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In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.

Teacher’s, at all levels, are confronted daily with the need to acquire, refine, and apply their skills in Technology Enhanced Learning. It is since many years that professional development initiatives for teachers, and adoption of technology-based tools, have been undertaken to foster new ways of teaching; yet much is still needed, as the recent needs for distance learning, in countries where the schools and universities were closed for social health reasons, has revealed quite dramatically. In this paper we describe an experiment of use of a Community of Practice (CoP), where teachers of different disciplines participated, with the aim to (1) get in contact with technologies of relatively easy adoption, and (2) interact with colleagues, to discuss with and learn from them. The experiment was conducted within the framework of a EU project aiming to bridge the gap between teachers and pupils in the use of the Web technologies. The CoP was designed based on the foundational Wenger’s concepts of domain, community, and practice. It supported an educational program on Web2.0 educational technologies for Vocational Education teachers. We present a discussion of the social aspects of the CoP dynamics, using evaluation metrics coming from the Social Network Analysis research area.

Wikipedia has become the mostused source of didactic materials with its immense number of Wiki pages,freely available to anyone. Wiki Course Builder is a Web platform designed to help teachers building courses composed by Wikipedia pages only,directly taken by the Wikipedia free encyclopedia.One of the interesting and novel features is the Community module, that provides a collaborative environment, through a suitable Graphic User Interface, where teachers can interact with all the courses in the platform. The Community is a graphical visualization of the Wikipedia pages used by all the teachers in the systems, grouped in clusters, each representing a particular topic. Another characteristic is the information about the Teaching Styles of those teachers that have used that page in their courses. This feature has a twofold advantage. First, each Wikipedia page is tagged with the Teaching Styles. Second, this social aspect gives useful suggestions for possible extensions or changes in the courses. We present a complete use case showing the usefulness of the social Community environment to speed up and improve the course building activity.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others.

Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.

The retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories (\(p < .01\)), and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications.

This paper presents a newborn collaboration between heterogeneous AI competences. In particular, it describes current work on the integration of machine learning techniques for the automatic generation of contents for an Intelligent Tutoring System grounded on automated planning techniques. The joint use of these two approaches allows on the one hand to facilitate the task of instructional designers in defining and preparing courses, and on the other hand to dynamically support the use of content according to different users context.

The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed.

Wikipedia is one of the most used repositories of free learning materials published by communities of experts: most of the students, and teachers do great use of it, despite the criticism about its reliability. Here we address the problem of helping teachers to build courses with Wikipedia pages to prepare on-The-fly courses. We use a web-based system called Wiki Course Builder, which allows teachers to query Wikipedia, by submitting a set of keywords, and having as a response four different ranked lists of retrieved Wikipedia pages, depending each list on a given retrieval metric implemented. In addition to the classic TF-IDF, Information Gain and Latent Semantic Indexing metrics, we propose a metric that highlights specific didactic aspects. This metric is based on the teacher's Teaching Styles namely, the Grasha teaching styles. Wikipedia pages are indexed with the Teaching Styles of the teachers that selected the given page; then, this value is compared with the Teaching Style of the teacher who is doing the search. The metric assigns higher rank to the Wikipedia pages that have a Teaching Style closer to the teachers'. We present an evaluation of the four retrieval metrics, showing that the teacher-styles based metric, Vs. The other three content-based metrics, allows for a better Wikipedia retrieval from a didactic point of view.

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

The public availability of datasets of teaching resources is an issue for the design, development and evaluation of Information Retrieval and Recommender Systems in Technology Enhanced Learning. Recently, the Dataset of Joint Educational Entities (DAJEE) has provided the community with a very exhaustive collection of resources coming from Massive Open Online Courses. This work proposes a representation of the DAJEE dataset according to Linked Data principles, interlinking the large amount of resources in DAJEE with the Web of Data. The transcript of the educational resources in DAJEE have been annotated through a Named Entity Recognition tool in order to create interlinks with the DBpedia entities. The DBpedia knowledge base provides additional information related to categories, that can be exploited to infer new knowledge and support reasoning processes.

