Invited Invited Presentations

• Sunday, September 9th, 9:00 - 10:00 (Tutorial)

Thomas Eiter, Vienna University of Technology, Austria

Answer Set Programming for the Semantic Web

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Abstract: The Semantic Web aims at extending the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and automation of tasks. Its development proceeds in layers, and the Ontology layer is the highest one that has currently reached a sufficient maturity, in the form of the Web Ontology Language (OWL), which is based on Description Logics. Current efforts are focused on realizing the Rules layer, which should complement the Ontology layer and offer sophisticated representation and reasoning capabilities. This raises, in particular, the issue of interlinking rules and ontologies.
Answer Set Programming (ASP), also called A-Prolog, is a well-known declarative programming paradigm which has its roots in Logic Programming and Non-monotonic Reasoning. Thanks to its many extensions, ASP is well-suited for modeling and solving problems which involve common sense reasoning, and has been fruitfully applied to a range of applications including data integration, configuration, diagnosis, text mining, and reasoning about actions and change.
Within the context of the Semantic Web, the usage of ASP and related formalisms has been explored in different directions. They have been exploited as a tool to encode reasoning tasks in Description Logics, but also as a basis for giving a semantics to a combination of rules and ontologies. In that, increasing levels of integration have been considered: loose couplings, where rule and ontology predicates are separated, and the interaction is via a safe semantic interface like an inference relation; tight couplings, where rule and ontology predicates are separated, and the interaction is at the level of models; full integration, where no distinction between rule and ontology predicates is made.
We will first briefly review ASP and ontology formalisms. We then will recall some of the issues that come up with the integration of rules and ontologies. After that, we will consider approaches to combine rules and ontologies under ASP, where particular attention well be devoted to non-monotonic description logic programs and their derivatives as a representative of loose couplings. However, also other approaches will be discussed. We further discuss the potential of such combinations, some applications, and finally some open issues.
The tutorial is based on material and results obtained in joint work with Jos de Bruijn (Free University of Bolzano), Giovambattista Ianni (Universita della Calabria), Thomas Krennwallner (TU Wien), Thomas Lukasiewicz (Universita di Roma ``La Sapienza'', Axel Polleres (DERI Galway), Roman Schindlauer (TU Wien), and Hans Tompits (TU Wien).

Short Biography: Thomas Eiter is a full professor (since 1998) in the Faculty of Informatics at Vienna University of Technology (TU Wien), Austria, and Head of the Institute of Information Systems (since 2004), where he also heads the Knowledge Based Systems Group. Before (1996-1998), he was an associate professor of Computer Science at the University of Giessen, Germany. He received his scientific education at TU Wien, where he graduated in Computer Science in 1989 and earned the Doctoral degree in Computer Science in 1991.
Dr. Eiter's current research interests include knowledge representation and reasoning, database foundations, logic programming, complexity in AI, knowledge-based agents, and logic in computer science. He has more than 150 publications in these areas, many of which appeared in prominent journals and conferences, including JACM, Artificial Intelligence, ACM TODS, ACM TOCL, JCSS, TPLP, IEEE TDKE etc. He has contributed to the DLV system and its extensions, like the DLVHEX system. Dr. Eiter's work has been honored with the IJCAI 2001 and AAAI 2002 Distinguished Paper Awards, and with a Best Paper Award of the European Semantic Web Conference (ESWC) 2006. He is a fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI), and a corresponding member of the Austrian Academy of Sciences.
Dr. Eiter has been involved in a number of national and international research projects, including the EU Networks of Excellence Compulog, CologNet, and REWERSE, and the EU Working Group WASP, which are related to the subject of this workshop. He is general chair of the the upcoming RR 2008 conference, and was PC co-chair of LPNMR 2001, FOIKS 2002, ICDT 2005, and RuleML 2006, as well general co-chair of KI 2001 and of a number of workshops.
Dr. Eiter serves currently on the board of the Austrian Science Fund (equivalent to the NSF) as a representative of Computer Science, the editorial board of Artificial Intelligence, and the advisory boards of the Journal of Artificial Intelligence Research (JAIR) and the Journal on Theory and Practice of Logic Programming (TPLP). He is a former associate editor of the IEEE Transactions on Knowledge and Data Engineering (TKDE).

• Monday, September 10th, 9:00 - 10:00 (Tutorial)

Miroslaw Truszczynski, University of Kentucky, USA

Logic Programming for Knowledge Representation

Abstract: In this tutorial I will discuss three recent research directions in logic programming inspired by knowledge representation needs. First, knowledge bases must be constructed in a modular fashion. Second, there has been a need for extending the syntax of answer-set programs with means to model numeric constraints. Such constraints are ubiquitous and, in particular, are common in knowledge representation applications. Next, there are logics other than logic programs with the answer-set semantics that also give rise to knowledge representation systems based on the principle that models of theories describe problem solutions. I will discuss one of the most promising such approaches, based on the ideas of model expansion and inductive definitions.
Theoretical advances in answer-set programming would remain just that, if they were not accompanied by effective answer-set programming software. I will conclude with comments on the state-of-the-art implementations.

Short Biography: Miroslaw Truszczynski received his Ph.D. from the Warsaw University of Technology in 1980. In 1984 he joined the Department of Computer Science at the University of Kentucky. He was promoted to the rank of professor in 1991. From 1993 to 2007 he served as Chair of the department.
Truszczynski's research interests include knowledge representation, nonmonotonic reasoning, logic, logic programming, constraint programming and combinatorics. He is the author or co-author of over 130 technical papers. Jointly with Victor Marek he wrote a research monograph "Nonmonotonic Logic" on mathematical foundations of nonmonotonic reasoning, which marked a milestone in the development of the field. He was also a co-editor of the book "The Logic Programming Paradigm: a 25-Year Perspective", celebrating accomplishments of the field of logic programming.
Truszczynski is active in his research community. He served as organizer or program committee chair for several conferences. He is a member of the Executive Committee of the Association of Logic Programming, Steering Committee of the Knowledge Representation, Inc., and Chair of the Steering Committee of Nonmonotonic Reasoning Workshops. He also serves on editorial and advisory boards of several professional journals, including Journal of Artificial Intelligence Research, Theory and Practice of Logic Programming, and AI Communications.

• Monday, September 10th, 14:00 - 15:00 (Tutorial)

Gopal Gupta, University of Texas at Dallas, USA

Coinductive Logic Programming and its Applications

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Abstract: Coinduction has recently been introduced as a powerful technique for reasoning about unfounded sets, unbounded structures, and interactive computations. Where induction corresponds to least fixed point semantics, coinduction corresponds to greatest fixed point semantics. In this paper we discuss the introduction of coinduction into logic programming. We discuss applications of coinductive logic programming to verification and model checking, lazy evaluation, concurrent logic programming and non-monotonic reasoning.

Short Biography: Gopal Gupta is a Professor of Computer Science at the University of Texas at Dallas. Prior to that he was in the faculty of New Mexico State University. His research interests are in programming languages, logic programming, parallel/distributed computing, software engineering, intelligent systems, and assistive technology. He has published extensively in these areas and has served in program committees of numerous conferences in these areas. He serves on the editorial board of the Theory and Practice of Logic Programming journal. He is a member of the executive council of the Association for Logic Programming and a past member of the European Association for Programming Languages and Systems.

• Tuesday, September 11th, 9:00 - 10:00 (Tutorial)

Michael Hanus, Christian-Albrechts-Universität zu Kiel, Germany

Multi-Paradigm Declarative Languages

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Abstract: Declarative programming languages advocate a programming style expressing the properties of problems and their solutions rather than how to compute individual solutions. Depending on the underlying formalism to express such properties, one can distinguish different classes of declarative languages, like functional, logic, or constraint programming languages. This tutorial surveys approaches to combine these different classes into a single programming language.

Short Biography: Michael Hanus studied computer science at the University of Dortmund where he received his Ph.D. degree. He had positions at the universities of Dortmund, Bielefeld, and Aachen and at the Max-Planck-Institut fuer Informatik in Saarbruecken. Since 2000 he is Professor of Computer Science at the University of Kiel and holds the chair of programming languages and compiler construction. His research activity is mainly concerned with the integration of functional and logic programming languages, the design and implementation of declarative programming languages, type systems for logic programming, analysis techniques for declarative programs, programming environments and applications of declarative languages. Currently, he is involved in the design, implementation, and application of the multi-paradigm declarative language Curry. He has published more than ninety papers on these topics in international conference proceedings, journals, and books.

