My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. Current generators only accept input that are relatively poor in information, such as feature structures or lists of propositions; they are unable to deal with input rich in information, as one might expect from, for example, an expert system with a complete model of its domain or a natural language understander with good inference ability. FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al.

A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway.

Announcement of the new Mobile Robot Competition and Exhibition, to be held annually at the AAAI National Conference on Artificial Intelligence. Announcement of the new Mobile Robot Competition and Exhibition, to be held annually at the AAAI National Conference on Artificial Intelligence.

The Association for the Advancement of Artificial Intelligence held its 1991 Spring Symposium Series on March 26-28 at Stanford University, Stanford, California. This article contains short summaries of the eight symposia that were conducted: Argumentation and Belief, Composite System Design, Connectionist Natural Language Processing, Constraint-Based Reasoning, Implemented Knowledge Representation and Reasoning Systems, Integrated Intelligent Architectures, Logical Formalizations of Commonsense Reasoning, and Machine Learning of Natural Language and Ontology.

The AAAI-90 Workshop on Qualitative Vision was held on Sunday, 29 July 1990. Over 50 researchers from North America, Europe, and Japan attended the workshop. This article contains a report of the workshop presentations and discussions.

Automated diagnosis is an important AI problem not only for its potential practical applications but also because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Current research in this area addresses many problems, including managing and structuring probabilistic information, modeling physical systems, reasoning with defeasible assumptions, and interleaving deliberation and action. Furthermore, diagnosis programs must face these problems in contexts where scaling up to deal with cases of realistic size results in daunting combinatorics. This article presents these and other issues as discussed at the First International Workshop on Principles of Diagnosis.

This article describes a task-specific, domain-independent architecture for candidate evaluation. I discuss the task-specific architecture approach to knowledge-based system development. Finally, I describe a task-specific expert system shell, which includes a development environment (Ceved) and a run-time consultation environment (Ceval). This shell enables nonprogramming domain experts to easily encode and represent evaluation-type knowledge and incorporates the encoded knowledge in performance systems.

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.

A five-year project, it is aimed at promoting cooperation among research centers, developing state-of-the art training and teaching programs, and demonstrating KBCS solutions to selected socioeconomic problems.

Review of Genetic Algorithms in Search, Optimization, and Machine Learning