Distributed intelligent control and management (DICAM) applications and support for semi-automated development

We have recently begun a 4-year effort to develop a new technology foundation and associated methodology for the rapid development of high-performance intelligent controllers. Our objective in this work is to enable system developers to create effective real-time systems for control of multiple, coordinated entities in much less time than is currently required. Our technical strategy for achieving this objective is like that in other domain-specific software efforts: analyze the domain and task underlying effective performance, construct parametric or model-based generic components and overall solutions to the task, and provide excellent means for specifying, selecting, tailoring or automatically generating the solution elements particularly appropriate for the problem at hand. In this paper, we first present our specific domain focus, briefly describe the methodology and environment we are developing to provide a more regular approach to software development, and then later describe the issues this raises for the research community and this specific workshop

Should the Psychiatrist Be Hospitalized?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68096/2/10.1177_002076407502100212.pd

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

The structure of concepts

The application of the term concept, though widespread, Is varied and inexact. The Important role of concepts Is seen throughout the areas of psychology, education, and artificial intelligence. A brief survey is made of the meanings of concept evidenced in psychological research in concept attainment, word association and semantic mediation, and information processing. From these data, desiderata are developed for an operational definition of concept. Finally, a definition meeting these criteria is offered. The term systemic concept is thus Introduced. This definition reflects the two principal characteristics of concepts uncovered: concepts as data and concepts as processes. It is suggested that the systemic concept offers a framework for analysis of diverse psychological problems and will facilitate comparison among distinct conceptual skills

Uniform representations of structured patterns and an algorithm for the induction of contingency-response rules

Many events (patterns) may be described by structural (conjunctive relational) representations, and general computational behavior may be represented in terms of a set of grammatical rules (productions, transformations) relating two such event representations as contingency and response components. Uniform representations and graphs of structural descriptions and rules are introduced. An abstraction of a set of uniform representations corresponds to a common subgraph of the corresponding uniform graphs. Every rule F = [(∀x1 ,…, xn) C(x1 ,…, xn) ⇒ R(x1 ,…, xn)] which can be induced from a training set I = {(Ci , Ri): i= 1,…, N} of contingency—response (input—output) pairs is identified with a common subgraph of the uniform graphs of the causal inferences Ci ⇒ Ri. A general learning problem is formulated for which three cases are distinguishable on the basis of if and how substitutions from input to output patterns are to be made. Category (unary) and n-ary predicate learning in this framework are discussed. Examples of rule learning applications are drawn from the domains of transformational grammar. The properties (both desirable and undesirable) of the proposed approach and the differences between it and previous approaches are also considered