Artificial intelligence. What works and what doesn’t

This article holds a mirror up to the community, both to provide feedback and stimulate more selfassessment. The significant accomplishments and strengths of the field are highlighted. The research agenda, strategy, and heuristics are reviewed, and a change of course is recommended to improve the field's ability to produce reusable and interoperable components. I have been invited to assess the status of progress in AI and, specifically, to address the question of what works and what does not. This question is motivated by the belief that progress in the field has been uneven, that many of the objectives have been achieved, but other aspirations remain unfulfilled. I think those of us who've been in the field for some time realize what a challenge it is to apply AI successfully to almost anything. The field is full of useful findings and techniques; however, there are many challenges that people have forecast the field would have resolved or produced solutions to by now that have not been met. Thus, the goals that I have set for this article are basically to encourage us "to look in the mirror" and do a self-assessment. I have to tell you that I'm in many places right now where people often jest about what a sorry state the field of AI is in or what a failure it was. And I don't think that's true at all. I don't think the people who have these opinions are very well informed, yet there's obviously a germ of truth in all this. I want to talk about some areas where I think the field actually has some problems in the way it goes about doing its work and try to build a shared perception with you about what most of the areas of strength are. I think there are some new opportunities owing to the fact that we have accomplished a good deal collectively, and the key funding organizations, such as the Defense Advanced Research Projects Agency (DARPA), recognize this. In addition, the Department of Defense (DOD) is increasingly relying on DARPA to produce solutions to some challenging problems that require AI technology. These significant problems create opportunities for today's researchers and practitioners. If I could stimulate you to focus some of your energies on these new problem areas and these new opportunities, I would be satisfied. At the outset, I want to give a couple of disclaimers. I'm not pretending here to do a comprehensive survey of the field. I actually participated in such an effort recently, the results of which were published in the Communications of the ACM (Hayes-Roth and Jacobstein 1994). In that effort, I tried to be objective and comprehensive. In this article, however, I'm going to try to tell you candidly and informally the way it looks to me, and I would entertain disagreement and discussion gladly. However, I think any kind of judgment is value laden. I do have some values. They're not necessarily the same as others, but I think they represent a pretty good crosssection of the viewpoints of many of the people in the field and many of the people who patronize the field (in both senses). My values are that a field ought to be able to demonstrate incremental progress, not necessarily every day, every week, but over th