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  1. Paolo Avesani; Francesco Ricci; R. De Col; F. Dalmaso; E. Santuliana,
    Requisiti per un sistema di ausilio alla definizione dei piani di emergenza per calamità naturali,
    In questo documento si riassumono le motivazioni che hanno guidato il Dipartimento di protezione civile della Provincia Autonoma di Trento a promuovere le tecnologie dell'informazione nella gestione dei piani di emergenza. In seguito ad un lavoro di analisi che ricostruisce il contesto della definizione dei piani di emergenza comunali, sono formulati i requisiti di un sistema informativo dedicato alla gestione del rischio idrogeologico,
  2. Francesco Ricci; Paolo Avesani,
    Apprendimento di metriche di similarità locali ed applicazioni alla gestione di incendi boschivi,
  3. Francesco Ricci; Paolo Avesani,
    Exact Learning and Data Compression with a Local Asymmetrically Weighted Metric,
    Worshop on Learning in Context-Sensitive Domains [ICML-96],
  4. Francesco Ricci; Paolo Avesani,
    Nearest Neighbor Classifaction with a Local Asymmetrically Weighted Metric,
    This paper introduces a new local asymmetric weighting scheme for the nearest neighbor classification algorithm. It is shown both with theoretical arguments and computer experiments that good compression rates can be achieved outperforming the accuracy of the standard nearest neighbor classification algorithm and obtaining almost the same accuracy as the k-NN algorithm with k optimised in each data set. The improvement in time performance is proportional to the compression rate and in general it depends on the data set. The comparison of the classification accuracy of the proposed algorithm with a local symmetrically weighted metric and with a global metric strongly shows that the proposed scheme is to be preferred,
  5. Paolo Avesani; Francesco Ricci; Anna Perini,
    Combining Human Assessment and Reasoning Aids for Decision-Making in Planning forest Fire Fighting,
    Proceedings of the IJCAI 95 Workshop 'Artificial Intelligence and the Environment',
    , pp. 71-
  6. Francesco Ricci; Paolo Avesani,
    Learning a Local Similarity Metric for Case-Based Reasoning,
    Proceedings of the the First International Conference [ICCBR-95] on Case-Based Reasoning Research and Development,
    , pp. 301-
  7. Francesco Ricci; Paolo Avesani,
    Learning an Asymmetric and Anisotropic Similarity Metric for Case-Based Reasoning,
    This paper presents a new class of local similarity metrics, called AASM, that are not symmetric and that can be adopted as basic retrieval method in a CBR system. We also introduce an anytime learning procedure that starting from an initial set of stored cases improves the retrieval accuracy by modifying the local definition of the metric. The learning procedure is an implementation of the reinforcement learning paradigm and can be run as a black box as no particular setting is required. We show with the aid of classical test sets tat AASM cab improve in many cases the accuracy of both nearest Neighbour methods and Salzberg’s NGE. Moreover AASM cab achieve significant data compression (10%) still maintaining the same accuracy of NN,
  8. Paolo Avesani; Anna Perini; Francesco Ricci; M. Rencelj,
    The IP Subsystem - RR50B Charade Restricted Report,
  9. F. Ricci;A. Perini;P. Avesani,
    Building First Intervention Plans: the forest fire case,
    , (the 7-th Workshop on Artificial Intelligence Research in Environmental Science,
    Biloxi, Mississipi,
    11/14/1994, 11/17/1994)
  10. A. Perini;F. Ricci;P. Avesani,
    Temporal reasoning and interactive planning,
    , (Workshop on Temporal Reasoning, AI*IA,
    Parma, Italy,