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    <title language="eng">Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies</title>
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    <parentTitle language="eng">Digitale Welt</parentTitle>
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    <title language="eng">The scenario coevolution paradigm</title>
    <abstract language="eng">Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018).</abstract>
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    <subTitle language="deu">adaptive quality assurance for adaptive systems</subTitle>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
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      <last_name>Gabor</last_name>
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      <first_name>Bernhard</first_name>
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      <first_name>Cornel</first_name>
      <last_name>Klein</last_name>
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      <first_name>Horst</first_name>
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      <first_name>Reiner</first_name>
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      <first_name>Jan</first_name>
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      <first_name>Marc</first_name>
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    <title language="eng">The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline</title>
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    <title language="eng">Uncertainty-based out-of-distribution classification in deep reinforcement learning</title>
    <abstract language="eng">Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. As a first step towards a solution, we consider the problem of detecting such data in a value-based deep reinforcement learning (RL) setting. Modelling this problem as a one-class classification problem, we propose a framework for uncertainty-based OOD classification: UBOOD. It is based on the effect that an agent’s epistemic uncertainty is reduced for situations encountered during training (in-distribution), and thus lower than for unencountered (OOD) situations. Being agnostic towards the approach used for estimating epistemic uncertainty, combinations with different uncertainty estimation methods, e.g. approximate Bayesian inference methods or ensembling techniques are possible. Evaluation shows that the framework produces reliable classification results when combined with ensemble-based estimators, while the combination with concrete dropout-based estimators fails to r eliably detect OOD situations.</abstract>
    <parentTitle language="eng">Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART</parentTitle>
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    <abstract language="deu">In cooperative game theory bargaining games refer to situations where players can agree to any one of a variety of outcomes but there is a conflict on which specific outcome to choose. However, the players cannot impose a specific outcome on others and if no agreement is reached all players receive a predetermined status quo outcome. Bargaining games have been studied from a variety of fields, including game theory, economics, psychology and simulation based methods like genetic algorithms. In this work we extend the analysis by means of deep multi-agent reinforcement learning (MARL). To study the dynamics of bargaining with reinforcement learning we propose two different bargaining environments which display the following situations: in the first domain two agents have to agree on the division of an asset, e.g., the division of a fixed amount of money between each other. The second domain models a seller-buyer scenario in which agents must agree on a price for a product. We empirica lly demonstrate that the bargaining result under MARL is influenced by agents’ risk-aversion as well as information asymmetry between agents.</abstract>
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      <first_name>Jaap van den</first_name>
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