As a first step, a system has been implemented to assist in the trial-anderror process of developing new models and techniques for quantitative interpretation of well logs. The user interface exploits graphical techniques to enable petroleum scientists to describe their models in the natural concepts of the domain. The resulting specification can be implemented in any of several different target languages. The system is in active use by petroleum scientists, who find that it has significantly reduced the time to get feedback on hypothesized models.
AI activities are also being pursued at other Schlumberger locations, often jointly with SDR The locations related to logging and interpretation include: Schlumberger-Doll Research, Ridgefield, Connecticut (Contact: Peter Wu'l); Schlumberger Well Services, Austin, Texas (Contact: Scott Gut/my); Schlumberger Well Services, Houston, Texas (Contact: Scott Ma&s); Nippon Schlumberger, K K, Tokyo, Japan (Contact: Dennzs O'NezU); I&ude et Production Schlumbcraer. Other Schlumberger companies involied in Ai research include! Expert Systems Current work in expert, systems is concerned with developing techniques for building more robust and versatile log interpretation systems. One shortcoming of "first generation" expert systems, such as the Dipmeter Advisor, is their inability to reason about the task that they attempt to perform. Any description of the overall task is usually procedurally encoded and unavailable for examination.
The results of artificial intelligence research are often important to other areas than computer science itself. One area which presents a wide variety of potential applications of artificial intelligence tecniques, is the area of software production. A well knoun role of artificial intelligence in software technology has been in the area of program synthesis; several experimental systems based on different methodological approaches have been deveLoped in the past (Barstou (1979); BarteLs et al. (1981); Green (1977); Green and Barstou (1978); Green et al. (1979); Manna and Waldinger (1979); Smith (1981)). At the Milan Polytechnic Artificial Intelligence Project, the BIS system (Caio et al. (1982)) based on an approach oriented to problem reduction methodology been developed.
Designing algorithms requires diverse knowledge about general problem-solving, algorithm design, implementation techniques, and the application domain. The knowledge can come from a variety of sources, including previous design experience, and the ability to integrate larowledge from such diverse sources appears critical to the success of human algorithm designers. Such integration is feasible in an automatic design system, especially when supported by the general problem-solving and learning mechanisms in the Soar architecture. Our system, Designer-Soar, now designs several simple generate-and-test and divide-and-conquer algorithms. The system already uses several levels of abstraction, generalizes from examples, and learns from experience, transferring knowledge acquired during the design of one algorithm to aid in the design of others.