Case-based Planning Research Articles

MODELER: Incorporating experiences to support model formulation—A case-based planning approach

Abstract Model formulation is key to most mathematical modeling tactics. The enormous amount of time needed to formulate problems is well documented. In this article, we focus on the three elements of model formulation—experience, planning, and opportunism. These elements are used in the implementation of MODELER, a case-based planning system that integrates case-based reasoning methods and an opportunistic control in a blackboard data architecture. An analysis of expert verbalizations provided significant inputs to the design of MODELER.

Episodic cases and real-time performance in a case-based planning system

Abstract In this article, we discuss COOKIE, an integrated planning, execution, and learning system that operates in the domain of meal planning and preparation. Its episodic representation scheme is used to avoid brittleness in planning, help attain real-time performance, and simplify the case acquisition process. An episodic representation of an event is one that allows reconstruction of the event—all of the actions, when they happened, what facts were true, and so forth. In COOKIE, this is a set of propositions in a temporal logic corresponding to the initial conditions, end conditions, goals, actions, and observations taken during the preparation of a meal. This representation is usable for a range of reasoning tasks: plan generation and projection, plan recognition, explanation, and failure recovery. This representation also promotes efficient real-time performance. The details present in the episode are maintained in the generated plans, providing the necessary information to schedule actions and observations before plan execution begins. We describe how our execution monitor was implemented to take advantage of this information while performing in an efficient manner.

A system for case-based process planning

A new approach to automated process planning, integrating generative and case-based planning methods, is presented. Unlike most of the existing Artificial Intelligence (AI) based process planning systems, this approach makes use of a dynamic knowledge base containing old cases, i.e. part descriptions and corresponding process plans. This knowledge base grows with experience and therefore may reduce periodic human maintenance of the system. Advantages of this approach may be an increase of the planning quality and a concurrent reduction of the planning expense. Furthermore, it enables an automated improvement of similarity metrics which can be regarded as learning.

Book review: Case-Based Planning: Viewing Planning as a Memory Task by Krlstian J. Hammond (Academic Press, 1989)

Much of the research at the Yale AI Lab relates to the investigation of memory. Learning and reminding form the basis of case-based reasoning in general and case-based planning (CBP) in particular. By remembering past successes and failures in planning, a planner may reduce much of its work to retrieving previously used plans and applying the to the situation at hand.

Case-based planning: A framework for planning from experience

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.

A case mix simulation decision support system model for negotiating hospital rates.

The institution of prospective payment systems by many health care insurers has drawn increased attention to case-based financial planning in hospitals. When hospital revenues are directly linked to patient diagnoses rather than to the types and quantities of services supplied to patients, managers must be aware of the financial implications of different case mixes and must be prepared to influence insurers' price structures. A case-based financial planning model is presented here for the purpose of assisting managerial decision making in the strategic areas of case mix planning and pricing. The computerized model characterizes hospitals as product manufacturers, the product being discharged patients. Diagnosis serves to differentiate the "products"; however, diagnoses are grouped by payor and similar treatment cost experiences to create a limited set of managerially meaningful case types. Diagnostic and treatment costs are also aggregated to facilitate the modeling of the hospital production process. The computerized model projects the number of patients of each case-type and total patient volume, based on estimated patient volume growth rates. The model also projects prices and contribution margins for each case-type, as well as total contribution to hospital overhead. Testing the model with a hypothetical example of a hospital strategic planning problem demonstrates the model's potential as a decision-making aid in case mix planning and case-type pricing. It also reveals several model shortcomings that require further developmental effort.