CILC 2011

 CILC 2011 relazioni invitate e tutorial, con abstract.
Recovered 2026-09-14 by Claude for the APICe Event record.
Source: Internet Archive snapshot of http://www.sci.unich.it/cilc2011/

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CILC 2011 - Convegno Italiano Logica Computazionale
26-esimo Convegno Italiano di Logica Computazionale
31 Agosto - 2 Settembre 2011, Pescara
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Relatori invitati
Andrea Omicini (Università di Bologna)
Coordination Models and Technologies toward Self-Organising Systems
Starting from the pioneering work on Linda and Gamma, coordination models, languages and technologies have gone through an amazing evolution process over the years. From closed to open systems, from parallel computing to multi-agent systems, from database integration to knowledge-intensive environments, coordination abstractions and technologies have gained in relevance and impact in those scenarios where complexity is a key factor. In this talk, we outline and motivate 25 years of evolution of coordination models and languages, with particular care to logic-based and declarative approaches, and discuss their potential perspectives in the future of artificial systems.
Fausto Spoto (Università di Verona)
Static Analysis of Java. Can we be logical?
Static analysis is important for verification, debugging and
optimisation of computer programs. In this context,
we present the use of propositional logic formulas for
the representation of dependency information about variables for Java
and Java bytecode programs. The idea comes from the analysis of logic
programs and from a mathematical theory known as "domain refinement".
The approach is cast here inside a denotational formalism and
applied to the static analysis of Java programs, in order to determine
when local variables can never contain the special "dangerous" value null.
Abstract interpretation is the framework that we use
to prove the correctness of the technique. We discuss the
benefits of the approach, namely, the complete context and flow
sensitivity of the resulting analysis. We also discuss its main
limitation, that is, a very weak approximation for instance variables
(also known as object "fields"). Hence, we overcome the latter limitation
with an oracle-based technique. We conclude with some experimental
evaluation over actual programs and with the discussion of further
improvements and open problems
Tutorial
Fabrizio Riguzzi (Università di Ferrara)
Probabilistic Logic Languages
The combination of logic and probability is very useful for
modeling domains with complex and uncertain relationships among
entities. Many probabilistic logic languages have been proposed in
various research fields.
In logic programming, the distribution semantics has recently gained
an increased attention and is adopted by many languages such as the
Independent Choice Logic, PRISM, Logic Programs with Annotated
Disjunctions and ProbLog.
Other languages instead follow a knowledge-based model construction
approach in which the probabilistic logic theory is used directly as a
template for generating an underlying complex graphical model.
In logic programming, CLP(BN) is based on a direct translation into
Bayesian networks.
Markov Logic instead uses full first order logic formulas for
specifying an underlying Markov network.
The talk will illustrate these approaches for combining logic and
probability and will highlight similarity and differences.
The talk will also introduce the types of reasoning that can be
performed with these languages: inference, weight learning and
structure learning.
In inference we want to compute the probability of a query given the
model and, possibly, some evidence. In weight learning we know the
structural part of the model (the logic formulas) but not the numeric
part (the weights) and we want to infer the weights from data. In
structure learning we want to infer both the structure and the weights
of the model from data.
The tutorial will then illustrate existing approaches for inference in
probabilistic logic programming languages.
It will discuss in details algorithms for performing inference on
languages that follow the distribution semantics and in particular the
PITA algorithm that uses tabling and answer subsumtpion.
PITA is of interest for its speed and versatility, as it can easily be
optimized for simpler settings and even possibilistic uncertain
reasoning.