The organization of the information in the knowledge economy hasbecome a priority business process. Better organization leads tofaster retrieval of relevant information. The process of searchingand sequencing didactic materials for course building is anarticulated and time-consuming process that requires considerableeffort by the user. The goal of this research is to implement aplatform for supporting the course building from Wikipedia articles. The selected materials will be automatically sequenced on the baseof prerequisite relation, in order to provide a knowledge graph, editable by the teacher, with prerequisite relationships, representing the optimum learning path for the students.

The Knowledge Society is increasing the demand for tools to manage the didactic knowledge stored in Learning Objects Repositories, and needed by teachers to generate courseware. In this respect, still there is a lack of automated tools for the analysis and retrieval of learning resources from such repositories. Here we propose the use of the OLAP technique to help teachers to specify a didactic ontology by which performing quantitative and qualitative analysis of Internet-based Learning Objects Repositories. The related system is presented, together with a case study based on real repositories.

LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, pre-course, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced, based on the initial learner’s Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the student’s navigation, and on the basis of learning assessments, the adaptation algorithm can output a new Learning Objects Sequence, to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LECOMPS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures, the As a Whole and the Layered approaches. The results are encouraging, both for the system on the whole and for the adaptive components.

LS-Plan is a system capable to provide educational hypermedia with adaptivity. During the student’s navigation in hyperspace, the system, by means of a suitable interface, receives in input, for each visited learning node, a behavioral pattern, containing the student’s knowledge acquired or not acquired in that node and measured by a post-test questionnaire. Then, by means of the adaptation algorithms, the system returns a Learning Object Sequence to the hypermedia, just to be recommended to the student. In this work we investigate in detail the adaptation algorithms together with the didactic strategies behind them, through an application to a real learning domain, subdivided in five case studies. Moreover, we show in detail how the automated planner, embedded into the system, allows consistency checks during the arrangement of the pool of Learning Nodes, allowing the teacher to define possible didactic strategies during automatic course personalization. Finally, the performance of the system is evaluated by means of an experimental design with positive results.

Identifying the pre-requisite relationships among learning objects is a crucial step for faculty and instructional designers when they try to adapt them for delivery in their general education distance courses. We propose a general-purpose content-based approach for facilitating this step by means of semantic analysis techniques: the learning objects are associated to WikiPedia pages (topics), and their dependency is obtained using the classification of those topics supported by Wikipedia Miner.

The designing and delivering of a new online course is a crucial task for teachers that have to face two main problems: building, or retrieving, and sequencing learning materials. Retrieving learning materials requires a great effort and a waste of time, while sequencing them requires an accurate didactic project. On the other hand, thanks to the Internet, teachers and instructional designers today can search and retrieve learning materials from Learning Objects Repositories freely available on the Web, such as Mertlot or Ariadne. In this paper we investigate the possibility of using the Wikipedia free encyclopedia, that is the biggest repository of educational material which is visited daily by about sixty million people with its 49 millions of registered people. It is a matter of facts that teachers consult this encyclopedia to arrange, integrate or enrich their courses. So here we propose a system, now at its early stage of development, aiming at supporting teachers to build courses basing on Wikipedia only. The system retrieves learning materials form Wikipedia and sequences them on the basis of the links embedded in the Wikipedia HTML pages, following a course building process based on the Grasha teaching styles and on a social didactic approach. A first questionnaire has been submitted to a sample of teachers with encouraging results.

This paper presents an approach to artificial intelligence planning based on linear temporal logic (LTL). A simple and easy-to-use planning language is described, PDDL-K (Planning Domain Description Language with control Knowledge), which allows one to specify a planning problem together with heuristic information that can be of help for both pruning the search space and finding better quality plans. The semantics of the language is given in terms of a translation into a set of LTL formulae. Planning is then reduced to "executing" the LTL encoding, i.e. to model search in LTL. The feasibility of the approach has been successfully tested by means of the system Pdk, an implementation of the proposed method.

