KI - Künstliche Intelligenz

German Journal on Artificial Intelligence - Organ des Fachbereichs "Künstliche Intelligenz" der Gesellschaft für Informatik e.V.

ISSN: 0933-1875 (Print) 1610-1987 (Online)

Description

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

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Intentional Forgetting

Current trends, like the digital transformation and ubiquitous computing, yield in a massive increase in available data and information. On the other hand, in Artificial Intelligence systems, capacity of knowledge bases is limited due to computational complexity of many inference algorithms. Consequently, continuously sampling information and unfiltered storing in knowledge bases does not seem to be a promising or even feasible strategy. In human evolution, learning and forgetting have evolved as advantageous strategies for coping with available information by adding new knowledge to and removing irrelevant information from the human memory. Learning has been adopted in AI systems in various algorithms and applications. Forgetting, however, especially intentional forgetting,
has gained much less attention, despite the potential forgetting can provide for coping with the seemingly ever increasing information overload observed in current systems.

The aim of this special issue is to provide an overview on available theories and methods as well as ongoing research on forgetting and intentional forgetting relevant for the theory and practice of AI and AI systems from different perspectives like, e.g., knowledge representation, cognition, ontologies, reasoning, machine learning, self-organization, distributed AI, etc.

Smart Production

Smart Production is key for industrial and societal growth, providing opportunities for designing our future work life. This special issue targets to illustrate the requirements and challenges of a smart production which targets not only cyber-physical systems as enabler technology and AI-based robots in production but also solutions for supporting humans in production. For a beneficial use of AI, the necessary infrastructure must be provided. This includes currently used communication protocols and proposed architectures, as well as production systems. Also, contributions concerning the role of AI in context of robots and human workers, respectively, and industrial use-cases are invited.

Cognitive Reasoning

Human reasoning or the psychology of deduction is well researched in cognitive psychology and in cognitive science. There are many findings which are based on experimental data about human reasoning tasks. Among others, models for the Wason selection task or the suppression task are discussed by psychologists and cognitive scientists. However, only few of these models are computational and often models are modified when applied to a different task. Automated deduction, on the other hand, mainly focuses on the automated proof search in formal, logical calculi. Indeed, there is tremendous success during the last decades, and automated deduction systems are used in many industrial applications. However, most automated deduction systems are not really concerned with human reasoning tasks. Recently, a coupling of the areas of cognitive science and automated reasoning is addressed in several approaches. For example, there is increasing interest in modeling human reasoning tasks within automated reasoning systems based on answer set programming, deontic logic, abductive logic programming, and various other AI approaches. This special issue is aiming to foster the synergies between cognitive science and automated deduction.

Reintegrating Artificial Intelligence and Robotics

A major goal of Artificial Intelligence (AI) is to create autonomous, intelligent machines, or robots, that can sense their surroundings, reason about what they have perceived, plan their next actions, and act accordingly to accomplish their tasks. Moreover, robots should be able to learn from their own experience (including interactions with other agents) and adapt to changing conditions within their environments over their lifetime. Several of these challenges have been addressed and investigated by different subdisciplines of AI including Perception, Knowledge Representation & Reasoning, Planning, Interaction, and Learning. However, although these research areas have made tremendous progress over the last decade, their developed methods and techniques have not always been reintegrated into situated robot systems and deployed in the real world. It is the aim of this Special Issue on "Reintegrating Artificial Intelligence and Robotics" to emphasize that the reintegration of AI methods is a non-trivial factor in the design, development and evaluation of robot systems. In particular, we are interested in work related to both fully-integrated robots systems that use methods of AI to perform complex tasks in realistic environments and fundamental AI techniques that have the potential to transform the capabilities of robot systems, but which not been convincingly demonstrated in integrated systems.

Artificial Intelligence in Games

The special issue focuses on artificial intelligence (AI) methods applied in and for different types of games (e.g., board games, video games, serious games). Games have been shown to be the perfect testbed for advanced AI methods. AI in games is now a well established research area with two dedicated conferences and as well as a dedicated journal. Especially deep learning methods have recently proven to beat the best human experts in Atari video games and the game Go. Other methods such as evolutionary computation have been shown to allow complete new types of games through procedural content generation. While there has been much progress in game AI recently, some games such as StarCraft remain beyond even the most advanced AI algorithms. The goal of this special issue is to present a survey of the current research in Game AI and emerging trends in this area.

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