See also: Stanford Heuristic Programming Project Memo HPP-77-25Proc. IJCAI-77: Fifth International Joint Conference on Artificial Intelligence, pp 1014-1029

Austin Tate Department of Artificial Intelligence University of Edinburgh Edinburgh Scotland Abstract Procedures for optimization and resource allocation in Operations Research first require a project network for the task to be specified. The specification of a project network is at present done in an intuitive way. AI work in plan formation has developed formalisms for specifying primitive activities, and recent work by Sacerdoti (1975a) has developed a planner able to generate a plan as a partially ordered network of actions. The "planning: a joint AI/OR approach" project at Edinburgh has extended such work and provided a hierarchic planner which can aid in the generation of project networks. This paper describes the planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain. Current work in Operations Research (OR) and Artificial Intelligence (AI) has concentrated on different aspects of the problem. We have taken an interdisciplinary approach in the hope that this will lead to a development of both these aspects. In the OR approach, the planning process falls into two stages. The constituent "jobs" of a plan are specified together with their precedence relationships (i.e.

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In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

Amsterdam: North-Holland

In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

We describe work on a project aimed at producing an interactive program for the construction of project networks (e.g. for house building tasks). To do this we have developed a planner which can form plans epresented as a partiQlly ordered netwo k of actions. A formalism (TF) is given for describing a domain in a hierarchic fashion. The representation of plans and the planner (NONLIN) are fully explained. During this work, a general technique was developed for answering queries about Q situation when the informQtion about the world is stored as a partiQlly ordered network of alterations made to some initial situation. We give a general procedure for recognizing and correcting for interactions between actions in the network. This is based on an analysis of the goal structure of the problem. The work is compared to that of Sacerdoti (l975a) who pioneered the techniques of planning using plans represented as partially ordered networks of actions.

Abstract: A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user’s view and access data according to the system’s view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query.IBM Journal of Research and Development 20:4, pp. 336-357.

Boitet, C. 1977 Where Does GETA Stand at the Beginning of Brinkmann, K.-H.

In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.