ICLP DOCTORAL CONSORTIUM 2014 ABSTRACTS: PAPERS WITH ABSTRACTS

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 in many logic programming languages and in Descripion Logics languages.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 probability of queries w.r.t. probabilistic knowledge bases.

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.

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. 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. 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. 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. ICLP`14 DC application for Joachim Jansen

ABSTRACT. Datalogis 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 there are still open problems regarding these decidable fragments. A very useful and natural feature however is missing from Datalog: The ability to express uncertain knowledge, or choices, using disjunction. In my doctoral studies we endeavour to investigate the impact of adding these feature to known Datalog languages.