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<p><tt>[Apologies for cross-posting]</tt><tt><br>
</tt><tt>[Please redistribute] </tt><tt><br>
</tt><tt><br>
</tt><tt>The organization of the autumn school on logic and
constraint programming invites you to participate in this year’s
school (September, 18-19, virtually in Calabria), co-located
with ICLP. It promises to be an interesting session -- for
students, as well as for more senior researchers -- in which
Marc Denecker discusses the <b>informal semantics</b> of logic
programs (is negation-as-failure actually classical?), Peter
Stuckey takes on the role of Trojan horse, convincing us to use
<b>Minizinc</b> instead of logic programming, Martin Gebser
provides unique insights in the magic he uses for tackling <b>industrial
applications</b> with answer set programming, and Elena
Bellodi will probably talk about <b>probabilistic logic
programming</b>.</tt><tt><br>
</tt><tt><br>
</tt><tt>The courses will be run as a hybrid model in which the
first two hours are thought live, and for the last two hours, a
recording will be made available. </tt><tt><br>
</tt><tt><br>
</tt><tt>The abstracts of these talks are included below. </tt><tt><br>
</tt><tt><br>
</tt><tt>Registration is included in the ICLP registration and can
be done via <a class="moz-txt-link-freetext" href="https://iclp2020.unical.it/registration">https://iclp2020.unical.it/registration</a> (early bird
registration ends at September 13th)</tt><tt><br>
</tt><tt><br>
</tt><tt>The talks will be a mixture of live sessions and
pre-recorded videos. More information will be made available on
<a class="moz-txt-link-freetext" href="https://sites.google.com/view/iclp-dc-2020/autumn-school-on-logic-programming?authuser=0">https://sites.google.com/view/iclp-dc-2020/autumn-school-on-logic-programming?authuser=0</a></tt><tt><br>
</tt><tt><br>
</tt><tt>Spread the word, and we hope to see you soon in virtual
Calabria. </tt><tt><br>
</tt><tt><br>
</tt><tt>Best regards,</tt><tt><br>
</tt><tt>Daniela Inclezan, Gopal Gupta, and Bart Bogaerts</tt><tt><br>
</tt><tt><br>
</tt><tt>--------------------------------</tt><tt><br>
</tt><tt><br>
</tt><b><tt>Martin Gebser (Klagenfurt University): Applications of
Answer Set Programming</tt></b><b><tt><br>
</tt></b><tt><b>Abstract:</b> Answer Set Programming (ASP) is a
paradigm of knowledge representation and reasoning that has
become a popular means for declarative problem solving. The
basic idea is to represent a complex application problem by a
logic program such that specific interpretations, called answer
sets, correspond to problem solutions. Powerful off-the-shelf
ASP systems, such as clingo, dlv and idp, automate the problem
solving process by first grounding a general problem encoding
relative to an instance given by facts, and then performing
Boolean constraint solving to compute (optimal) answer sets.</tt><tt><br>
</tt><tt>The application areas of ASP include a variety of domains
ranging from artificial intelligence, databases, mathematical
and scientific fields to industrial use cases. For instance, the
clingo system has been utilized for radio spectrum reallocation
in the first-ever incentive auction conducted by the Federal
Communications Commission, which in 2016 yielded about 20
billion dollars revenue. Likewise, the dlv system has been
deployed as a core tool in enterprise software for e-medicine,
e-tourism, intelligent call routing and workforce management.
Last but not least, the idp system has been harnessed for
interactive configuration in the banking sector.</tt><tt><br>
</tt><tt>Starting from the expressive modeling language, this
tutorial presents and illustrates central features making ASP
attractive for solving application problems. We particularly
demonstrate the proficient usage of optimization, which is of
crucial importance in virtually all realistic settings. Beyond
traditional single-shot solving, we also outline recent
advancements in multi-shot solving, driving the application of
ASP in dynamic areas like automated planning, robotics control
and stream reasoning.</tt><tt><br>
</tt><tt><br>
</tt><b><tt>Marc Denecker (KU Leuven): On the informal semantics
of knowledge representation languages and the case of Logic
Programming.</tt></b><b><tt><br>
</tt></b><tt><b>Abstract:</b> The informal semantics of a
formal language aims to explain the ``intuitive'' meaning of the
logical symbols, and of the formulas and theories of the
language. In the context of a KR language, it aims to express
the knowledge conveyed by formulas and theories about the
application domain, in a precise and systematic way. It is a
controversial concept. In formal science, one often avoids to
talk about such soft informal topics. For this reason, many may
prefer to view a (declarative) formal language as a tool to
encode computational problems. In that view, the question of its
informal ``intuitive'' semantics seems of no scientific
relevance. Strictly speaking, the meaning of negation as
failure is not a scientific question here.</tt><tt><br>
</tt><tt>In this course, we will view a formal KR language as a
formal study of certain types of knowledge. The question of its
informal semantics then becomes the corner stone of such a
study, as it relates the formal entities (the formulas) to the
informal objects that they intend to represent (the knowledge).