• Tuesday, September 11th - 14:00 - 15:00 (Invited Talk)

Chitta Baral, Arizona State University, USA

Towards Overcoming the Knowledge Acquisition Bottleneck
in Answer Set Prolog Applications: Embracing Natural Language Inputs

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Abstract: Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language semantics have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this talk we will discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving. In particular we will discuss reasoning in the travel domain, textual entailment, reasoning about cardinality of sets, and solving combinatorial puzzles.
Various parts of this talk are joint work with Michael Gelfond, Marcello Balduccini, Richard Scherl, and my students Luis Tari and Juraj Dzifcak.

Short Biography: Chitta Baral is a professor at the Arizona State University. He obtained his B.Tech(Hons) degree from the Indian Institute of Technology, Kharagpur in 1987 and his M.S and Ph.D degrees from the University of Maryland at College Park in 1990 and 1991 respectively. He has been working in the field of knowledge representation and logic programming since 1988, especially in the area of reasoning about actions and change. His research has been supported over the years by National Science Foundation, NASA, United Space Alliance, and ARDA/DTO. He received the NSF CAREER award in 1995. He authored the book ``Knowledge Representation, Reasoning, and Declarative Problem Solving'' about Answer Set Prolog in 2003. In recent years, he has been working on using Answer Set Prolog for reasoning in the molecular biology domain and in natural language based question answering systems. For more about him and his research please see http://www.public.asu.edu/~cbaral/.

• Wednesday, September 12th, 9:00 - 10:00 (Invited Talk)

Gerhard Brewka, University of Leipzig, Germany

Preferences, Contexts and Answer Sets

Abstract: Answer set programming (ASP) is a declarative programming paradigm based on logic programs under stable model semantics, respectively its generalization to answer set semantics. Besides the availability of rather efficient answer set solvers, one of the major reasons for the success of ASP in recent years was the shift from a theorem proving to a constraint programming view: problems are represented such that stable models, respectively answer sets, rather than theorems correspond to solutions.
It is obvious that preferences play an important role in everyday decision making - and in many AI applications. For this reason a number of approaches combining answer set programming with explicit representations of preferences have been developed over the last years. The approaches can be roughly categorized according to the preference representation (quantitative vs. qualitative) the type of preferences they allow (static vs. dynamic) and the objects of prioritization (rules vs. atoms/formulas).
We will focus on qualitative dynamic formula preferences, give an account of existing approaches and show that by adding adequate optimization constructs one obtains interesting solutions to problems in belief merging, consistency handling, game theory and social choice.
Explicit representations of contexts also have quite a tradition in AI, going back to foundational work of John McCarthy. A context, intuitively, is a particular view of a state of affairs. Contexts can also be used as representations of beliefs of multiple agents.
We show how multi-context systems based on bridge rules, as developed by Fausto Giunchiglia and colleagues in Trento, can be extended to nonmonotonic context systems. We first discuss multi-context logic programming systems, and then generalize the ideas underlying these systems to a general framework for integrating arbitrary logics, monotonic or nonmonotonic. Techniques from answer set programming are at the heart of the framework. We finally give a brief outlook on how the two main topics of the talk, preferences and contexts, can be combined fruitfully.
Several of the presented results were obtained in cooperation with Thomas Eiter, Ilkka Niemela and Mirek Truszczynski.

Short Biography: Gerhard Brewka is Professor for Intelligent Systems at University of Leipzig, where he also directs the doctoral programme in knowledge representation. Before moving to Leipzig he held positions in Sankt Augustin, Berkeley and Vienna. His major research areas are knowledge representation, in particular nonmonotonic reasoning, qualitative preference handling, inconsistency handling, models of argumentation, logic programming/answer set programming and nonmonotonic multi-context systems. He (co)-authored two books on nonmonotonic reasoning. He was/is PC-chair of ECAI-06, LPNMR-07 and KR-08. Gerhard Brewka is an ECCAI-fellow since 2002 and a member of the ECCAI-board since 2006.