Detecting embedded horn structure in propositional logic

We show that the problem of finding a maximum renamable Horn problem within a propositional satisfiability problem is NP-hard but can be formulated as a set packing and therefore a maximum clique problem, for which numerous algorithms and heuristics have been developed

Factive and nonfactive mental state attribution

Factive mental states, such as knowing or being aware, can only link an agent to the truth; by contrast, nonfactive states, such as believing or thinking, can link an agent to either truths or falsehoods. Researchers of mental state attribution often draw a sharp line between the capacity to attribute accurate states of mind and the capacity to attribute inaccurate or “reality-incongruent” states of mind, such as false belief. This article argues that the contrast that really matters for mental state attribution does not divide accurate from inaccurate states, but factive from nonfactive ones

Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Smart test data generators via logic programming

We present a novel counterexample generator for the interactive theorem prover Isabelle based on a compiler that synthesizes test data generators for functional programming languages (e.g. Standard ML, OCaml) from specifications in Isabelle. In contrast to naive type-based test data generators, the smart generators take the preconditions into account and only generate tests that fulfill the preconditions. The smart generators are constructed by a compiler that reformulates the preconditions as logic programs and analyzes them by an enriched mode inference. From this inference, the compiler can construct the desired generators in the functional programming language. These test data generators are applied to find errors in specifications, as we show in a case study of a hotel key card system

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks