In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from available information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular query of a user. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of such argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts containing different topics and demonstrate the benefit of semantic similarity measures as well as the relevance of structural aspects.

Self-driving vehicles have been proposed as a solution for the rapidly increasing number of fatal traffic accidents, which now claim a staggering 1.3 million casualties each year. While we have made strides in advancing self-driving technology, we have yet to explore at length how autonomous vehicles will be programmed to deal with situations that endanger human life, according to a new study published in Frontiers in Behavioral Neuroscience. To understand how self-driving cars might make these judgments, the researchers looked at how humans deal with similar driving dilemmas. A study examining the ethics behind decisions self-driving cars make has found that the majority of people will not agree with guidelines drawn up by an ethics committee. When faced with driving dilemmas, people show a high willingness to sacrifice themselves for others, make decisions based on the victim's age and swerve onto sidewalks to minimize the number of lives lost. Ethical guidelines tend to disagree with human instincts in this case, which dictate that no life should be valued above another.

Case-based reasoning (CBR) is a problem-solving paradigm that uses exemplars or previous solutions to solve new problems (Aamodt and Plaza 1994; Kolodner 1993). First, CBR can reduce search. Solution reuse is compatible with a wide range of problem-solving methods, so a CBR component can be used in many types of problem-solving system. When similar problems recur, CBR can significantly improve performance. This performance improvement can be particularly significant for problems with large search spaces, such as planning and design (Bergmann and Wilke 1996; Branting and Aha 1995; Veloso 1994; Koton 1988).

The Third International Conference on Case-Based Reasoning was held at the Seeon Monastery, Bavaria, 27 to 30 July 1999. About 120 researchers from 21 countries attended. The conference included 4 workshops; 3 invit-ed talks; 24 technical presentations; a poster session; and an Industry Day, where the focus was on mature technologies and applications in industry.