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Did you miss the school? Don't worry! All lectures were recorded and can be viewed at:
Did you miss the school? Don't worry! All lectures were recorded and can be viewed at:
Students and researchers interested in topics in logic programming are invited to attend the 2020 Autumn School. It will take place during the 36th International Conference on Logic Programming (ICLP) (September 18-24, 2020, Rende, Italy).
Students and researchers interested in topics in logic programming are invited to attend the 2020 Autumn School. It will take place during the 36th International Conference on Logic Programming (ICLP) (September 18-24, 2020, Rende, Italy).
The 2-day school is suited for those who wish to learn advanced topics in logic programming and constraint programming. It will consist of four half-day tutorials.
The 2-day school is suited for those who wish to learn advanced topics in logic programming and constraint programming. It will consist of four half-day tutorials.
Schedule:
Schedule:
18/9 14.15-16.15: Peter Stuckey: MiniZinc for high-level solver-independent modelling
18/9 16.30-18.30: Elena Bellodi: Introduction to Probabilistic Logic Programming
19/9 14.15-16.15: Marc Denecker: On the informal semantics of knowledge representation languages and the case of Logic Programming
19/9 16.30-18.30: Martin Gebser: Applications of Answer Set Programming
Confirmed Speakers:
Confirmed Speakers:
Martin Gebser (Klagenfurt University): Applications of Answer Set Programming
Martin Gebser (Klagenfurt University): Applications of Answer Set Programming
Abstract: 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.
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.
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.
Marc Denecker (KU Leuven): On the informal semantics of knowledge representation languages and the case of Logic Programming.
Marc Denecker (KU Leuven): On the informal semantics of knowledge representation languages and the case of Logic Programming.
Abstract: 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.
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.
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.
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.
Peter Stuckey (Monash University): MiniZinc for high-level solver-independent modelling
Peter Stuckey (Monash University): MiniZinc for high-level solver-independent modelling
Abstract: 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.
Practical files needed for tutorial can be found here.
Elena Bellodi (University of Ferrara): Introduction to Probabilistic Logic Programming
Elena Bellodi (University of Ferrara): Introduction to Probabilistic Logic Programming
Abstract: 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.
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.
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 http://cplint.eu.
Registration
Registration
Registration is part of the ICLP 2020 registration
The Autumn School on Logic and Constraint Programming can be attended by any of the participants of ICLP conference or workshops. There is no additional registration fee.
We highly recommend students to also participate in our doctoral consortium. More information can be found on the DC page
Organizers
Organizers
Daniela Inclezan, Miami University
Bart Bogaerts, Vrije Universiteit Brussel
Gopal Gupta, University of Texas at Dallas
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