Solving AI planning problems by transformation into (normal) logic programs and computing answer sets (stable models) has gained considerable interest over the last years. We investigate in this context a classical AI search technique, bidirectional search, where search is performed both from the initial facts towards the goal and vice versa. Our contribution is to show how bidirectional search can be realized in the logic programming/answer set paradigm to planning. This seems not having been investigated so far. We report on practical experiments on planning problems from an AIPS competition and show how our approach helps speeding up the planning process. We perceive our contribution mainly as a technique that is compatible with and complementary to existing extensions and improvements, rather than as a concrete planning system.

Darwin is the first implementation of the Model Evolution Calculus by Baumgartner and Tinelli. The Model Evolution Calculus lifts the DPLL procedure to first-order logic. Darwin is meant to be a fast and clean implementation of the calculus, showing its effectiveness and providing a base for further improvements and extensions. Based on a brief summary of the Model Evolution Calculus, we describe in the main part of the paper Darwin's proof procedure and its data structures and algorithms, discussing the main design decisions and features that influence Darwin's performance. We also report on practical experiments carried out with problems from the CADE-18 and CADE-19 system competitions, as well as on results on parts of the TPTP problem library.

We aim to demonstrate that automated deduction techniques, in particular those following the model computation paradigm, are very well suited for database schema/query reasoning. Specifically, we present an approach to compute completed paths for database or XPath queries. The database schema and a query are transformed to disjunctive logic programs with default negation, using a description logic as an intermediate language. Our underlying deduction system, KRHyper, then detects if a query is satisfiable or not. In case of a satisfiable query, all completed paths -- those that fulfill all given constraints -- are returned as part of the computed models.

The purpose of computing completed paths is to reduce the workload on a query processor. Without the path completion, a usual XPath query processor would search the whole database for solutions to the query, which need not be the case when using completed paths instead.

We understand this paper as a first step, that covers a basic schema/query reasoning task by model-based deduction. Due to the underlying expressive logic formalism we expect our approach to easily adapt to more sophisticated problem settings, like type hierarchies as they evolve within the XML world.

The growing size of electronically available text corpora like companies' intranets or the WWW has made information access a hot topic within Computational Linguistics. Despite the success of statistical or keyword based methods, deeper Knowledge Representation (KR) techniques along with “inference” are often mentioned as mandatory, e.g. within the Semantic Web context, to enable e.g. better query answering based on “semantical” information. In this paper we try to contribute to the open question how to operationalize semantic information on a larger scale. As a basis we take the frame structures of the Berkeley FrameNet II project, which is a structured dictionary to explain the meaning of words from a lexicographic perspective. Our main contribution is a transformation of the FrameNet II frames into the answer set programming paradigm of logic programming.

Because a number of different reasoning tasks are subsumed under “inference” in the context of natural language processing, we emphasize the flexibility of our transformation. Together with methods for automatic annotation of text documents with frame semantics which are currently developed at various sites, we arrive at an infrastructure that supports experimentation with semantic information access as is currently demanded for.

The Living Book is a system for the management of personalized and scenario specific teaching material. The main goal of the system is to support the active, explorative and self-determined learning in lectures, tutorials and self study. Living Book includes a course on “Logic for Computer Scientists” with a uniform access to various tools like theorem provers and an interactive tableau editor. It is routinely used within teaching undergraduate courses at our university.

This paper describes both, the Living Book together with its use of theorem proving technology as a core component in the knowledge management system (KMS), and the use of this new concept in academic teaching. The KMS provides a scenario management component where teachers may describe those parts of given documents that are relevant in order to achieve a certain learning goal. The task of the KMS is to assemble new documents from a database of elementary units called “slices” (definitions, theorems, and so on) in a scenario-based way (like “I want to prepare for an exam and need to learn about resolution”).

The computation of such assemblies is carried out by a model-generating theorem prover for first-order logic with a default negation principle. Its input consists of meta data that describe the dependencies between different slices, and logic-programming style rules that describe the scenario-specific composition of slices. Additionally, users may assess what units they know or don't know. This information is stored in a user model, which is taken into account to compute a model that specifies the assembly of a personalized document.

This paper introduces the e-learning context we are faced with, motivates our choice of logic, sketches the newly developed calculus used in the KMS. Finally, the application and evaluation of Living Books is presented.