Smoke Test Planning using Answer Set Programming

Smoke testing is an important method to increase stability and reliability of hardware- gramming, Testing depending systems. Due to concurrent access to the same physical resource and the impracticality of the use of virtualization, smoke testing requires some form of planning. In this paper, we propose to decompose test cases in terms of atomic actions consisting of preconditions and effects. We present a solution based on answer set programming with multi-shot solving that automatically generates short parallel test plans. Experiments suggest that the approach is feasible for non-inherently sequential test cases and scales up to thousands of test cases

Domain-agnostic procedural content generation can be done declaratively

Procedural content generation is applied in the development process of many commercial games: au- tomatically generated contents are delivered to players in order to offer a constantly changing user experience and enrich the game itself. Designing algorithms for content generators can be a tedious job. The product of this work is often too domain specific and offers limited reusability and customizability. Declarative approaches to content generation, more properly defined as Declarative Content Specifica- tion (DCS) techniques, like the ones based on Answer Set Programming (ASP), promise to overcome some of these drawbacks, since they allow focusing on describing content requirements rather than programming ad-hoc generation engines. Also, DCS speed up the prototype generation techniques themselves. In turn, DCS techniques struggle to gain momentum mainly because of lack of integration with game engines. Furthermore, the promise of generality and reusability is neutralized by the burden of wiring and adapting declarative content specifications to the context of the game at hand. In this work, we report about our progress toward a general DCS module working in the Unity game engine, and integrated in an existing asset for declaratively defining AI modules. We illustrate both the design and runtime workflow of this module, and how game content developers could use it for devising their own content generation schemes. For this purpose, an example highlighting the advantages of this approach is described

Joint Reasoning for Multi-Faceted Commonsense Knowledge

Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de