Joachim Jansen

Predicate logic as a modeling language: Modeling and solving some machine learning and data mining problems with IDP3

This paper provides a gentle introduction to problem solving with the IDP3 system. The core of IDP3 is a finite model generator that supports first order logic enriched with types, inductive definitions, aggregates and partial functions. It offers its users a modeling language that is a slight extension of predicate logic and allows them to solve a wide range of search problems. Apart from a small introductory example, applications are selected from problems that arose within machine learning and data mining research. These research areas have recently shown a strong interest in declarative modeling and constraint solving as opposed to algorithmic approaches. The paper illustrates that the IDP3 system can be a valuable tool for researchers with such an interest. The first problem is in the domain of stemmatology, a domain of philology concerned with the relationship between surviving variant versions of text. The second problem is about a somewhat related problem within biology where phylogenetic trees are used to represent the evolution of species. The third and final problem concerns the classical problem of learning a minimal automaton consistent with a given set of strings. For this last problem, we show that the performance of our solution comes very close to that of a state-of-the art solution. For each of these applications, we analyze the problem, illustrate the development of a logic-based model and explore how alternatives can affect the performance.

Simulating dynamic systems using linear time calculus theories

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 3 with several inferences from fields concerned with dynamic specifications.

A logic-based framework for the security analysis of Industrial Control Systems

Industrial Control Systems (ICS) are used for monitoring and controlling critical infrastructures such as power stations, waste water treatment facilities, traffic lights, and many more. Lately, these systems have become a popular target for cyber-attacks. Security is often an afterthought, leaving them vulnerable to all sorts of attacks. This article presents a formal approach for analysing the security of Industrial Control Systems, both during their design phase and while operational. A knowledge- based system is used to analyse a model of the control system and extract system vulnerabilities. The approach has been validated on an ICS in the design phase.

Bootstrapping Inference in the IDP Knowledge Base System

AbstractDeclarative systems aim at solving tasks by running inference engines on a specification, to free their users from having to specify how a task should be tackled. In order to provide such functionality, declarative systems themselves apply complex reasoning techniques, and, as a consequence, the development of such systems can be laborious work. In this paper, we demonstrate that the declarative approach can be applied to develop such systems, by tackling the tasks solved inside a declarative system declaratively. In order to do this, a meta-level representation of those specifications is often required. Furthermore, by using the language of the system for the meta-level representation, it opens the door to bootstrapping: an inference engine can be improved using the inference it performs itself.One such declarative system is the IDP knowledge base system, based on the language

Compiling input∗ FO(·) inductive definitions into tabled Prolog rules for IDP3

\rm FO(\cdot)^{\rm IDP}

Meta-level representations in the IDP knowledge base system: Towards bootstrapping inference engine development

FO(·)IDP, a rich extension of first-order logic. In this paper, we discuss how

Experimental Evaluation of a State-Of-The-Art Grounder

\rm FO(\cdot)^{\rm IDP}