In recent years, there has been a radical change in the world of education and training that is causing that many schools, universities and companies are adopting the most modern technologies, mainly based on Web architectures and Web 2.0 instruments and tools, for learning, managing and sharing of knowledge. In this context, an e-Learning system can reach its maximum potential and effectiveness if it could take advantage of the information in its possession and process it in an intelligent and personalized way. The Educational Data Mining is an emergent field of research where the approach to personalization makes use of the log data generated by learners during their training process, to dynamically update users learning profiles such as skills and learning styles and identify students behavioral patterns. In this paper we present a case study of a data mining approach, based on cluster analysis, in order to support the detection of learning styles in a community of learners, following the Grasha-Riechmann learning styles model. As an e-learning framework we used the Moodle LMS platform and studied the log files generated by a course taken by a community of learners. The first experimental results suggest a connection between clusters and learning styles, reinforcing the use of this approach.

In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student's needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory. In this paper we propose a fine-grained student model, embedded into an Adaptive Educational Hypermedia, LS-Plan provided as plug-in for Moodle. In this way we satisfy the two most important requirements: a fine-grained personalization and a large diffusion. In particular, the substantial modification proposed in this contribution regards the methodology to evaluate the knowledge of the single student which currently has a low granularity level. The experiments showed that the new system has improved the evaluation mechanism by adding information that students and teachers can use to keep track of learning progress.

"The success of training through virtual environments is due both to an interesting cost savings for industry and positive results for learners. However, the system behind this approach is usually hard to produce, being strongly domain-dependent and for this reason difficult to port on other domains. Here we present the design of a domain-independent training system based on simulation environments, capable to be reused in different domains and especially in the industrial ones, saving costs considerably. The main idea behind the system design is the separation between the domain logic and the trainer role. This distinction is achieved by the adoption of a modular and flexible agent-based system approach. We present a first case study for training advanced technicians to learn maintenance procedures for complex medium tension equipments."

Nowadays, teachers tend to build their own didactic local repository composed by learning objects retrieved from web repositories or, in most cases, by self-made didactic material. In this way they do not share their teaching experience, loosing a precious shortcut to a fast professional update and to an improvement of their teaching activity. In this paper we address the problem of helping teachers to retrieve didactic material from a repository through a didactic social network where teachers with similar Teaching Styles, can help each other in retrieving educational material. To this aim a teaching-styles based social network is built following the Grasha TS paradigm. We present a first evaluation of the network embedded in a web application.

"In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standard-compliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context."

"\"Training in industry is one of the most critical and expensive tasks to be faced by the management. Furthermore, in some cases, it is dangerous or even impossible to directly train operators on the real plants where security and safety problems may arise, making it very difficult to start training programs at low cost. For these reasons, the field of training in industry is rapidly developing using software or hardware solutions coming mainly from the following research areas: i) Human-Computer interaction, i.e., the use of complex and interactive human-machine interfaces, ii) plant simulators, i.e., software systems which are delivered with the plant itself to test and to learn complex tasks and processes, iii) Intelligent Training Systems, i.e., the availability of intelligent and personalized training systems where a virtual tutor guides users through a personalized learning path. In this paper we present the overall architecture of a system for industrial training, embedded into an Intelligent Tutoring System that can provide more effective and personalized training and learning in a context where working directly on real plants can be difficult and very expensive. In particular we present a simulator for training operators in using power plants, based on a multimedia and on interactive interface. This system is particularly suitable to be used for training in industrial electric and oil plants. Moreover, the system allows operators for collaborative problem solving. Currently the system is under delivery to an Italian Electric industry.\""

Personalization is becoming a mandatory requirement in Web-based Education and long distance learning in general, representing a flexible way of learning the exact amount of knowledge to reach a given learning goal. This approach saves time and money and it is particularly suited for life-long learning. The drawback is that the teacher has to produce some effort to prepare didactic material and while research in this field proposes several intelligent systems providing personalization with advanced didactic strategies, teacher’s point of view is less considered. In this chapter we extend our previous work that aimed to build an adaptive system for education called LS-Plan, taking into account both teacher’s and student’s needs. In particular we carried out a comprehensive evaluation of the system embedded into an Adaptive Educational Hypermedia called Lecomps5, in order to experiment and prove the added value of the system.

