Logic Programming and Machine Ethics

Transparency is a key requirement for ethical machines. Verified ethical behavior is not enough to establish justified trust in autonomous intelligent agents: it needs to be supported by the ability to explain decisions. Logic Programming (LP) has a great potential for developing such perspective ethical systems, as in fact logic rules are easily comprehensible by humans. Furthermore, LP is able to model causality, which is crucial for ethical decision making.Comment: In Proceedings ICLP 2020, arXiv:2009.09158. Invited paper for the ICLP2020 Panel on "Machine Ethics". arXiv admin note: text overlap with arXiv:1909.0825

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Air Force Institute of Technology Contributions to Air Force Research and Development, Calendar Year 1987

From the introduction:The primary mission of the Air Force Institute of Technology (AFIT) is education, but research and consulting are essential integral elements in the process. This report highlights AFIT\u27s contributions to Air Force research and development activities [in 1987]

On learning history based policies for controlling Markov decision processes

Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state abstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP. However, there has been little formal analysis of such history-based algorithms, as most existing frameworks focus exclusively on memory-less features. In this paper, we introduce a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction mappings. Furthermore, we use this framework to design a practical RL algorithm and we numerically evaluate its effectiveness on a set of continuous control tasks

Analysis and Modular Approach for Text Extraction from Scientific Figures on Limited Data

Scientific figures are widely used as compact, comprehensible representations of important information. The re-usability of these figures is however limited, as one can rarely search directly for them, since they are mostly indexing by their surrounding text (e. g., publication or website) which often does not contain the full-message of the figure. In this thesis, the focus is on making the content of scientific figures accessible by extracting the text from these figures. A modular pipeline for unsupervised text extraction from scientific figures, based on a thorough analysis of the literature, was built to address the problem. This modular pipeline was used to build several unsupervised approaches, to evaluate different methods from the literature and new methods and method combinations. Some supervised approaches were built as well for comparison. One challenge, while evaluating the approaches, was the lack of annotated data, which especially needed to be considered when building the supervised approach. Three existing datasets were used for evaluation as well as two datasets of 241 scientific figures which were manually created and annotated. Additionally, two existing datasets for text extraction from other types of images were used for pretraining the supervised approach. Several experiments showed the superiority of the unsupervised pipeline over common Optical Character Recognition engines and identified the best unsupervised approach. This unsupervised approach was compared with the best supervised approach, which, despite of the limited amount of training data available, clearly outperformed the unsupervised approach.Infografiken sind ein viel verwendetes Medium zur kompakten Darstellung von Kernaussagen. Die Nachnutzbarkeit dieser Abbildungen ist jedoch häufig limitiert, da sie schlecht auffindbar sind, da sie meist über die umschließenden Medien, wie beispielsweise Publikationen oder Webseiten, und nicht über ihren Inhalt indexiert sind. Der Fokus dieser Arbeit liegt auf der Extraktion der textuellen Inhalte aus Infografiken, um deren Inhalt zu erschließen. Ausgehend von einer umfangreichen Analyse verwandter Arbeiten, wurde ein generalisierender, modularer Ansatz für die unüberwachte Textextraktion aus wissenschaftlichen Abbildungen entwickelt. Mit diesem modularen Ansatz wurden mehrere unüberwachte Ansätze und daneben auch noch einige überwachte Ansätze umgesetzt, um diverse Methoden aus der Literatur sowie neue und bisher noch nicht genutzte Methoden zu vergleichen. Eine Herausforderung bei der Evaluation war die geringe Menge an annotierten Abbildungen, was insbesondere beim überwachten Ansatz Methoden berücksichtigt werden musste. Für die Evaluation wurden drei existierende Datensätze verwendet und zudem wurden zusätzlich zwei Datensätze mit insgesamt 241 Infografiken erstellt und mit den nötigen Informationen annotiert, sodass insgesamt 5 Datensätze für die Evaluation verwendet werden konnten. Für das Pre-Training des überwachten Ansatzes wurden zudem zwei Datensätze aus verwandten Textextraktionsbereichen verwendet. In verschiedenen Experimenten wird gezeigt, dass der unüberwachte Ansatz besser funktioniert als klassische Texterkennungsverfahren und es wird aus den verschiedenen unüberwachten Ansätzen der beste ermittelt. Dieser unüberwachte Ansatz wird mit dem überwachten Ansatz verglichen, der trotz begrenzter Trainingsdaten die besten Ergebnisse liefert

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics