1,721,185 research outputs found
Introduzione all'intelligenza artificiale
No full textL'articolo presenta un'introduzione all'Intelligenza Artificiale (IA) in forma divulgativa e informale ma precisa. L'articolo affronta prevalentemente gli aspetti informatici della disciplina, presentando le principali tecniche usate nei sistemi di IA divise in simboliche e subsimboliche. L'ultima parte dell'articolo presenta il dibattito in corso tra gli esperi e il pubblico su vantaggi e svantaggi dell'IA e in particolare sui possibili pericoli. L'articolo termina con l'opinione dell'autore al riguardo.The paper presents an introduction to Artificial Intelligence (AI) in an accessible and informal but precise form. The paper focuses on the algorithmic aspects of the discipline, presenting the main techniques used in AI systems groped in symbolic and subsymbolic. The last part of the paper is devoted to the discussion ongoing among experts in the field and the public at large about on the advantages and disadvantages of AI and in particular on the possible dangers. The personal opinion of the author on this subject concludes the paper
Associate Editor della sezione Frontiers in Computational Intelligence della rivista Frontiers in Robotics and AI
No full textAbstract della tesi di dottorato: Estensioni del linguaggio di rappresentazione della programmazione logica induttiva
No full textQuesta tesi si occupa dello studio dell'estensione del linguaggio di rappresentazione della
Programmazione Logica Induttiva. La Programmazione
Logica Induttiva (Inductive Logic Programming, ILP nel seguito) è un'area
di ricerca che si occupa dell'apprendimento di programmi logici partendo
da una conoscenza a priori nella forma di un programma
logico e da esempi nella forma di fatti ground. Sono state considerati tre diverse estensioni della
programmazione logica: programmi logici abduttivi, programmi logici multi-teoria e programmi
logici con negazione esplicita
The Distribution Semantics for Normal Programs with Function Symbols
No full textTThe distribution semantics integrates logic programming and probability theory using a possible worlds approach. Its intuitiveness and simplicity have made it the most widely used semantics for probabilistic logic programming, with successful applications in many domains. When the program has function symbols, the semantics was defined for special cases: either the program has to be definite or the queries must have a finite number of finite explanations. In this paper we show that it is possible to define the semantics for all programs. We also show that this definition coincides with that of Sato and Kameya on positive programs. Moreover, we highlight possible approaches for inference, both exact and approximate
A Top Down Interpreter for LPAD and CP-logic
No full textLogic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming. The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages.
The algorithm is based on the one available for ProbLog.
The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that the added expressiveness effectively requires more computation resources
Extended Semantics and Inference for the Independent Choice Logic
No full textThe Independent Choice Logic (ICL) is a language for expressing
probabilistic information in logic programming that adopts a distribution
semantics: an ICL theory defines a distribution over a set of possible worlds
that are normal logic programs. The probability of a query is then given by the
sum of the probabilities of worlds where the query is true.
The ICL semantics requires the theories to be acyclic. This is a strong
limitation that rules out many interesting programs.
In this paper we present an extension of the ICL semantics that allows theories
to be modularly acyclic.
Inference with ICL can be performed with the Cilog2 system that computes
explanations to queries and then makes them mutually incompatible by means of
an iterative algorithm.
We propose the system PICL (for Probabilistic inference with ICL) that computes
the explanations to queries by means of a modification of SLDNF\--resolution
and then makes them mutually incompatible by means of Binary Decision Diagrams.
PICL and Cilog2 are compared on problems that involve computing the probability
of a connection between two nodes in biological graphs and social networks.
PICL turned to be more efficient, handling larger networks/more complex queries
in a shorter time than Cilog2. This is true both for marginal and for
conditional queries
Classification and visualization on the hepatitis dataset
No full textIn this paper we address goals 2 and 3 of those proposed by the donors of the Hepatitis dataset, namely to evaluate whether it is possible to estimate the stage of liver fibrosis from the results of examinations, and to evaluate the effectiveness of the interferon therapy. Goal 2 was addressed by learning various classifiers that predict the value of fibrosis from the values of examinations other than the biopsy. Unfortunately, the best accuracy obtained was only 50.6 %, up only 2.1 % from the performance of the default classifier, thus showing that replacing biopsies is still very hard if not impossible. As regards goal 3, we have plotted the distribution of the values of the difference in fibrosis and in activity before and after the interferon therapy. The plots show that the therapy actually reduces the level of activity but not the level of fibrosis.
Moreover, we have also plotted the distribution of the values of the difference of GOT before and after the therapy. The graph
shows that a moderate reduction of GOT is obtained
SLGAD Resolution for Inference on Logic Programs with Annotated Disjunctions
No full textLogic Programs with Annotated Disjunctions (LPADs) allow to express
probabilistic information in logic programming. The semantics of an LPAD is
given in terms of well-founded models of the normal logic programs obtained
by selecting one disjunct from each ground LPAD clause.
Inference on LPADs can be performed using either the system Ailog2, that was
developed for the Independent Choice Logic, or SLDNFAD, an algorithm based on
SLDNF. However, both of these algorithms run the risk of going into infinite
loops and of performing redundant computations.
In order to avoid these problems, we present SLGAD resolution that computes
the (conditional) probability of a ground query from a range-restricted LPAD
and is based on SLG resolution for normal logic programs. As SLG, it uses
tabling to avoid some infinite loops and to avoid redundant computations.
The performances of SLGAD are evaluated on classical benchmarks for normal logic
programs under the well-founded semantics, namely a 2-person game and the
ancestor relation, and on a game of dice.
SLGAD is compared with Ailog2 and SLDNFAD on the problems in which they do
not go into infinite loops, namely those that are described by a modularly
acyclic program.
On the 2-person game and the ancestor relation, SLGAD is more expensive than
SLDNFAD on problems where SLDNFAD succeeds but is faster than Ailog2 when the
query is true in an exponential number of instances.
If the program requires the repeated computation of similar goals, as for the
dice game, then SLGAD outperforms both Ailog2 and SLDNFAD
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