This paper presents a cognitive model of the planning process. The model generalizes the theoretical architecture of the Hearsay-II system. Thus, it assumes that planning comprises the activities of a variety of cognitive “specialists.” Each specialist can suggest certain kinds of decisions for incorporation into the plan in progress. These include decisions about: (a) how to approach the planning problem; (b) what knowledge bears on the problem; (c) what kinds of actions to try to plan; (d) what specific actions to plan; and (e) how to allocate cognitive resources during planning. Within each of these categories, different specialists suggest decisions at different levels of abstraction. The activities of the various specialists are not coordinated in any systematic way. Instead, the specialists operate opportunistically, suggesting decisions whenever promising opportunities arise. The paper presents a detailed account of the model and illustrates its assumptions with a “thinking aloud” protocol. It also describes the performance of a computer simulation of the model. The paper contrasts the proposed model with successive refinement models and attempts to resolve apparent differences between the two points of view. Cognitive Science 3:275-310.

Our purpose in studying natural language understanding in conjunction with problem solving is to bring together the constraints of what formal representation can actually be obtained with the question of what knowledge is required in order to solve a wide range of problems in a semantically rich domain. We believe that these issues cannot sensibly be tackled in isolation. In practical terms we have had the benefits of an increased awareness of common problems in both areas and a realisation that some of our techniques are applicable to both the control of inference and the control of parsing. Early work on solving mathematical problems stated in natural language was done by Bobrow (STUDENT - (i]) and Chamiak (CARPS - [5]). However the rudimentary parsing and simple semantic structures used by Bobrow and Charniak are inadequate for any but the easiest problems. Our intention has been to build on B/RG Chris This work was supported by SRC grant number 94493 and an SRC research studentship for Mellish.

The Sphinx once challenged a particularly tasty-looking student of language to solve the riddle: "How is saying'My toc is turning blue,' as a request to get off my toe, similar to slamming a door in someone's face?" The poor student stammered that in both cases, when the agents are trying to communicate something, they have analogous intentions. "Yes indeed" countered the Sphinx, "but what are those intentions?" Hearing no reply, the monster promptly devoured the poor student and sat back smugly to wait for the next oral exam. The research described herein was supported primarily by the National Research Council of Canada, and also by the National Institute of Education under Contract US-N1E-C-400-76-0116, the Department of Computer Science of the University of Tor3nto, and by a summer graduate student associateship (1975) to Cohen from the International Business Machines Corporation.

Production systems designed to function and grow in environments that make large numbers of different, sometimes competing, and sometimes unexpected demands require support from their interpreters that is qualitatively different from the support required by systems that can be carefully hand crafted to function in constrained environments. In this paper we explore the role of conflict resolution in providing such support. Using criteria developed in the paper, we evaluate both individual conflict resolution rules and strategies that make use of several rules.

Traditional scheduling algorithms (using the techniques of PERT charts, decision analysis or operations rrsrarrh) require well-defined, quantitative, complete sets of constrainls*. They are insufficient for scheduling situations where the problem description is ill-defined, involving incomplete, possibly inconsistent and generally qualitative constraints. The NUDGE program uses an extensive knowledge base to debug scheduling requests by supplying typical values for qualitative constraints, supplying missing details and resolving minor inconsistencies. The result is that an informal request is converted to a complete description suitable for a traditional scheduler. To implement the NUDGE program, a knowledge representation language -- FRL-0 -- based on a few powerful generalizations of the traditional property list representation has been developed.

Reprinted as Chapter 3 (pp.250-271) of Webber & Nilsson, Readings in Artificial IntelligenceElcock, E. W. and Michie, D. (Eds.), Machine Intelligence 8, pp. 94-€“138. Ellis Horwood.

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.

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.

This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and the use of extra blocks as temporary supports and counterweights in the course of the construction. BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces.

Given an axiomatic description of its capabilities in some world, she generates her own procedures to embody these capabilities.