are viewed in terms of their attributes. A set of attributes are identified and a specific value and a suitable relative importance weight are assigned to each attribute. However, it is not easy for the expert to quantify the relative importance weight of an attribute: this assignment entails a certain abstraction activity from the expert and, as is well known, experts have difficulty in providing their knowledge in abstract and general terms. In order to overcome this difficulty we propose a method for automatically inferring relative importance weights from a set of specific action sequences. An action sequence is a list in chronological order of the actions executed by the expert when facing specific cases of decision problems. Providing action sequences requires no other effort but remembering specific episodes, and this task is much easier for experts than having to directly provide precise numbers expressing relative importance weights. Moreover in many cases action sequences are already stored in suitable records. Consider, for example, the list of medical tests executed on a given patient, a list included in the patient clinical record stored in the clinical database of a hospital. On the basis of these considerations the proposed method should be useful for designers of expert systems which face problems of choosing the right action among a set of alternative actions.

Uncertainty calculus overcomes many of the drawbacks affecting other well-known approaches to uncertain reasoning. Nevertheless, it suffers from other severe limitations and gives rise to counter-intuitive results. We show that common sense inconsistencies of uncertainty calculus originate from a scarce cognitive plausibility in the definition of propagation and aggregation operators, and we propose an improved version of Driankov's uncertainty calculus (1986) where the definition of such operators is substantially revised according to common sense criteria. Revised uncertainty calculus allows overcoming in a general and uniform way all the problems identified in uncertainty calculus and provides a substantial enhancement of cognitive plausibility. It has been successfully tested in the design and development of a real application concerning preventive diagnosis of power transformers.

The paper proposes a model for making the most opportune choice among a number of alternatives in situations involving trade-offs and uncertainty. Alternative actions are evaluated on the basis of strategic attributes, such as cost and effectiveness, and the choice is dynamically determined. The issue is addressed by proposing a set of rules (trade-off rules) representing strategic knowledge specifically oriented to solve trade-off problems. Trade-off rules are encoded in conditional sentences such as: "if P then Ci is preferred to Cj", where P specifies a condition that must be satisfied by the strategic attributes associated with two candidate alternatives, and Ci and Cj are general selection criteria such as "select alternative with the minimum (or maximum) value of a specific attribute ATTR". In the first part of the paper, a deep analysis of various trade-off problems and various interdependencies among choices is presented. The second part proposes a general paradigm for strategic reasoning under trade-offs. The paradigm is evaluated using a diagnostic application in the medical domain. Finally, the proposal is discussed in the context of related work.

The paper presents an approach to causal knowledge elicitation supported by a tool directly used by the domain expert. This knowledge elicitation approach is characterized by trying to guess an interpretation of the knowledge entered by the expert. The tool (initially general), as it is used, self customizes its guessing capability, remembers failures in guessing (in order to avoid similar failures in the future) and when they occur elicits their explanations. Even in this case, elicitation is supported by guessing on the basis of previous similar failures. The resulting overall effect is that the tool digs up tenaciously causal knowledge from the expert's mind, playing in this way a cooperative role for model building.>

Strategic common sense is defined as the heuristic considerations human experts often apply to decide which choice is the most opportune to make from a set of possible alternatives. A method for acquiring modeling, and representing human strategic common sense in diagnostic expert systems is presented. The preference parameter and preference criterion tools, which are used to define a basic level of strategic knowledge, are reviewed. Strategic common sense and its representation using general rules and metarules modeling are discussed. An interview with a physician who provided a medical case and the related solutions is summarized. It is shown that the solutions given by the physician match the conclusions obtained by applying the strategic common sense rules to the medical example.>