The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL.

The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL.

We propose an approach for meeting real-time constraints in AI systems that views (1) time as a resource that should be considered when making control decisions, (2) plans as ways of expressing control decisions, and (3) approximate processing as a way of satisfying time constraints that cannot be achieved through normal processing. In this approach, a real-time problem solver estimates the time required to generate solutions and their quality. This estimate permits the system to anticipate whether the current objectives will be met in time. The system can then take corrective actions and form lower-quality solutions within the time constraints. These actions can involve modifying existing plans or forming radically different plans that utilize only rough data characteristics and approximate knowledge to achieve a desired speedup. A decision about how to change processing should be situation dependent, based on the current state of processing and the domain-dependent solution criteria. We present preliminary experiments that show how approximate processing helps a vehicle-monitoring problem solver meet deadlines and outline a framework for flexibly meeting real-time constraints.

Citing the confusing statements in the AI literature concerning the relationship between branch and bound (B&B) and heuristic search procedures were present a simple and general formulation of B&B which should help dispel much of the confusion. We illustrate the utility of the formulation by showing that through it some apparently very different algorithms for searching And/Or trees reveal the specific nature of their similarities and differences. In addition to giving new insights into the relationships among some AI search algorithms, the general formulation also provides suggestions on how existing search procedures may be varied to obtain new algorithms.

In Kanal, L. N. and Kumar, V. (Eds.), Search in Artificial Intelligence, chap. 1, pp. 1–27. Springer-Verlag.

An important task in postal automation technology is determining the position and orientation of the destination address block in the image of a mail piece such as a letter, magazine, or parcel. Analysis of physical characteristics of mail pieces indicates that in order to automate the address finding task, several different image analysis operations are necessary. Some examples are locating a rectangular white address label on a multicolor background, progressively grouping characters into text lines and text lines into text blocks, eliminating candidate regions by specialized detectors (for example, detecting regions such as postage stamps), and identifying handwritten regions. Described here are several operations, their utility as predicted by statistics of mail piece characteristics, and the results of applying the operations to a task set of mail piece images.

In the restaurant script, notated as $RESTAURANT, the roles might directly to the United Press International Yale researchers explored intentionality include customer, waitress, and cook; news wire and could skim news One of the earliest programs to the props could be a menu, table, and stories in dozens of different domains, embody goals and plans within the silverware; the locations could be the and produce summaries in several languages. CD paradigm was Jim Meehan's bar, dining area, and kitchen; and the On the DEC-20 (which by TALESPIN, which made up stories events would include arriving, seating, 1978 had replaced the PDP-101, similar to the fables of Aesop.

AI research are discussed This article is individuals outside the field. Even Of course, linguists have never an introduction to Scientific DataLink's AI'S practitioners are somewhat confused thought of their field as having much microfiche publication of the Yale AI about what AI really is. to do with AI at all. However, as technical reports In this context, examples Is AI mathematics? A great many money for linguistics has begun to of research conducted at the Yale AI researchers believe strongly that disappear and money for AI has Artificial Intelligence Project relating to knowledge representations used in AI increased, it has become increasingly each of the research problems is presented. Suddenly, theories of know how the answer will turn out language that were never considered even before they have figured out by their creators to be process models what exactly the questions are. They at all are now proposed as AI models.

This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.

This report contains a historical perspective on previous Distributed Artificial Intelligence Workshops, highlights of the roundtable discussions, and a collection of research abstracts submitted by the participants.