This paper presents an algorithm for evaluating an arithmetic expression over "big" rational numbers. The method exploits p -adic arithmetic and parallelism to achieve efficiency. Roughly, the algorithm begins by mapping the input rational numbers to the related p -adic codes for several prime bases, then the corresponding expression is evaluated over these codes and finally the rational output is recovered from the resulting codes. All these three steps are parallelized. An efficient algorithm to compute the recovery step is proposed. Let p be the maximum of the prime bases for the codes, let r be their truncation order and let k be the number of processors. The asymptotic bit-wise complexity of the proposed recovery step is O ( r ( k log p ) log 2 3 . The previous known bound was O ( r 2 ( k log p ) log 2 3 ). Let n be the number of operations in the given expression: the asymptotic bitwise complexity of the proposed algorithm for evaluating arithmetic expressions is O ( n r 2 log p ).

Personalization and Adaptation are among the main challenges in the field of e-learning, where currently just few Learning Management Systems, mostly experimental ones, support such features. In this work we present an architecture that allows moodle to interact with the Lecomps system, an adaptive learning system developed earlier by our research group, that has been working in a stand-alone modality so far. In particular, the Lecomps responsibilities are circumscribed to the sole production of personalized learning objects sequences and to the management of the student model, leaving to moodle all the rest of the activities for course delivery. The Lecomps system supports the “dynamic” adaptation of learning objects sequences, basing on the student model, i.e., learner’s Cognitive State and Learning Style. Basically, this work integrates two main Lecomps tasks into moodle, to be directly managed by it: Authentication and Quizzes.

This paper presents LS-Plan , a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman's model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student's Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the student's navigation, and on the basis of learning assessments, the adaptation algorithm can propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.

Our work carries on the idea of configuring personalized courses by means of automated planning techniques, preserving coherence with present standards for e-learning, in particular with SCORM properties. Starting from a previous prototype based on suitably defined learning objects, learning components, we intend to make it available in a wider contest. To this aim we design a mapping between our learning component specification and the definition of a SCO, a SCORM 1.2 compliant learning object. In this way we can extend conservatively the usual SCO by enriching it with those elements that are relevant for the process of automated configuration: while the original SCORM properties stay unchanged, we can then make course configuration with a SCORM compliant learning object. In order to obtain such a mapping, the ScormUni tag format has been devised, which is an extension of SCORM meta data. We show here how the above mentioned prototype has evolved into UniTag , a software module that extends Moodle (Version 1.6).

Topics about the integration of the numeric and symbolic computation paradigms are discussed. Mainly an approach through a uniform representation of numbers and symbols is presented, that allows for the application of algebraic algorithms to numeric problems. The p-adic construction is the basis of the unifying representation environment. An integrated version of the Hensel algorithm is presented, which is able to perform symbolic and numeric computations over instances of ground (concrete) and parametric structures, and symbolic computations over instances of abstract structures. Examples are provided to show how the approach outlined and the proposed implementation can treat both cases of symbolic and numeric computations. In the numeric case it is shown that the proposed extension of the Hensel Algorithm can allow for the exact manipulation of numbers. Moreover, such an extension avoids the use of simplification algorithms, since the computed results are already in simplified form.

The "planning as satisfiability" paradigm, which reduces solving a planning problem P to the search of a model of a logical description of P, relies on the assumption that the agent has complete knowledge and control over the world. This work faces the problem of planning in the presence of incomplete information and/or exogenous events, still keeping inside the "planning as satisfiability" paradigm, in the context of linear time logic.We give a logical characterization of a "conditioned model", which represents a plan solving a given problem together with a set of "conditions" that guarantee its executability. During execution, conditions have to be checked by means of sensing actions. When a condition turns out to be false, a different "conditioned plan" must be considered. A whole conditional plan is represented by a set of conditioned models. The interest of splitting a conditional plan into significant sub-parts is due to the heavy computational complexity of conditional planning.The paper presents an extension of the standard tableau calculus for linear time logic, allowing one to extract from a single open branch a conditioned model of the initial set of formulae, i.e. a partial description of a model and a set of conditions U guaranteeing its "executability". As can be expected, if U is required to be minimal, the analysis of a single branch is not sufficient. We show how a global view on the whole tableau can be used to prune U from redundant conditions. In any case, if the calculus is to be used with the aim of producing the whole conditional plan off-line, a complete tableau must be built. On the other hand, a single conditioned model can be used when planning and execution (with sensing actions) are intermingled. In that case, the requirement for minimality can reasonably be relaxed.

