The Fredkin Challenge in AI Research and Applications at Chandrasekaran, B. see nlso Hendler, Match, 2(2): Summer 1981, 30-33.

My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. It was motivated by two difficulties in scaling up existing generators. 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. Current generators also have a very restricted knowledge of language -- indeed, they succeed largely because they have few syntactic or lexical options available (McDonald 1987) -- and they are unable to cope with more knowledge because they deal with interactions among the various possible choices only as special cases. To address these and other issues, I built a system called FIG (flexible incremental generator). FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Computation is done by spreading activation across the network, supplemented with a small amount of symbolic processing. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al. 1988).

General Electric's Research and Elscint (Hackensack, NJ), a manufacturer Johnson Controls (Milwaukee, WI) Development Center (Schenectady, of medical imaging systems, has has begun deployment of a knowledge-based NY) has developed an expert system begun offering its customers a service engineering application which is being used to increase the option based on expert systems. The to increase the productivity of the speed of design of new jet engines, MasterMind system delivers troubleshooting engineering design function. The system, called Engineous, on laptop or desktop computers. The General (Menlo Park, CA), is conveyor for further processing. It problems and recommends solutions objects have become rotated.

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.

The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, cost-effective schedules in dynamic and stochastic manufacturing environments.

. It is argued that Situated Agents should be designed using a unitaryon-line computational model. The Constraint Net model of Zhang and Mackworth satisfiesthat requirement. Two systems for situated perception built in our laboratory are describedto illustrate the new approach: one for visual monitoring of a robot’s arm, the other forreal-time visual control of multiple robots competing and cooperating in a dynamic world.First proposal for robot soccer.Proc. VI-92, 1992. later published in a book Computer Vision: System, Theory, and Applications, pages 1-13, World Scientific Press, Singapore, 1993.

In UAI-92, pp. 121–129.

Artificial Intelligence, 5 (2-4), 265-78.

In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence., Vol. 1, pp. 285-293. Wiley. Links to a variety of constraint satisfaction articles. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, Volume 25, Issue 1, January 1985, Pages 65–74 (http://www.sciencedirect.com/science/article/pii/0004370285900414). Constraint Satisfaction. Technical Report, University of British Columbia, 1985 (http://dl.acm.org/citation.cfm?id=901711). The logic of constraint satisfaction. Artificial Intelligence, Volume 58, Issues 1–3, December 1992, Pages 3–20 (http://www.sciencedirect.com/science/article/pii/000437029290003G). The complexity of constraint satisfaction revisited. Artificial Intelligence, Volume 59, Issues 1–2, February 1993, Pages 57–62 (http://www.sciencedirect.com/science/article/pii/000437029390170G). Parallel and distributed algorithms for finite constraint satisfaction problems. Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing, 1991 (https://ieeexplore.ieee.org/document/218214). Hierarchical arc consistency: exploiting structured domains in constraint satisfaction problems. Computational Intelligence, Volume 1, Issue 1, pages 118–126, January 1985 (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1985.tb00064.x). Knowledge structuring and constraint satisfaction: the Mapsee approach. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume:10, Issue: 6) (https://ieeexplore.ieee.org/abstract/document/9108?section=abstract). Chapter 2 – Constraint Satisfaction: An Emerging Paradigm. Foundations of Artificial Intelligence, Volume 2, 2006, Pages 13–27. Handbook of Constraint Programming (http://www.sciencedirect.com/science/article/pii/S1574652606800064).

In AAAI-92, pp. 816–822.