AIpanel discussion at AAAI-84, in which I was one of the panelists, appeared in the Fall, 1985 issue of AI Magazine, since due to some communication gap I wasn't aware that the panel discussion was going to be published and I hadn't had a chance to proofread my section of the transcript. I was rather unhappy when I read the section that contained my remarks: Perhaps because of an accent that would not vanish after 20 years in this country, my remarks were, in significant places, embarrassingly garbled by the transcriber ("the most performed paradigmatic change"?, "AI has been the whole expectation of the problem"?, "Knowledge use invalidities has been the cause of misunderstanding"?), and in other places, the crucial "not" had been omitted or added, completely changing my intended meaning, "not" being generally very unforgiving in this regard (where I had said, "The problem is underestimation of the problems of multiplicity of generic knowledge structures," "is" appears as "isn't;" I am pretty sure I didn't say, "I also believe that faster architectures could do the trick," since at that stage in my talk, I was criticizing the belief that what it takes is faster architectures, while crucial epistemic problems remained unsolved). Perhaps it is best to outline the main points of my panel presentation to make clear what I really said (this time without an accent and slowly): 1. AI has already made significant paradigmatic contributions by fostering the idea of cognition as computation. This notion is bound to have far-reaching consequences to philosophy and psychology. This is a weak theory of mind (or mental architecture) in the sense that it says something about organization, but doesn't make any strong commitment about content.

This research was motivated by the widely held belief that constructing an automatic program synthesis system that can accept a high-level description of a problem for an arbitrary domain and generate code for the problem completely automatically is pragmatically impossible. However, by focusing on a well-defined domain, it is possible to incorporate sufficient knowledge within a system so that it can communicate with an end user at the level of his(her) application and automatically generate a program from a problem specification. Such knowledge-based systems often employ a catalog of transformational rules that progressively refine an abstract specification into a concrete implementation. A major research issue in such systems is how to increase the efficiency of the systems by controlling the application of rules and avoiding repetitive traversal of the search space. In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and The knowledge base consists of three subcomponents: a concept dictionary, a library of reusable components, and a layered rule base.

The accuracy of an automated reading tutor in tracking the reader’s position is affected by phenomena at the frontier of the speech recognizer’s output as it evolves in real time. We define metrics of real-time tracking accuracy computed from the recognizer’s successive partial hypotheses, in contrast to previous metrics computed from the final hypothesis. We analyze the resulting considerable loss in real-time accuracy, and propose and evaluate a method to address it. Our method raises real-time accuracy from 58% to 70%, which should improve the quality of the tutor’s feedback.

Reading tutors can provide an unprecedented opportunity to collect and analyze large amounts of data for understanding how students learn. We trained models of oral reading prosody (pitch, intensity, and duration) on a corpus of narrations of 4558 sentences by 11 fluent adults. We used these models to evaluate the oral reading prosody of 85,209 sentences read by 55 children (mostly) 7-10 years old who used Project LISTEN's Reading Tutor during the 2005-2006 school year. We mined the resulting data to pinpoint the specific common syntactic and lexical features of text that children scored best and worst on. These features predict their fluency and comprehension test scores and gains better than previous models. Focusing on these features may help human or automated tutors improve children’s fluency and comprehension more effectively.

In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and AI (hierarchical planning, analogical reasoning). Based on this framework, I constructed a prototype system, APU, that can synthesize UNIX shell scripts from a high-level specification of problems typically encountered by novice shell programmers. An empirical evaluation of the system's performance points to certain criteria that determine the feasibility of the derivational analogy approach in the automatic programming domain when the cost of detecting analogies and recovering from wrong analogs is considered.

ECAI, Pisa 1984) to facilitate the decompiling of wealth and power.

And even if verification to be accommodated within the SPIV paradigm. But until were possible it would not contribute very much to the such time as we find these learning algorithms (and I development of production software. Hence "verifiability don't think that many would argue that such algorithms must not be allowed to overshadow reliability. Scientists will be available in the foreseeable future) we must face should not confuse mathematical models with reality." the prospect of systems that will need to be modified, in AI is perhaps not so special, it is rather an extreme nontrivial ways, throughout their useful lives. Thus incremental and thus certain of its characteristics are more obvious development will be a constant feature of such than in conventional software applications. Thus the SPIV software and if it is not fully automatic then it will be part methodology may be inappropriate for an even larger class of the human maintenance of the system. I am, of course, of problems than those of AI. not suggesting that the products of say architectural design I have raised all these points not to try to deny the (i.e., buildings) will need a learning capability. Nevertheless, worth of Mostow's ideas and issues concerning the design a final fixed design, that remains "optimal" in a process, but to make the case that such endeavors should dynamically changing world, is a rare event.The similarity also be pursued within a fundamentally incremental and between AI system development and the design of more evolutionary framework for design. The potential of the concrete objects is still present, but it is, in some respects, RUDE paradigm is deserving of more attention than it is rather tenuous I admit.

What are the powerful new ideas in knowledge based design? What important research issues require further investigation? Perhaps the key research problem in AI-based design for the 1980's is to develop better models of the design process. A comprehensive model of design should address the following aspects of the design process:the state of the design ; the goal structure of the design process;design decisions; rationales for design decisions; control of the design process; and the role of learning in design. This article presents some of the most important ideas emerging from current AI research on design especially ideas for better models design. It is organized into sections dealing with each of the aspects of design listed above.