ABSTRACT. Electronic voting should offer at least the same guarantees than traditional paper-based voting systems. In order to achieve this, electronic voting protocols make use of cryptographic primitives, as in the more traditional case of authentication or key exchange protocols. All these protocols are notoriously difficult to design and flaws may be found years after their first release. Formal models, such as process algebra, Horn clauses, or constraint systems, have been successfully applied to automatically analyze traditional protocols and discover flaws. Electronic voting protocols however significantly increase the difficulty of the analysis task. Indeed, they involve for example new and sophisticated cryptographic primitives, new dedicated security properties, and new execution structures.

After an introduction to electronic voting, we will describe the current techniques for e-voting protocols analysis and review the key challenges towards a fully automated verification.

ABSTRACT. The CADE and IJCAR conferences are the major forums for the presentation of new research in all aspects of automated deduction. In order to stimulate ATP research and system development, and to expose ATP systems within and beyond the ATP community, the CADE ATP System Competition (CASC) is held at each CADE and IJCAR conference. CASC-J7 will be held on 20th July 2014, during the 7th International Joint Conference on Automated Reasoning (IJCAR, which incorporates CADE), CASC evaluates the performance of sound, fully automatic, ATP systems. The evaluation is in terms of:

• the number of problems solved,
• the number of problems solved with a solution output, and
• the average runtime for problems solved;

in the context of:

• a bounded number of eligible problems,
• chosen from the TPTP Problem Library, and
• specified time limits on solution attempts.

The competition organizer is Geoff Sutcliffe. The competition is overseen by a panel of knowledgeable researchers who are not participating in the event. The panel members are Bernhard Beckert, Maria Paola Bonacina, and Aart Middeldorp.

ABSTRACT. The termination competition focuses on automated termination analysis for all kinds of programming paradigms, including categories for term rewriting, imperative programming, logic programming, and functional programming. Moreover, there are also categories for automated complexity analysis. In all categories, participation of tools providing certified proofs is welcome.

In every category, all participating tools of that category are run on a randomly selected subset of the available problems. The goal of the termination competition is to demonstrate the power of the leading tools in each of these areas.

The 2014 execution of the competition is organized by Johannes Waldmann and Stefan von der Krone. The Steering Committee is formed by Jürgen Giesl, Frederic Mesnard, Albert Rubio (Chair), René Thiemann and Johannes Waldmann.

ABSTRACT. We consider the application of Constraint Handling Rules (CHR) for the specification of type inference systems, such as that used by Haskell. Confluence of CHR guarantees that the answer provided by type inference is correct and consistent. The standard method for establishing confluence relies on an assumption that the CHR program is terminating. However, many examples in practice are give rise to non-terminating CHR programs, rendering this method inapplicable. Despite no guarantee of termination or confluence, the Glasgow Haskell Compiler (GHC) supports options that allow the user to proceed with type inference anyway, e.g. via the use of the UndecidableInstances flag. In this paper we formally identify and verify a set of relaxed criteria, namely range-restricted-ness, local confluence, and ground termination, that ensure the consistency of CHR-based type inference that maps to potentially non-terminating CHR programs.

ABSTRACT. In computational cognitive science, the cognitive architecture ACT-R is very popular. It describes a model of cognition that is amenable to computer implementation, paving the way for computational psychology. Its underlying psychological theory has been investigated in many psychological experiments, but ACT-R lacks of a formal definition of its underlying concepts from a mathematical-computational view. Although the canonical implementation of ACT-R is now modularized, this production rule system is still hard to adapt and extend in central parts like the conflict resolution mechanism (which decides which of the applicable rules to apply next). In this work, we present a concise implementation of ACT-R based on Constraint Handling Rules which has been derived from a formalization in prior work. To show the adaptability of our approach, we implement several different conflict resolution mechanisms discussed in the ACT-R literature. This results in the first implementation of one such approach. For the other mechanisms, we empirically evaluate if our implementation matches the results of reference implementations of ACT-R.

ABSTRACT. This paper introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are uniform cumulative distribution functions (cdf) in order to employ linear computations in the probabilistic domain. The reasoning by means of p-box cdf-intervals is an interval computation which is exerted on the real domain then it is projected onto the cdf domain. This operation conveys additional knowledge represented by the obtained probabilistic bounds. The empirical evaluation of our implementation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.

ABSTRACT. Datalog± is a Datalog-based language family enhanced with existential quantification in rule heads, equalities and negative constraints. Query answering over databases with respect to a Datalog± theory is generally undecidable, however several syntactic restrictions have been proposed to remedy this fact. However, a useful and natural feature however is as of yet missing from Datalog±: The ability to express uncertain knowledge, or choices, using disjunction. It is the precise objective of the doctoral thesis herein discussed, to investigate the impact on the complexity of query answering, of adding disjunction to well-known decidable Datalog± fragments, namely guarded, sticky and weakly-acyclic Datalog± theories. For guarded theories with disjunction, we obtain a strong 2Exp lower bound in the combined complexity, even for very restricted formalisms like fixed sets of (disjunctive) inclusion dependencies. For sticky theories, the query answering problem becomes undecidable, even in the data complexity, and for weakly-acyclic query answering we see a reasonable and expected increase in complexity.

ABSTRACT. Constraint Handling Rules (CHR) has matured into a general purpose language over the past two decades. Any general purpose language requires its own development tools. Visualization tools, in particular, facilitate many tasks for programmers as well as beginners to the language. The article presents on-going work towards the visualization of CHR programs. The process is done through source-to-source transformation. It aims towards reaching a generic transformer to visualize different algorithms implemented in CHR.

