Planner

Abstract: Project controls have traditionally been focused on after-the-fact detection of variances. This thesis proposes a control system, the Last Planner system, that causes the realization of plans, and thus supplements project management’s concern for management of contracts with the management of production. The Last Planner system has previously been successfully applied by firms with direct responsibility for production management; e.g., specialty contractors. This thesis extends system application to those coordinating specialists, both in design and construction, through a series of case studies, one of which also explores the limits of unilateral implementation by specialists. In addition to the extended application, two questions drive this research. The first question is 1) What can be done by way of tools provided and improved implementation of the Last Planner system of production control to increase plan reliability above the 70% PPC level? Previous research revealed substantial improvement in productivity for those who improved plan reliability to the 70% level, consequently there is reason to hope for further improvement, possibly in all performance dimensions, especially with application across an entire project rather than limited to individual specialty firms. That question is explored in three case studies, the last of which achieved the 90% target. The second question is 2) How/Can Last Planner be successfully applied to increase plan reliability during design processes. That question is explored in an extensive case study, which significantly contributes to understanding the design process from the perspective of active control, but unfortunately does not fully answer the question, primarily because the project was aborted prior to start of construction. However, it is argued that Last Planner is especially appropriate for design production control because of the value-generating nature of design, which renders ineffective traditional techniques such as detailed front end planning and control through after-the-fact detection of variances. Issues for future research are proposed, including root cause analysis of plan failures and quantification of the benefits of increased plan reliability for both design and construction processes.

Abstract: Case-based planning is based on the idea that a machine planner should make use of its own past experience in developing new plans, relying on its memories instead of a base of rules. Memories of past successes are accessed and modified to create new plans. Memories of past failures are used to warn the planner of impending problems and memories of past repairs are called upon to tell the planner how to deal with them. Successful plans are stored in memory, indexed by the goals they satisfy and the problems they avoid. Failures are also stored, indexed by the features in the world that predict them. By storing failures as well as successes, the planner is able to anticipate and avoid future plan failures. These ideas of memory, learning and planning are implemented in the case-based planner CHEF, which creates new plans in the domain of Szechwan cooking.

Abstract: This article presents a view of planning as a task supported by a dynamic memory. This view attempts to ingegrate models of memory, learning, and planning into a single system that learns about planning by creating new plans and analyzing how they interact with the world. We call this view of planning case-based planning. A case-based planner makes use of its own past experience in developing new plans. It relies on its memory of observed effects, rather than a set of causal rules, to create and modify new plans. Memories of past successes are accessed and modified to create new plans. Memories of past failures are used to warn the planner of impending problems, and memories of past repairs are called upon to tell the planner how to deal with them. This view of planning from experience supports and is supported by a learning system that incorporates new experiences into the planner's episodic memory. This learning algorithm gains from the planner's failures as well as its successes. Successful plans are stored in memory, indexed by the goals they satisfy and the problems they avoid. Failures are also stored and indexed by the features in the world that predict them. By storing failures as well as successes, the planner is able to anticipate and avoid future plan failures. Case-based planning is aimed at improving planning behavior in three areas: failure avoidance, plan repair, and plan reuse. It also attempts gains over current learning systems, in that the learning is driven by the functional needs of a planner.

Abstract: PLANNER is a language for proving theorems and manipulating models in a robot. The language is built out of a number of problem solving primitives together with a hierarchical control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Conclusions can be drawn from these various changes in state. Goals can be established and dismissed when they are satisfied. The deductive system of PLANNER is subordinate to the hierarchical control structure in order to make the language efficient. The use of a general purpose matching language makes the deductive system more powerful.

Abstract: This analysis of the communicative acts of a senior planner during two episodes in a working day contributes to the study of planning as an interactive practice The types and forms of knowledge used and the communicative work undertaken are examined The paper identifies the way power is exercised through communication It shows planners how to make their expertise available and transparent to the clients and communities they serve