Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search (Blue Sky Idea)

  • David Harel Weizmann Institute of Science
  • Assaf Marron Weizmann Institute of Science
  • Ariel Rosenfeld Weizmann Institute of Science
  • Moshe Vardi Rice University
  • Gera Weiss Ben-Gurion University

Abstract

Artificial intelligence (AI) techniques, including, e.g., machine learning, multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES), including, e.g., Statecharts and scenario-based programming, is a promising development approach, offering intuitiveness, ease of enhancement, compositionality, and amenability to formal analysis. We propose an approach for integrating AI and ES techniques in developing complex intelligent systems, which can greatly simplify agile/spiral development and maintenance processes. The approach calls for automated detection of whether certain goals and sub-goals are met; a clear division between sub-goals solved with AI and those solved with ES; compositional and incremental addition of AI-based or ES-based components, each focusing on a particular gap between a current capability and a well-stated goal; and, iterative refinement of sub-goals solved with AI into smaller sub-sub-goals where some are solved with ES, and some with AI. We describe the principles of the approach and its advantages, as well as key challenges and suggestions for how to tackle them.

Published
2019-07-17
Section
Senior Member Presentation Track Papers: Blue Sky Papers