The aim of the paper is to show the effectiveness of the p-adic arithmetic in scientific computation by selecting and solving problems which manipulates “big” numbers.

In this paper we propose the use of the p-adic arithmetic as a basic computational tool for a symbolic computation system in the framework of the TASSO project. This arithmetic has been chosen for two main reasons.

Systems for symbolic mathematics are based on the availability of powerful methods and techniques, which have been developed for numeric computation, symbolic and algebraic computation and automated deduction. But those different computing paradigms really work independently in such systems. Each of them represents an individual computing environment, while they are not integrated to support a uniform environment for computation. The problem of the integration of numeric and symbolic computation is still open (Caviness 1986, Limongelli and Miola 1990, Mascari and Miola 1986).

This paper shows the expressive power of the functional programming language Standard ML (SML) in the context of computer algebra. It is focused on a special application of the p-adic lifting technique, the Hensel algorithm, that is utilized in a symbolic but also numeric context. This experiment demonstrates that SML provides a suitable frame for the implementation of abstract algebraic notions together with the possibility to code related algorithms in a generic way on the corresponding level of abstraction.

We describe a parallel implementation of an algorithm for solving systems of linear equations over the field of rational numbers based on Gaussian elimination. The rationals are represented by truncated p-adic expansion. This approach permits us to do error free computations directly over the rationals without converting the system to an equivalent one over the integers. The parallelization is based on a multiple homomorphic image technique and the result is recovered by a parallel version of the Chinese remainder algorithm. Using a MIMD machine, we compare the proposed implementation with the classical modular arithmetic, showing that truncated p-adic arithmetic is a feasible tool for solving systems of linear equations. The proposed implementation leads to a speedup up to seven by ten processors with respect to the sequential implementation.

In this paper we analyze the possibility of speeding up rational number arithmetic by using a parallel p-adic approach.

This paper presents several methodological and technological aspects for the treatment of mathematical objects by very high level abstract specification of data and methods. A homogeneus environment is defined for the design and implementation at the highest abstract level. The presentation is developed from the point of view of both formal specification and implementation languages. Indeed specification and programming are considered as uniform actions under the same general conceptual model. The conceptual model is completely supported by the Object-Oriented methodology. Particular interest is devoted to Algorithmic Logic, as a logic specification language, and to Loglan as an innovative Object-Oriented programming language.

In the past years, great attention has been payed to approximated p-adic arithmetic expressed in the form of Hensel codes and several contributions have made this p-adic arithmetic really effective, just according to this new consideration of it. However it has been shown that the appliability of p-adic arithmetic is strongly constrained by the size of rational numbers which constitutes the output of the given computation. The key idea we like to present here, it is based on the intention of getting any advantage out the variation of the prime p, choosing it as a very large number. This choise suggests a definition of a new algorithm which will benefit from a parallel execution. Thus our aim is to perform “big” rational numbers arithmetic applying the so called g-adic approach, based on the theory of g-adic numbers. This paper describes a general schema of g-adic computation and then presents two algorithms to perform the inverse mapping together with the related complexity analysis.

The “planning as satisfiability” approach for classical planning establishes a correspondence between planning problems and logical theories, and, consequently, between plans and models. This work proposes a similar framework for contingency planning: considering contingent planning problems where the sources of indeterminism are incomplete knowledge about the initial state, non-inertial fluents and non-deterministic actions, it shows how to encode such problems into Linear Time Logic. Exploiting the semantics of the logic, and the notion of conditioned model introduced in this work, a formal characterization is given of the notion of contingent plan (a plan together with the set of conditions that ensure its executability).