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In this paper, we advance a novel approach to the problem of autonomous robot navigation. The environment is a complex indoor scene with very little a priori knowledge, and the navigation task is expressed in terms of natural language directives referring to natural features of the environment itself. The system is able to analyze digital images obtained by applying a sensor fusion algorithm to ultrasonic sensor readings. Such images are classified in different categories using a case-based approach. The architecture we propose relies on fuzzy theory for the construction of digital images, and wavelet functions for their representation and analysis. [ABSTRACT FROM AUTHOR]

Computer and network security is an extremely active and productive research area. Scientists from all over the world address the pertaining issues, using different types of models and methods. In this article we illustrate a case-based approach where the normal user-computer interaction is read like snapshots regarding a reduced number of instances of the same application, attack-free and sufficiently different from each other. The generic case representation is obtained by interpreting in numeric form the arguments and parameters of system calls deemed potentially dangerous. The similarity measure between a new input case and the ones stored in the case library is achieved through the calculation of the Earth Mover's Distance between the corresponding feature distributions, obtained by means of cluster analysis. [ABSTRACT FROM AUTHOR]

The large amount of available information on the Web makes it hard for users to locate resources about particular topics of interest. Traditional search tools, e.g., search engines, do not always successfully cope with this problem, that is, helping users to seek the right information. In the personalized search domain, focused crawlers are receiving increasing attention, as a well-founded alternative to search the Web. Unlike a standard crawler, which traverses the Web downloading all the documents it comes across, a focused crawler is developed to retrieve documents related to a given topic of interest, reducing the network and computational resources. This chapter presents an overview of the focused crawling domain and, in particular, of the approaches that include a sort of adaptivity. That feature makes it possible to change the system behavior according to the particular environment and its relationships with the given input parameters during the search. [ABSTRACT FROM AUTHOR]

With the exponential growth of the available information on the World Wide Web, a traditional search engine, even if based on sophisticated document indexing algorithms, has difficulty meeting efficiency and effectiveness performance demanded by users searching for relevant information. Users surfing the Web in search of resources to satisfy their information needs have less and less time and patience to formulate queries, wait for the results and sift through them. Consequently, it is vital in many applications - for example in an e-commerce Web site or in a scientific one - for the search system to find the right information very quickly. Personalized Web environments that build models of short-term and long-term user needs based on user actions, browsed documents or past queries are playing an increasingly crucial role: they form a winning combination, able to satisfy the user better than unpersonalized search engines based on traditional Information Retrieval (IR) techniques. Several important user personalization approaches and techniques developed for the Web search domain are illustrated in this chapter, along with examples of real systems currently being used on the Internet. [ABSTRACT FROM AUTHOR]

A very common issue of adaptive Web-Based systems is the modeling of documents. Such documents represent domain-specific information for a number of purposes. Application areas such as Information Search, Focused Crawling and Content Adaptation (among many others) benefit from several techniques and approaches to model documents effectively. For example, a document usually needs preliminary processing in order to obtain the relevant information in an effective and useful format, so as to be automatically processed by the system. The objective of this chapter is to support other chapters, providing a basic overview of the most common and useful techniques and approaches related with document modeling. This chapter describes high-level techniques to model Web documents, such as the Vector Space Model and a number of AI approaches, such as Semantic Networks, Neural Networks and Bayesian Networks. This chapter is not meant to act as a substitute of more comprehensive discussions about the topics presented. Rather, it provides a brief and informal introduction to the main concepts of document modeling, also focusing on the systems that are presented in the rest of the book as concrete examples of the related concepts. [ABSTRACT FROM AUTHOR]

The architecture herein advanced finds its rationale in the visual interpretation of data obtained from monitoring computers and computer networks with the objective of detecting security violations. This new outlook on the problem may offer new and unprecedented techniques for intrusion detection which take advantage of algorithmic tools drawn from the realm of image processing and computer vision. In the system we propose, the normal interaction between users and network configuration is represented in the form of snapshots that refer to a limited number of attack-free instances of different applications. Based on the representations generated in this way, a library is built which is managed according to a case-based approach. The comparison between the query snapshot and those recorded in the system database is performed by computing the Earth Mover΄s Distance between the corresponding feature distributions obtained through cluster analysis. [ABSTRACT FROM AUTHOR]

Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed by using a set of manually classified documents, i.e. a training collection. Term-based representation of documents has found widespread use in TC. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we design, implement, and evaluate a new text classification technique. Our main idea consists in finding a series of projections of the training data by using a new, modified LSI algorithm, projecting all training instances to the low-dimensional subspace found in the previous step, and finally inducing a binary search on the projected low-dimensional data. Our conclusion is that, with all its simplicity and efficiency, our approach is comparable to SVM accuracy on classification. [ABSTRACT FROM AUTHOR]

The Web has grown from a simple hypertext system for research labs to an ubiquitous information system including virtually all human knowledge, e.g., movies, images, music, documents, etc. The traditional browsing activity seems to be often inadequate to locate information satisfying the user needs. Even search engines, based on the Information Retrieval approach, with their huge indexes show many drawbacks, which force users to sift through long lists of results or reformulate queries several times. Recently, an important research activity effort has been focusing on this vast amount of machine-accessible knowledge and on how it can be exploited in order to match the user needs. The personalization and adaptation of the human-computer interaction in information seeking by means of machine learning techniques and in AI-based representations of the information help users to address the overload problem. This chapter illustrates the most important approaches proposed to personalize the access to information, in terms of gathering resources related to given topics of interest and ranking them as a function of the current user needs and activities, as well as examples of prototypes and Web systems. [ABSTRACT FROM AUTHOR]

This paper discusses the automatic generation of programs by adapting the construction process to the user currently interacting with the program. A class of such systems is investigated where such generation process is continuously repeated making the program design and implementation evolve according to user behaviour. By leveraging on existing technologies (software generation facilities, modelling languages, specific and general standard metamodels) an experimental proof of concept system that is able to generate itself while interacting with the user is introduced and tested. The findings are discussed and a general organization for this class of adaptive systems is briefly proposed and compared with existing literature. [ABSTRACT FROM AUTHOR]

In this paper we present a case-based approach we have used for the realization of HUMOS, a hybrid user modeling shell system that has been designed for building and maintaining long term models of individual Internet users. A distinguished feature of HUMOS is the hybrid approach to user modeling, where a sub-symbolic module is integrated into a case-based reasoner. A case study in information filtering systems on the World Wide Web is also presented. The described system, named WIFS, is based on HUMOS and is capable of selecting HTML/text documents, collected from the Web, according to the interests and characteristics of the user. Presently the system acts as an intelligent interface for the Web search engines. The experimental results we have obtained are encouraging and support the choice of the case-based approach to user modeling for adaptive systems. [ABSTRACT FROM AUTHOR]