We present an environment for logic programming languages called Toulouse Inference Machine (TIM). Its meta-level architecture permits the user to define how to compute a new goal from a given one. Our aim is to define a frame as general as possible for creating extensions of Prolog and, in particular, to provide a general methodology to implement non-classical logics. There are three basic assumptions on which our frame is built: first, to keep as a base the fundamental logic programming mechanisms that are backward chaining, depth first strategy, backtracking, and unification; second, to parametrize the inference step, and finally, to select clauses "by hand". Applications in logic programming and, in particular, in non-classic logic programming are presented: we specify with a few TIM inference rules various extensions of Prolog by non-classical concepts which have been proposed in the literature.
Current implementations of intrusion detection systems (IDSs) have two drawbacks: 1) they normally generate far too many false positives, overloading human operators to such an extent that they can not respond effectively to the real alerts; 2) depending on the proportion of genuine attacks within the total network traffic, an IDS may never be effective. One approach to overcoming these obstacles is to correlate information from a wide variety of networks sensors, not just IDSs, in order to obtain a more complete picture on which to base decisions as to whether alerted events represent malicious activity or not. The challenge in such an analysis is the generation of the correlation rules that are to be used. At present, creating these rules is a time consuming manual task that requires expert knowledge. This work describes how data mining, specifically the k-means clustering technique, can be employed to assist in the semi-automatic generation of such correlation rules.
Como é possível que a partir da negação do racional (isto é, do colapso na representação do conhecimento, dado pela presença de informações contraditórias) se possa obter conhecimento adicional? Esse problema, além de seu interesse intrínseco, adquire uma relevância adicional quando o encontramos na representação do conhecimento em bases de dados e raciocínio automático, por exemplo. Nesse caso, diversas tentativas de tratamento têm sido propostas, como as lógicas não-monotônicas, as lógicas que tentam formalizar a ideia do raciocínio por falha (default). Tais tentativas de solução, porém, são falhas e incompletas; proponho que uma solução possível seria formular uma lógica do irracional, que oferecesse um modelo para o raciocínio permitindo não só suportar contradições, como conseguir obter conhecimento, a partir de tais situações. A intuição subjacente à formulação de tal lógica são as lógicas paraconsistentes de da Costa, mas com uma teoria da dedução diferente e uma semântica completamente distinta (à qual me refiro como "semântica de traduções possíveis"). Tal proposta, como pretendo argumentar, fornece um enfoque para a questão que é ao mesmo tempo completamente satisfatório, aplicável do ponto de vista prático e aceitável do ponto de vista filosófico.
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