Self-Explaining Agents
DOI:
https://doi.org/10.11113/jt.v63.1955Keywords:
Self-*, self-explanation, agent-capability descriptions, self-explanatory descriptions, NSM, pragmaticAbstract
This work advocate self-explanation as one foundation of self-* properties. Arguing that for system component to become more self-explanatory the underlining foundation is an awareness of themselves and their environment. In the research area of adaptive software, self-* properties have shifted into focus caused by the tendency to push ever more design decisions to the applications runtime. Thus fostering new paradigms for system development like intelligent and learning agents. This work surveys the state-of-the-art methods of self-explanation in software systems and distills a definition of self-explanation. Additionally, we introduce a measure to compare explanations and propose an approach for the first steps towards extending descriptions to become more explanatory. The conclusion shows that explanation is a special kind of description. The kind of description that provides additional information about a subject of interest and is understandable for the audience of the explanation. Further the explanation is dependent on the context it is used in, which brings about that one explanation can transport different information in different contexts. The proposed measure reflects those requirements.
References
Kephart, J., and D. Chess. 2003. The Vision of Autonomic Computing. IEEE Computer. 36(1): 41–50.
Hinchey, M.G., and R. Sterrit. 2006. Self-Managing Software. IEEE Computer. 39(2): 107–109.
Fähndrich, J., S. Ahrndt, and S. Albayrak. 2013. Towards Self-Explaining Agents. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent Systems and Computing. 221: 147–154.
Oaks, P., A. Hofstede, and D. Edmond. 2003. Capabilities: Describing What Services Can Do. Service-Oriented Computing - ICSOC 
2910(1): 1–16.
Goddard, C. 2008. Cross-Linguistic Semantics. Amsterdam: John Benjamins Publishing Company.
Chi, M. 2000. Self-explaining Expository Texts: The Dual Processes of Generating Inferences and Repairing Mental Models. Advances in Instructional Psychology. 5(1): 161–238.
Overton, J.A. 2011. Scientific explanation and computation, WS on Computational Explanations. International Joint Conference on Artificial Intelligence.
Moore, J., and W. Swartout. 1989. A Reactive Approach to Explanation. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. 2(1): 1504–1510.
Salehie, M., and L. Tahvildari. 2009. Self-Adaptive Software: Landscape and Research Challenges. ACM Transactions on Autonomous and Adaptive Systems. 4(2): 1–42.
Heckerman, D., E. Horvitz, and B. Nathwani. 1992. Toward Normative Expert Systems: Part I The Pathfinder Project. Methods of Information in Medicine. 31(2): 90–105.
Cole, W. 1989. Understanding Bayesian Reasoning via Graphical Displays. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 20(SI): 381–386.
Druzdzel, M. 1996. Qualitative Verbal Explanations in Bayesian Belief Networks. Artificial Intelligence and Simulation of Behavior Quarterly – Special Issue on Bayesian belief networks. 94: 43–54.
Müller-Schloer, C., and H. Schmeck. 2010. Organic Computing: A Grand Challenge for Mastering 
Complex Systems. It – Information Technology. 52(3): 135–141.
Müller-Schloer, C. 2004. Organic Computing–On the Feasibility of Controlled Emergence. IEEE/ACM/IFIP International Conference on Hardware/Software CoDesign and System Synthesis. 2–5.
Cheng, B., R. Lemos, et al. 2009. Software Engineering for Self-Adaptive Systems: A Research Roadmap. Software Engineering for Self-Adaptive Systems. 1–26.
Duda, R., D. Stork and P. Hart. 2000. Pattern Classification and Scene Analysis Part 1 Pattern Classification. Wiley-Interscience. Edition 2.
Leake, D. 1991. Goal-based Explanation Evaluation. Cognitive Science. 15(4): 509–545.
Ghallab, M., D. Nau, and P. Traverso. 2004. Automated Planning: Theory & Practice. Morgan Kaufmann Series in Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers.
Sutton, R., and A. Barto. 1996. Reinforcement Learning–An Introduction. Adaptive Computation and Machine Learning Series. Cambridge: The MIT Press.
