their un-debuggability, and their inability to "explain" their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value. .. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence.

No abstract

Automated composition of Web services or the process of forming new value added Web services is one of the most promising challenges in the semantic Web service research area. Semantics is one of the key elements for the automated composition of Web services because such a process requires rich machine-understandable descriptions of services that can be shared. Semantics enables Web service to describe their capabilities and processes, nevertheless there is still some work to be done. Indeed Web services described at functional level need a formal context to perform the automated composition of Web services. The suggested model (i.e., Causal link matrix) is a necessary starting point to apply problem-solving techniques such as regression-based search for Web service composition. The model supports a semantic context in order to find a correct, complete, consistent and optimal plan as a solution. In this paper an innovative and formal model for an AI planning-oriented composition is presented.