Self-Explaining systems have the ability to describe their functionality in a con- text depending and structured way. Self-explanatory descriptions thus help rea- soners to better understand these systems. Becoming more self-explanatory re- quires not only work on the descriptions, the used languages and description paradigms but also on the reasoners, which utilize such descriptions and tools to create them. This places the research questions of this workshop in between the Service, the Agent and the Artificial Intelligence community.
Services and their descriptions have been well researched and researchers investigated languages like OWL and OWL-S. Such languages are used to describe functionality which enables reusability and adaptability of service oriented architectures with reasoners like FACT++ or Pellet. This is one reason why service descriptions become more self-explanatory these days.
Agents on the other hand use less self-explanatory description. The agent community focus on developing more sophisticated planning algorithms (e.g., Fast Downward Stone Soup) and heuristics to select the right functionality to become part of the plan.
Both paradigms cope with situations where complex systems are build upon descriptions of functionalities, which are more or less self-explanatory. Artificial Intelligent reasoners are used to analyze those descriptions and reason whether the functionality satisfies a given requests or preconditions and effects. Combining the strong suits of both research areas, bear opportunities for self-explaining agents.
This workshop analyzes state-of-the-art in regards to the use of self-explanatory descriptions used by service matcher, agent planner as well as how to extract heuristics by reasoning upon self-explanatory descriptions and semantic description language with the goal of building more loosely coupled, dynamic and adaptive software. Thus we welcome practical applications as well as theoretical foundations as contributions for this workshop.
Thus self-explanations rises the following questions:
- How to describe a functionality to become more self-explaining?
- How to improve AI reasoners to better understand self-explaining functionality descriptions?
- What are requirements to languages used to create more self-explaining descriptions of functionality?
Evaluation frameworks for such approaches are represented by research contests like the Semantic Service Selection Contest1 or the International Planning Com- petition.
Topics of interest
This workshop deepen (but is not limited to) the following research areas:
– Reasoning on Semantic Descriptions
– Languages for Semantic Descriptions
– AI Methods on Adaption and Semantic Reasoning
– IOPE/OWL-S and other Description Paradigms
– Semantic Service Matchmaking
– Service Planning
– AI Planning using semantic descriptions
– Reinforcement Learning on functionality descriptions – Requirements of Self-Explaining Systems
– Self-Configuration through Self-Explanation

The workshop will consist of paper presentation of the accepted papers and a follow up discussion on the research questions of the workshop.

Important Dates
– Paper submission: 19th January 2015
– Notification: 23th February 2015
– Camera-ready: 9th March 2015
– Workshop date: 3rd - 5th June 2015

Program Commitee
– David B. Leake, Indiana University
– Katia Sycara, Carnegie Mellon University
– Birgitta Knig-Ries, University of Jena
– Maria Ganzha, University of Gdask
– Johannes F ̈ahndrich: DAI-Labor, Technische Universit ̈at Berlin
– Sebastian Ahrndt: DAI-Labor, Technische Universit ̈at Berlin
– Benjamin Hirsch: EBTIC, Khalifa University
– Marco Lu ̈tzenberger: DAI-Labor, Technische Universit ̈at Berlin
– Michael Kaisers: Centrum Wiskunde and Informatica, Amsterdam