Cambridge Dictionary Online. 2012. URL http://dictionary.cambrid ge.org/dictionary/british/self- explanatory?q=self-explanatory.
Peano, G. 1889. Arithmetices principia, nova methodo exposita (The Principles of Arithmetic, presented by a new method).
Glass, D. 2009. Inference to the Best Explanation: A Comparison of Approaches. In: Proceedings of the 2nd Symposium on Computing and Philosophy. 22–27.
Glass, D., 2007. Coherence Measures and Inference to the Best Explanation. In: Synthese. 157(3): 275–296.
Chajewska, U., and J. Halpern. 1997. Defining Explanation in Probabilistic Systems, Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence. 62–71.
Olsson, E. 2005. Against Coherence: Truth, Probability, and Justification. Oxford: Oxford University Press.
Olsson, E. 2002. What is the Problem of Coherence and Truth? Journal of Philosophy. 99(5): 246–272.
Fitelson, B. 2003. A probabilistic theory of coherence. Analysis. 63(3): 194–199.
Leake, D.B. 1992. Evaluating Explanations: A Content Theory. New-York: Psychology Press.
Klusch, M., U. Küster, A. Leger, D. Martin, and M. Paolucci. 2012. 4th International Semantic Service Selection Contest - Performance Evaluation of Semantic Service Matchmakers. http://www-ags.dfki.uni-sb.de/~klusch/s3/s3c-2012-summary-report.pdf. Last visited: 2013-06-30.
Masuch, N., B. Hirsch, M. Burkhardt, A. Heßler, and S. Albayrak. 2012. SeMa2: A Hybrid Semantic Service Matching Approach. Semantic Web Services. Berlin: Springer. 35–47.
Morris, C.W. 1938. Foundations of the Theory of Signs. Chicago: University of Chicago Press. 1(2): 66.
Sooriamurthi, R., and D. Leake. 1994. Towards Situated Explanation. Proceedings of the Twelfth National Conference on Artifical Intelligence. 2(1): 1492.
Coleman, J., M. O’Rourke, and D.B. Edwards. 2005. Pragmatics and Agent Communication Languages. 50–62.
Laddaga, R. 2000. Active Software. First International Workshop on Self-Adaptive Software. New-York: Springer. 11–26.
Braubach, L., A. Pokahr, and D. Moldt. 2005. Goal Representation for BDI Agent Systems. Programming Multi-Agent Systems. Lecture Notes in Computer Science. 3346(1): 44–65.
Sycara, K., M. Klusch, S. Widoff, and J. Lu. 1999. Dynamic Service Matchmaking among Agents in Open Information Environments. SIGMOD Record. 28(1): 47–53.
Grüninger, M. Hull, and S. McIlraith. 2008. A Short Overview of Flows: A First-order Logic Ontology for Web Services. Data Engineering. 31(3):1–5.
Martin, D., M. Paolucci, S. McIlraith, and M. Burstein. 2004. Bringing Semantics to Web Services: 
The OWL-S Approach. First International Workshop on Semantic Web Services and Web Process Composition (SWSWPC 2004). 26–42.
Wierzbicka, A. 1996. Semantics: Primes and Universals. Oxford: Oxford University Press.
Singh, M. 1998. Agent Communication Languages: Rethinking the Principles. IEEE Computer. 31(12): 40–47.
Swartout, G, G. Yolanda, and A. Valente. 1999. Representing capabilities of problem solving methods. IJCAI Workshop on Ontologies and Problem-Solving Methods. 1–8.
Fillmore, C. 1968. The Case for Case. New York: Holt, Rinehart & Winston.
Horridge, M., N. Drummond, J. Goodwin, A. Rector, R. Stevens, and H. Wang. 2006. The Manchester OWL Syntax. Proceedings of the 2006 OWL Experiences and Directions Workshop OWL-ED. 216(1).
Horrocks, I, O. Kutz, and U. Sattler. 2006. The even more irresistible SROIQ, Proceedings of the 10th International Confonference on Principles of Knowledge Representation and Reasoning (KR 2006), AAAI Press. 57–67.
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