The current state-of-the-art in multi-objective optimization assumes either a given utility function, learns a utility function interactively or tries to determine the complete Pareto front, requiring a post elicitation of the preferred result. However, result elicitation in real world problems is often based on implicit and explicit expert knowledge, making it difficult to define a utility function, whereas interactive learning or post elicitation requires repeated and expensive expert involvement. To mitigate this, we learn a utility function offline, using expert knowledge by means of preference learning. In contrast to other works, we do not only use (pairwise) result preferences, but also coarse information about the utility function space. This enables us to improve the utility function estimate, especially when using very few results. Additionally, we model the occurring uncertainties in the utility function learning task and propagate them through the whole optimization chain. Our method to learn a utility function eliminates the need of repeated expert involvement while still leading to high-quality results. We show the sample efficiency and quality gains of the proposed method in 4 domains, especially in cases where the surrogate utility function is not able to exactly capture the true expert utility function. We also show that to obtain good results, it is important to consider the induced uncertainties and analyze the effect of biased samples, which is a common problem in real world domains.

In the middle of the 1980s, David Poole introduced a semantical, model-theoretic notion of specificity to the artificial-intelligence community. Since then it has found further applications in non-monotonic reasoning, in particularin defeasible reasoning. Poole's notion, however, turns out to be intricate and problematic,which -- as we show -- can be overcome to some extent by a closer approximation of the intuitive human concept of specificity. Besides the intuitive advantages of our novel specificity ordering over Poole's specificity relation in the classical examples of the literature, we also report some hard mathematical facts: Contrary to what was claimed before, we show that Poole's relation is not transitive. Our new notion of specificity is transitive and also monotonic w.r.t.

In the early 1960s, the languages Fortran (John Backus, IBM) for scientific, and Cobol (Jean Sammet, IBM, and DoD) for commercial applications dominated. Programs were written on paper, then punched on cards, and one waited a day for the results. Programming languages were recognized as essential aids and accelerators of the programming process. In 1960, an international committee published the language Algol 60.1 It was the first time a language was defined by concisely formulated constructs and by a precise, formal syntax. Two years later, it was recognized that a few corrections and improvements were needed. Mainly, however, the range of applications should be widened, because Algol 60 was intended for scientific calculations (numerical mathematics) only.

When China restricted the importation of recyclable waste products in 2018, many western companies turned to robotic technologies to strengthen their processing capabilities. "The ban exposed how vulnerable the current infrastructure for recycling is," says Chris Wirth, vice-president of marketing and business development for AMP Robotics, a Denver-based industrial recycling artificial intelligence company. To recycle in a cost-effective, comprehensive and safe way, goods must be broken down into their constituent commodities to be sold on, in a process that has been likened to "unscrambling an egg". Roboticists think that computer vision, neural networks and modular robotics can enable a more intelligent, flexible approach to recycling. AI-enabled robotics can identify items based on visual cues such as logos, colour, shape and texture, sorting them and taking them apart.

Deployed for healthcare, environmental and even bartending duties around the world, robots have just been enlisted to sort through waste flowing through a Florida recycling plant, where they perform faster and safer than humanly possible. The 14 high-speed, precision robots installed this summer at Single Stream Recyclers (SSR) in Sarasota are guided by an artificial intelligence platform that applies computer vision and machine learning to direct the robots' rapid-fire movements. "The average human can pick 30-40 items per minute. The robots can pick 80 items per minute," said John Hansen, co-owner of SSR, which processes materials from numerous Southwest Florida communities at its nearly 100,000-square-foot recovery facility. Developed by Denver-based AMP Robotics, the robots identify and sort plastics, cartons, paper, cardboard, metals and other materials streaming through their cube-like housing.

Free variables occur frequently in mathematics and computer science with ad hoc and altering semantics. We present the most recent version of our free-variable framework for two-valued logics with properly improved functionality, but only two kinds of free variables left (instead of three): implicitly universally and implicitly existentially quantified ones, now simply called "free atoms" and "free variables", respectively. The quantificational expressiveness and the problem-solving facilities of our framework exceed standard first-order and even higher-order modal logics, and directly support Fermat's descente infinie. With the improved version of our framework, we can now model also Henkin quantification, neither using quantifiers (binders) nor raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a choice operator with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports reductive proof search optimally.

Paul Bernays and David Hilbert carefully avoided overspecification of Hilbert's epsilon-operator and axiomatized only what was relevant for their proof-theoretic investigations. Semantically, this left the epsilon-operator underspecified. In the meanwhile, there have been several suggestions for semantics of the epsilon as a choice operator. After reviewing the literature on semantics of Hilbert's epsilon operator, we propose a new semantics with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports proof search optimally and is natural in the sense that it does not only mirror some cases of referential interpretation of indefinite articles in natural language, but may also contribute to philosophy of language. Finally, we ask the question whether our epsilon within our free-variable framework can serve as a paradigm useful in the specification and computation of semantics of discourses in natural language.