The scientific thesis of such a study is then that a formal
semantics correctly formalizes the informal semantics. The
course starts with some considerations on viewing a formal
language as a formal study of some forms of knowledge. The
discussion is based on, a.o., Poppers ideas of formal science.
The goal of this discussion is to derive insights needed to
understand the current status of informal semantics in Logic
Programming, and instruments to analyze it.</tt><tt><br>
</tt><tt>In the second part of the lecture, we apply the above
ideas and instruments on Logic Programming. A brief historical
overview is given on the topic of informal semantics. Three
main ideas for informal semantics were proposed: the Closed
World Assumption by Ray Reiter, logic programs as definitions by
Keith Clark, and the (auto)epistemic/default interpretation by
Michael Gelfond. We then analyze these informal semantics using
the instruments introduced in the first part: where these
informal semantics agree and disagree, how they were formalized,
how to interpret semantical objects, what is the meaning of
negation and the rule operator in them and which informal
semantics applies in the context of concrete examples.</tt><tt><br>
</tt><tt>The last part of the lecture is devoted to (inductive)
definitions and the definitional view of LP. We argue that it is
the most precise and the most widely applicable. Definitions
extend CWA but are more precise and more general. They are not
equivalent with the epistemic view and neither subsumes the
other. But there are more applications for definitions than for
epistemic theories. In the view of logic programs as
definitions, we argue that negation is classical but the rule
operator is not (which confirms what Clark suggested long ago).
We recall Harel's critique on completion semantics for
expressing inductive definitions, and give the proof that in
general, inductive definitions cannot be expressed in FO. We
discuss the integration of definitional knowledge with the
knowledge representation paradigm of classical logic, as it was
done in the logic FO(ID). We end with considering what the
declarative view of a logic program as a definition can
contribute in the view of LP as a programming language, as a
query language and as a KR language.</tt><tt><br>
</tt><tt><br>
</tt><b><tt>Peter Stuckey (Monash University): MiniZinc for
high-level solver-independent modelling</tt></b><b><tt><br>
</tt></b><tt><b>Abstract: </b>In this tutorial we will
introduce you to modelling discrete optimization problems using
MiniZinc. MiniZinc allows you to model a discrete optimization
problem without committing to a particular solver or solver
technology. Thus you can avoid committing to the wrong solver
technology to your problem. MiniZinc supports Constraint
Programming, Mixed Integer Programming, Boolean SATisfiability,
SAT Modulo Theories and Constraint-Based Local Search solvers.
The tutorial will cover basic modelling, modelling viewpoints,
and debugging models. The tutorial will involve a series of
hands-on tasks using MiniZinc.</tt><tt><br>
</tt><tt><br>
</tt><b><tt>Elena Bellodi (University of Ferrara): Probabilistic
Logic Programming</tt></b><b><tt><br>
</tt></b><tt><b>Abstract:</b> Recently much work in Machine
Learning has concentrated on representation languages able to
combine aspects of logic and probability, in order to model
domains characterized by both complex and uncertain
relationships among entities. Machine Learning approaches based
on such combinations have recently achieved important results,
originating the fields of Statistical Relational Learning,
Probabilistic Logic Programming and, more generally, Statistical
Relational Artificial Intelligence. </tt><tt><br>
</tt><tt>The course will concentrate on Probabilistic Logic
Programming (PLP), which has received an increasing attention
for its ability to incorporate probability in Logic Programming.
Among various proposals for PLP, the one based on the
distribution semantics has gained popularity being at the basis
of many PLP languages. </tt><tt><br>
</tt><tt>The course will describe syntax and semantics for the
main PLP languages under the distribution semantics, and
overview several systems for inference and learning. Then, it
will provide an overview of hybrid Probabilistic Logic Programs,
in which random variables may be both discrete and continuous.
The course will present the main application areas and will
include a hands-on experience with the PLP system cplint using
the web application <a class="moz-txt-link-freetext" href="http://cplint.eu">http://cplint.eu</a>.</tt><tt><br>
</tt></p>
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