Koala: capture, share, automate, personalize business processes on the web

  • Authors:

  • Greg Little;Tessa A. Lau;Allen Cypher;James Lin;Eben M. Haber;Eser Kandogan

  • Affiliations:

  • MIT CSAIL, Cambridge, MA;IBM Almaden Research Center, San Jose, CA;IBM Almaden Research Center, San Jose, CA;IBM Almaden Research Center, San Jose, CA;IBM Almaden Research Center, San Jose, CA;IBM Almaden Research Center, San Jose, CA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2007

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Abstract

We present Koala, a system that enables users to capture, share, automate, and personalize business processes on the web. Koala is a collaborative programming-by-demonstration system that records, edits, and plays back user interactions as pseudo-natural language scripts that are both human- and machine-interpretable. Unlike previous programming by demonstration systems, Koala leverages sloppy programming that interprets pseudo-natural language instructions (as opposed to formal syntactic statements) in the context of a given web page's elements and actions. Koala scripts are automatically stored in the Koalescence wiki, where a community of users can share, run, and collaboratively develop their "how-to" knowledge. Koala also takes advantage of corporate and personal data stores to automatically generalize and instantiate user-specific data, so that scripts created by one user are automatically personalized for others. Our initial experiences suggest that Koala is surprisingly effective at interpreting instructions originally written for people.