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Ute Schmid

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Can Humans Teach Machines to Code?

Celine Hocquette
Johannes Langer
Andrew Cropper
Ute Schmid

The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide sufficient examples to teach a concept to a machine. To evaluate the validity of this assumption, we conduct a study where human participants provide examples for six programming concepts, such as finding the maximum element of a list. We evaluate the generalisation performance of five program synthesis systems trained on input-output examples (i) from a human group, (ii) from a gold standard set, and (iii) randomly sampled. Our results suggest that human-provided examples are typically insufficient for a program synthesis system to learn an accurate program.

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IROS Conference 2024 Conference Paper

FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework

Lukas Meyer
Andreas Gilson
Ute Schmid
Marc Stamminger

We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count. The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit. We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango. Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.

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IJCAI Conference 2017 Conference Paper

Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract)

José Hernández-Orallo
Fernando Martínez-Plumed
Ute Schmid
Michael Siebers
David Dowe

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ECAI Conference 2016 Conference Paper

A Practical Approach to Fuse Shape and Appearance Information in a Gaussian Facial Action Estimation Framework

Teena Hassan
Dominik Seuss
Johannes Wollenberg
Jens-Uwe Garbas
Ute Schmid

In many domains of computer vision, such as medical imaging and facial image analysis, it is necessary to combine shape (geometric) and appearance (texture) information. In this paper, we describe a method for combining geometric and texture-based evidence for facial actions within a Kalman filter framework. The geometric evidence is provided by a face alignment method. The texture-based evidence is provided by a set of Support Vector Machines (SVM) for various Action Units (AU). The proposed method is a practical solution to the problem of fusing categorical probabilities within a Kalman filter based state estimation framework. A first performance evaluation on upper face AUs demonstrates the practical applicability of the proposed fusion method. The method is applicable to arbitrary imaging domains, apart from facial action estimation.

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AIJ Journal 2016 Journal Article

Computer models solving intelligence test problems: Progress and implications

José Hernández-Orallo
Fernando Martínez-Plumed
Ute Schmid
Michael Siebers
David L. Dowe

While some computational models of intelligence test problems were proposed throughout the second half of the XXth century, in the first years of the XXIst century we have seen an increasing number of computer systems being able to score well on particular intelligence test tasks. However, despite this increasing trend there has been no general account of all these works in terms of how they relate to each other and what their real achievements are. Also, there is poor understanding about what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. In this paper, we provide some insight on these issues, in the form of nine specific questions, by giving a comprehensive account of about thirty computer models, from the 1960s to nowadays, and their relationships, focussing on the range of intelligence test tasks they address, the purpose of the models, how general or specialised these models are, the AI techniques they use in each case, their comparison with human performance, and their evaluation of item difficulty. As a conclusion, these tests and the computer models attempting them show that AI is still lacking general techniques to deal with a variety of problems at the same time. Nonetheless, a renewed attention on these problems and a more careful understanding of what intelligence tests offer for AI may help build new bridges between psychometrics, cognitive science, and AI; and may motivate new kinds of problem repositories.

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ECAI Conference 2010 Conference Paper

Data-Driven Detection of Recursive Program Schemes

Martin Hofmann 0008
Ute Schmid

We present an extension to a current approach to inductive programming (IGOR2), that is, learning (recursive) programs from incomplete specifications such as input/outout examples. IGOR2 uses an analytical, example-driven strategy for generalization. We extend the set of IGOR2's refinement operators by a further operator - identification of higher-order schemes - and can show that this extension does improve speed as well as scope.

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JMLR Journal 2006 Journal Article

Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach

Emanuel Kitzelmann
Ute Schmid

We describe an approach to the inductive synthesis of recursive equations from input/output-examples which is based on the classical two-step approach to induction of functional Lisp programs of Summers (1977). In a first step, I/O-examples are rewritten to traces which explain the outputs given the respective inputs based on a datatype theory. These traces can be integrated into one conditional expression which represents a non-recursive program. In a second step, this initial program term is generalized into recursive equations by searching for syntactical regularities in the term. Our approach extends the classical work in several aspects. The most important extensions are that we are able to induce a set of recursive equations in one synthesizing step, the equations may contain more than one recursive call, and additionally needed parameters are automatically introduced. [abs] [ pdf ][ bib ] &copy JMLR 2006. ( edit, beta )

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TCS Journal 2006 Journal Article

Metaphors and heuristic-driven theory projection (HDTP)

Helmar Gust
Kai-Uwe Kühnberger
Ute Schmid

A classical approach of modeling metaphoric expressions uses a source concept network that is mapped to a target concept network. Both networks are often represented as algebras. In this paper, a representation using the mathematically sound framework of heuristic-driven theory projection (HDTP) is presented which is—although quite different from classical approaches—algebraic in nature, too. HDTP has the advantage that a structural description of source and target can be given and the connection between both domains are more clearly specified. The major aspects of the formal properties of HDTP, the specification of the underlying algorithm HDTP-A, and the development of a formal semantics for analogical reasoning will be discussed. We will apply HDTP to different types of metaphors.

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ICAPS Conference 2000 Conference Paper

Applying Inductive Program Synthesis to Macro Learning

Ute Schmid
Fritz Wysotzki

The goal of this paper is to demonstratethat inductive progrwnsynthesis can be applied to learning macrooperators from planning experience. Wedefine macros as recursive program schemes (RPSs). An RPSrepresents the completesubgoal structure of a given problem domainwith arbitrary complexity(e. g., rocket transportation problemwith n objects), that is, it represents domainspecific control knowledge. Wepropose the following steps for macrolearning: (1) Exploring a problem domainwith small complexity (e. g., rocket with 3 objects) using an universal planning technique, (2) transforming the universal plan into a finite program, and (3) generalizing this programinto an RPS.

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