Logistic optimization frequently involves complex routing decisions bound by tight numerical constraints such as vehicle capacities. This paper addresses a real-world industrial multi-batching problem where products must be routed between distributed sites. The objective is to determine optimal routes, travel frequencies, and packing configurations at minimum cost. The problem corresponds to a minimum cost flow problem coupled a bin packing problem. We investigate direct formalizations, decompositions, and scalable sequential approaches across three base technologies: Mixed-Integer Linear Programming, Constraint Programming, and Constraint Answer Set Programming. Our contributions are threefold: we propose a direct formalization of the problem, additional distinct approaches that scale for an industrial use case, and finally an empirical evaluation. By comparing these approaches we highlight the most effective configurations. Results suggests that a three-step approach provides the best results: combining MILP for flow routing, a greedy bin packing and CP for refinement.

In the design of global logistics problems, the solution spaces are typically extremely large. To demonstrate how these challenges can be addressed in Answer Set Programming (ASP), this work investigates a representative industrial use case of a global logistics problem in the aerospace problem domain. An exploration of specific areas of the search space is done by using heuristic-driven solving for the formulation of domain heuristics that guide the solver to potentially desirable configurations. A quantitative evaluation on the Key Performance Indicators and a qualitative evaluation on the variability of the models by means of a similarity analysis shows promising results.

The Weak Completion Semantics is a novel cognitive theory which has been successfully applied -- among others -- to the suppression task, the selection task and syllogistic reasoning. It is based on logic programming with skeptical abduction. Each weakly completed program admits a least model under the three-valued Lukasiewicz logic which can be computed as the least fixed point of an appropriate semantic operator. The operator can be represented by a three-layer feed-forward network using the Core method. Its least fixed point is the unique stable state of a recursive network which is obtained from the three-layer feed-forward core by mapping the activation of the output layer back to the input layer. The recursive network is embedded into a novel network to compute skeptical abduction. This extended abstract outlines a fully connectionist realization of the Weak Completion Semantics.

The weak completion semantics is an integrated and computational cognitive theory which is based on normal logic programs,three-valued Lukasiewicz logic, weak completion, and skeptical abduction. It has been successfully applied – among others – to the suppression task, the selection task, and to human syllogistic reasoning. In order to solve ethical decision problems like – for example – trolley problems, we need to extend the weak completion semantics to deal with actions and causality. To this end we consider normal logic programs and a set E of equations as in the fluent calculus. We formally show that normal logic programs with equality admit a least E-model under the weak completion semantics and that this E-model can be computed as the least fixed point of an associated semantic operator. We show that the operator is not continuous in general, but is continuous if the logic program is a propositional, a finite-ground, or a finite datalog program and the Herbrand E-universe is finite. Finally, we show that the weak completion semantics with equality can solve a variety of ethical decision problems like the bystander case, the footbridge case, and the loop case by computing the least E-model and reasoning with respect to this E-model. The reasoning process involves counterfactuals which is necessary to model the different ethical dilemmas.

The Weak Completion Semantics is a novel cognitive theory which has been successfully applied to the suppression task, the selection task, syllogistic reasoning, the belief bias effect, spatial reasoning as well as reasoning with conditionals. It is based on logic programming with skeptical abduction. Each program admits a least model under the three-valued Lukasiewicz logic, which can be computed as the least fixed point of an appropriate semantic operator. The semantic operator can be represented by a three-layer feed-forward network using the core method. Its least fixed point is the unique stable state of a recursive network which is obtained from the three-layer feed-forward core by mapping the activation of the output layer back to the input layer. The recursive network is embedded into a novel network to compute skeptical abduction. This paper presents a fully connectionist realization of the Weak Completion Semantics.

Abstract The belief bias effect is a phenomenon which occurs when we think that we judge an argument based on our reasoning, but are actually influenced by our beliefs and prior knowledge. Evans, Barston and Pollard carried out a psychological syllogistic reasoning task to prove this effect. Participants were asked whether they would accept or reject a given syllogism. We discuss one specific case which is commonly assumed to be believable but which is actually not logically valid. By introducing abnormalities, abduction and background knowledge, we adequately model this case under the weak completion semantics. Our formalization reveals new questions about possible extensions in abductive reasoning. For instance, observations and their explanations might include some relevant prior abductive contextual information concerning some side-effect or leading to a contestable or refutable side-effect. A weaker notion indicates the support of some relevant consequences by a prior abductive context. Yet another definition describes jointly supported relevant consequences, which captures the idea of two observations containing mutually supportive side-effects. Though motivated with and exemplified by the running psychology application, the various new general abductive context definitions are introduced here and given a declarative semantics for the first time, and have a much wider scope of application. Inspection points, a concept introduced by Pereira and Pinto, allows us to express these definitions syntactically and intertwine them into an operational semantics.

Building domain taxonomies is a crucial task in the domain of ontology construction. Domain taxonomy learning keeps getting more important as a form of automatically obtaining a knowledge representation of a certain domain. The alternative of manually developing domain taxonomies is not trivial. The main issues encountered when manually developing a taxonomy are the non-availability of a domain knowledge expert and the considerable amount of effort needed for this task. This paper proposes Taxo Learn, an approach to automatic construction of domain taxonomies. Taxo Learn is a new methodology that combines aspects from existing approaches, but also contains new steps in order to improve the quality of the resulted domain taxonomy. The contribution of this paper is threefold. First, we employ a word sense disambiguation step when detecting concepts in the text. Second, we show the use of semantics-based hierarchical clustering for the purpose of taxonomy learning. Third, we propose a novel dynamic labeling procedure for the concept clusters. We evaluate our approach by comparing the machine generated taxonomy with a manually constructed golden taxonomy. Based on a corpus of documents in the field of financial economics, Taxo Learn shows a high precision for the learned taxonomic concept relationships.