ABSTRACT. The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL^P, which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog.

ABSTRACT. Describing complex objects by elementary ones is a common strategy in mathematics and science in general. In their seminal 1965 paper, Kenneth Krohn and John Rhodes showed that every finite deterministic automaton can be represented (or “emulated”) by a cascade product of very simple automata. This led to an elegant algebraic theory of automata based on finite semigroups (Krohn-Rhodes Theory). Surprisingly, by relating logic programs and automata, we can show in this paper that the Krohn-Rhodes Theory is applicable in Answer Set Programming (ASP). More precisely, we recast the concept of a cascade product to ASP, and prove that every program can be represented by a product of very simple programs, the reset and standard programs. Roughly, this implies that the reset and standard programs are the basic building blocks of ASP with respect to the cascade product. In a broader sense, this paper is a first step towards an algebraic theory of products and networks of nonmonotonic reasoning systems based on Krohn-Rhodes Theory, aiming at important open issues in ASP and AI in general.

ABSTRACT. The paper presents a knowledge representation language Alog which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of Alog, an algorithm for computing its answer sets, and comparison with other approaches.

ABSTRACT. In this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in terms of causal graphs formed by rule labels and edges that represent their application ordering. For positive programs, we show that the causal justifications obtained for a given atom have a direct correspondence to (relevant) syntactic proofs of that atom using the program rules involved in the graphs. The most interesting contribution is that this causal information is obtained in a purely semantic way, by algebraic operations (product, sum and application) on a lattice of causal values whose ordering relation expresses when a justification is stronger than another. Finally, for programs with negation, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct. As a result, default negation behaves as ``absence of proof'' and no justification is derived from negative literals, something that results convenient for elaboration tolerance, as we explain with several examples.

ABSTRACT. Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. The ASP Modeling Competition 2014, held on-site collocated with the 30th International Conference on Logic Programming (ICLP), follows the spirit of Prolog Programming Contests from previous ICLP editions. That is, complex problems of various kinds are waiting to be modeled, using the ASP-Core-2 standard input language of ASP systems. Dauntless programmers, novices and experts, are encouraged to team up, take the challenge and the glory.

ABSTRACT. My research goal is to employ a parser generation algorithm based on the Earley parsing algorithm to the evaluation and compilation of queries to logic programs, especially to deductive databases. By means of partial deduction, from a query to a logic program a parameterized automaton is to be generated that models the evaluation of this query. This automaton can be compiled to executable code; thus we expect a speedup in runtime of query evaluation.

ABSTRACT. Answer Set Programming (ASP) is a powerful modelling formalism that is very efficient in solving combinatorial problems. ASP solvers implement the stable model semantics that eliminates circular derivations between Boolean variables from the solutions of a logic program. Due to this, ASP solvers are better suited than propositional satisfiability (SAT) and Constraint Programming (CP) solvers to solve a certain class of problems whose specification includes inductive definitions such as reachability in a graph. On the other hand, ASP solvers suffer from the grounding bottleneck that occurs due to their inability to model finite domain variables. Furthermore, the existing stable model semantics are not sufficient to disallow circular reasoning on the bounds of numeric variables. An example where this is required is in modelling shortest paths between nodes in a graph. Just as reachability can be encoded as an inductive definition with one or more base cases and recursive rules, shortest paths between nodes can also be modelled with similar base cases and recursive rules for their upper bounds. This deficiency of stable model semantics introduces another type of grounding bottleneck in ASP systems that cannot be removed by naively merging ASP with CP solvers, but requires a theoretical extension of the semantics from Booleans and normal rules to bounds over numeric variables and more general rules. In this work, we propose Bound Founded Answer Set Programming (BFASP) that resolves this issue and consequently, removes all types of grounding bottleneck inherent in ASP systems.

ABSTRACT. This research introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are uniform cumulative distribution functions (cdf) in order to employ linear computations in the probabilistic domain. The reasoning by means of p-box cdf-intervals is an interval computation which is exerted on the real domain then it is projected onto the cdf domain. This operation conveys additional knowledge represented by the obtained probabilistic bounds. Empirical evaluation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.

ABSTRACT. Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers.

ABSTRACT. Dynamic systems play a central role in fields such as planning, verification, and databases. Fragmented throughout these fields, we find a multitude of languages to formally specify dynamic systems and a multitude of systems to reason on such specifications. Often, such systems are bound to one specific language and one specific inference task. It is troublesome that performing several inference tasks on the same knowledge requires translations of your specification to other languages. In this paper we study whether it is possible to perform a broad set of well-studied inference tasks on one specification. More concretely, we extend IDP with several inferences from fields concerned with dynamic specifications.

ABSTRACT. Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. Our claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp, whose statistics are used for generating static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a unified framework. Taking advantage of this, we conducted an extensive experimental study and compared the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the impact of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration but also over manually tuned configurations of clasp.

ABSTRACT. I am Joachim Jansen and this is my research summary, part of my application to the Doctoral Consortium at ICLP'14. I am a PhD student in the Knowledge Representation and Reasoning (KRR) research group, a subgroup of the Declarative Languages and Artificial Intelligence (DTAI) group at the department of Computer Science at KU Leuven. I started my PhD in September 2012. My promotor is prof. dr. ir. Gerda Janssens and my co-promotor is prof. dr. Marc Denecker.

ABSTRACT. This publication is to present a summary of research (referred as κ-Labs) carried out in author’s Ph.D studies on topic of application of Logic Programming as scripting language for virtual character behavior control in First Person Shooter (FPS) games.