ABSTRACT. Recent developments in Web technologies have transformed Web users from passive consumers to active creators of digital content. A significant portion of this content is of argumentative form, as users see the Web as a means to enable dialogical exchange, debating, and commenting on products, services or events. In this context, being able to identify, mine, represent, reason with, and query ar-gumentative information found online is an important consideration. In previous work, some of the authors of this paper proposed ArgQL, a high-level declarative language for querying argumentative information found online. The current paper describes various extensions and improvements of ArgQL that bring it closer to actual use in realistic environments. These include, methods to support more ex-pressive keyword-based searching in arguments, and the support for querying non-argumentative information that is associated with arguments, such as the date of creation, author, topic etc (i.e., argument metadata).

ABSTRACT. There is a crucial need for tools to help designing sustainable agrosystems. In this paper, we consider the issue of selecting plant species according to the ecosystem services they are likely to deliver. For that, we rely on the one hand on recent scientific results in agronomy linking functional traits (i.e., measurable characterics of plant species) to ecosystem services, and on the other hand on data collected by the research community in ecology. The architecture of our prototype is inspired by the ontology-based data access paradigm, which clearly distinguishes between the data level and the knowledge representation level, with mappings linking the two levels. Knowledge is represented in a rule langage that extends plain Datalog with computed functions and stratified negation. We detail the construction of a knowledge base devoted to vine grassing, i.e., installing herbaceous service plants in vineyards.

ABSTRACT. We present a novel approach to testing Answer Set Programs (ASPs) in the context of a system designed to enable a domain expert to write, test and deploy legal smart contracts. Common practice is to use compiled imperative languages to write smart contracts which limits what can be achieved, and provides a clear opportunity for an approach that empowers domain experts. Our system supports the construction of declarative smart contracts by domain experts by employing a smart user interface that communicates visually and verbally using domain expert level concepts. It captures the ontology and legal logic of a legal document in a model automatically constructed in ASP. This paper discusses a complementary approach to testing, achieved by structuring rules and splitting testing into model validation and program verification. Holding ontology information about the application domain allows the approach to be highly automated, so that we achieve automatic discovery of all hypothetical scenarios and exhaustive testing for each rule. Our approach places the domain expert in a tight learning loop where the behaviour of each rule scenario can be understood from the visual and verbal feedback, and rule corrections can be made immediately if required.

ABSTRACT. Decision Model and Notation (DMN) is a formalism for the representation of knowledge about decision processes. It represents a set of decision rules in an easy-to-understand tabular format, called a decision table. In this paper, we argue that current formal semantics for DMN have certain limitations and we propose a novel formal semantics. Our semantics considers a decision table as a definition. The semantics consists of two components: the first component captures the meaning of one row (rule), and the second component aggregates the meaning of the rules into meaning for the whole table. By choosing the second component in different ways, the different "hit policies" of DMN (i.e., mechanisms for deciding what happens when multiple rows of the same table are applicable) can be represented.

Our semantics can cope better with undefined and unknown values and provides a foundation for forms of reasoning different from deriving the output for a given set of inputs.

ABSTRACT. Preventive maintenance (PM) of manufacturing units aims at maintaining the operable condition of the production line while optimizing the maintenance timing and the loss of productivity during maintenance operations. The lesser studied type of preventive maintenance understands a production line as a single machine with multiple components of different maintenance needs. This is relevant when rotating machinery is deployed, e.g., in the paper and steel industries, to mass production of raw materials consumed by other businesses. A failure in any stage of the production line has the potential of making the entire machine inoperable and enforcing a shutdown and corrective maintenance costs. This work gives an abstract formalization of PM scheduling for multi-component machines as an optimization problem. To provide a lower bound for the complexity of the optimization problem, we prove that the underlying decision problem is NP-complete for varying-size multi-component machines and scheduling timelines. Besides the formalization, the second main contribution of the paper is due to the practical need to solve the problem in industrial applications: the work gives the first encoding of the PM scheduling problem using Answer Set Optimization (ASO). Some preliminary experiments are conducted and reported to set the scene for further algorithm development.

ABSTRACT. The representation of a dynamic problem in ASP usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or via an action or temporal language. The multiplication of variables and constraints is commonly done during grounding and the solver is completely ignorant about the temporal relationship among the different instances. On the other hand, a key factor in the performance of today's ASP solvers is conflict-driven constraint learning. Our question is now whether a constraint learned for particular time steps can be generalized and reused at other time stamps, and ultimately whether this enhances the overall solver performance on dynamic problems. Knowing full well the domain of time, we study conditions under which learned dynamic constraints can be generalized and propose a simple translation of the original logic program such that, for the translated programs, all learned constraints can be generalized to other time points. Last but not least, we empirically evaluate the impact of adding the generalized constraints to the ASP solver.

ABSTRACT. I-DLV-sr is a recently proposed logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the ASP system I^2-DLV. Flink enables distributed stream processing, whereas I^2-DLV acts as full-fledged reasoner capable of transparently performing incremental evaluations. In this paper, we present a new and optimized version of I-DLV-sr that features an improved management of parallel computations and communications between Flink and I^2-DLV, along with new linguistic extensions needed to allow its effective application in smart city scenarios.

ABSTRACT. We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LPMLN, a probabilistic extension of ASP based upon a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LPMLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative front-ends, one for LPMLN, PLOG, and PROBLOG. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. Although plingo's core amounts to a re-implementation of LPMLN in terms of modern ASP technology, it integrates various probabilistic reasoning modes with the full modeling language and reasoning spectrum of clingo.

ABSTRACT. Counterfactual reasoning has been the subject of extensive study in philosophy, logics, and AI. The connection between counterfactual reasoning and theory revision is well-known since Ramsey’s intuition, according to which “to find out whether the counterfactual ‘if A were true, then B would be true’ is satisfied in a state S, change the state S minimally to include A, and test whether B is satisfied in the resulting state”. In this paper we study how to model this idea in Defeasible Logic for devising logics for counterfactual reasoning and suitable selection function models.

ABSTRACT. We extend the KLM approach to defeasible reasoning to be applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide an appropriate semantics for it, and present a representation result characterising the semantic description of defeasibility in terms of our postulates. An advantage of our semantics is that it is sufficiently general to be applicable to other restricted versions of first-order logic as well. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by Rational Closure as it is defined for defeasible propositional logic and defeasible description logics. We show that this form of defeasible entailment is rational in the sense that it adheres to the full set of rationality postulates.

ABSTRACT. Given the increasingly dynamic nature of knowledge in the era of Web-based information exchange, techniques to revise recorded knowledge – such as knowledge graphs or ontologies – with respect to new findings are more important than ever. For knowledge representation approaches based on formal logics, the AGM belief revision postulates by Alchourrón, Gärdenfors, and Makinson continue to represent a cornerstone in research related to belief change. Katsuno and Mendelzon (K&M) adopted the AGM postulates for changing belief bases and characterized AGM belief base revision in propositional logic over finite signatures. We generalize K&M’s approach to (multiple) base revision in arbitrary Tarskian logics, covering all logics with a classical model-theoretic semantics and hence a wide variety of logics used in knowledge representation and beyond. Our generic formulation applies to various notions of “base” (such as belief sets, arbitrary or finite sets of sentences, or single sentences). The core result is a representation theorem showing a two-way correspondence between AGM base revision operators and certain “assignments”: functions mapping belief bases to total — yet not transitive — “preference” relations between interpretations. We also provide a characterization of all logics for which our result can be strengthened to assignments producing transitive preference relations (as in K&M’s original work).

ABSTRACT. Application development continues to evolve, with many technologies addressing user needs and an ever increasing volume and complexity of use cases. This session looks at how the current trends of development democratization, task to process automation, and low-code are impacting the interest in, and adoption, of decision technologies, and extrapolates to their evolution in the late 2020s.