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

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

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System

Menetelmiä ja malleja kielelliseen ja musiikilliseen laskennalliseen luovuuteen

Computational creativity is an area of artificial intelligence that develops algorithms and simulations of creative phenomena, as well as tools for performing creative tasks. In this thesis, we present various computational methods and models of linguistic and musical creativity. The emphasis is on developing methods that are maximally unsupervised, i.e. methods that require a minimal amount of hand-crafted linguistic, world, or domain knowledge. This thesis consists of an introductory part and five original research articles. The introductory part outlines computational creativity as a research field and discusses some of the philosophical foundations underlying the current work. The research articles present specific methods and algorithms for automatic composition of poetry and songs. The first article proposes a corpus-based poetry generation method that relies on statistical language modelling and morphological analysis and synthesis. In the second article, we expand that basic model with constraint programming techniques to handle more aspects of the poetic structure and style. The third article presents a method for mining document-specific word associations and proposes using them in poetry generation to produce poems based, for instance, on a specific news story. The fourth article presents a song composition system that utilises constraint programming to produce songs with matching lyrics and music in a transformational way, i.e. it is able to modify its own search space and preferences. Transformationality of the system is achieved with a metalevel component that can modify the system's internal constraints leading into new conceptual spaces. Finally, the fifth article discusses possibilities of combining personal biosignal measurements, especially electroencephalography, with techniques of computational creativity and presents an art installation called Brain Poetry based on these ideas. The current work relies heavily on the use of unsupervised data mining techniques to automatically build models of specific creative domains such as poetry. The proposed methods and models are flexible and they are to a large extent independent of language and style. Thus, they provide a general framework for computational or synthetic creativity in linguistic and musical domains that can be easily expanded in many ways. Applications of this work include pedagogical tools, computer games, and artistic results.Laskennallinen luovuus on tekoälytutkimuksen osa-alue, joka kehittää algoritmeja ja simulaatioita luovista ilmiöistä sekä työkaluja luovien tehtävien suorittamiseen. Tässä väitöskirjassa esitetään uusia laskennallisia menetelmiä kielellisen ja musiikillisen luovuuden alueella. Väitöskirjatutkimuksen pääpaino on ollut mahdollisimman ohjaamattomien menetelmien kehittämisessä. Toisin sanoen näiden menetelmien tulisi vaatia minimaalinen määrä käsin syötettyä tietoa kielestä, maailmasta tai tietystä erityisalasta. Väitöskirja koostuu johdanto-osasta sekä viidestä alkuperäisestä tutkimusartikkelista. Johdanto-osa esittelee laskennallisen luovuuden tutkimusalana ja käsittelee tärkeimpiä työn taustalla olevia filosofisia kysymyksiä. Tutkimusartikkelit esittelevät spesifejä menetelmiä ja algoritmeja runojen ja laulujen automaattiseen tuottamiseen. Nämä menetelmät perustuvat kielen tilastolliseen mallintamiseen, morfologiseen analyysiin ja synteesiin sekä rajoitelaskentaan. Lisäksi esitetään, kuinka rajoitelaskentaa voidaan hyödyntää laskennallisesti luovan järjestelmän transformationaalisuuden saavuttamiseen. Tällöin järjestelmä kykenee muokkaamaan omaa hakuavaruuttaan ja tavoitteitaan sisäisiä rajoitteitaan muuntelemalla. Viimeisessä artikkelissa käsitellään biosignaalimittausten yhdistämistä laskennallisen luovuuden menetelmiin ja esitellään taideinstallaatio, joka perustuu näihin ajatuksiin. Tehdyssä tutkimuksessa on hyödynnetty erityisesti ohjaamattomia tiedonlouhintamenetelmiä mallien rakentamiseksi luovuutta vaativina pidetyistä alueista, kuten runoudesta. Esitetyt menetelmät ja mallit ovat joustavia ja pitkälti riippumattomia tietystä kielestä tai tyylilajista. Siten ne tarjoavat yleisluontoisen ja helposti laajennettavissa olevan viitekehyksen laskennalliselle kielelliselle ja musiikilliselle luovuudelle. Työn sovelluksiin lukeutuvat muun muassa pedagogiset työkalut, tietokonepelit ja taiteelliset tulokset