Case-based Planner Research Articles

Maintenance of Plan Libraries for Case-Based Planning: Offline and Online Policies

Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. The quality of such a plan library critically influences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective. In this paper, we formally define the problem of maintaining a library of cases, discuss which criteria should drive the maintenance, study the computational complexity of the maintenance problem, and propose offline techniques to reduce an oversized library that optimize different criteria. Moreover, we introduce a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. Finally, we experimentally show the practical effectiveness of the offline and online methods for reducing the library.

Model-Lite Case-Based Planning

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a ``skeletal plan'' and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison to a state-of-the-art case-based planner that depends on complete domain models.

Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care

This paper presents an intelligent environment developed for monitoring patients’ health care in execution time in hospital environments. The CBPMP (case-based planner for monitoring patients) is an autonomous deliberative case-based planner designed to plan the nurses’ working time dynamically, to maintain the standard working reports about the nurses’ activities, and to guarantee that the patients assigned to the nurses are given the right care. The planner operates in wireless devices and is integrated with complementary software into an intelligent environment, named AmI-P (Ambient Intelligence for patients). CBPMP description, its relationship with the complementary technology, and preliminary results of system prototype in a real environment are presented.

Intelligent environment for monitoring Alzheimer patients, agent technology for health care

This paper presents an autonomous intelligent agent developed for monitoring Alzheimer patients' health care in execution time in geriatric residences. The AGALZ (Autonomous aGent for monitoring ALZheimer patients) is an autonomous deliberative case-based planner agent designed to plan the nurses' working time dynamically, to maintain the standard working reports about the nurses' activities, and to guarantee that the patients assigned to the nurses are given the right care. The agent operates in wireless devices and is integrated with complementary agents into a multi-agent system, named ALZ-MAS (ALZheimer Multi-Agent System), capable of interacting with the environment. AGALZ description, its relationship with the complementary agents, and preliminary results of the multi-agent system prototype in a real environment are presented.

Planejamento de ações para automação inteligente da manufatura

Este artigo investiga o uso do sistema FAR-OFF na área da Automação da Manufatura. O sistema FAR-OFF possui característica similar aos sistemas de planejamento baseado em busca heurística, os quais têm apresentado excelentes resultados nos últimos anos na área de planejamento. Entretanto, em vez de ser um sistema generativo, o FAR-OFF é um sistema de planejamento baseado em casos que garante estabilidade para solucionar problemas em tempo aceitável. Os resultados apresentados pelo sistema no domínio de logística mostram que este é um sistema promissor para automação inteligente.

Case‐Base Maintenance By Integrating Case‐Index Revision and Case‐Retention Policies in a Derivational Replay Framework

This article presents case‐base maintenance policies for case index revision and case retention. The policies are formulated in the context of case‐based planners performing case adaptation by derivational replay. I implemented these policies on a particular case‐based planner and claim that my case‐index revision policy improves the accuracy of the retrieval and that the case‐retention policy filters redundant cases better than other case‐retention policies known from the literature. My claims are validated by empirical validation. It will be seen that there is an interrelation between the two policies that improves the filtering process of the case‐retention policy.

Introspective reasoning for index refinement in case-based reasoning

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.

Introspective reasoning for index refinement in case-based reasoning

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.

Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.

Case-based process planning using an object-oriented model representation

This research focuses on the development of a process-planning system. This system utilizes case-based techniques for process selection and sequencing to combine the advantages of the variant and generative approaches to process planning. The case-based process planner utilizes an object-oriented model representation to operate on general three-dimensional prismatic parts, which are represented by a collection of features (e.g. slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan-merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiences to become more effective and efficient.

Massively parallel support for case-based planning

The Caper case-based planner uses the massive parallelism of the Connection Machine to quickly retrieve cases and plans from a large, unindexed memory. The system can retrieve cases and plans based on any feature of the target problem, including abstractions of target features. By controlling which features are part of the retrieval probe and their level of abstraction, a wide range of queries can be issued. The more specific the query, the closer the retrieved cases are to the current problem. Unlike serial planners, Caper can afford to retrieve several plans to achieve different parts of the target problem, and then merge them into a composite plan that solves most of the target goals with less adaptation. We are testing Caper's case-retrieval components in two domains: car assembly and transportation logistics. >

Automatic construction of process plans from solid model representations

Methods and an implemented system for automatic generation of process plans from the CAD boundary representation of a part are presented. Two problems are addressed: one is the machine understanding of the shape of a part from its low-level boundary representation, and the other is developing a plan based on this semantic information. The architecture of the system at the highest level consists of a solid modeler, a shape description system, an automatic process planner, and the interfaces between them. Cooperative reasoning and combining geometric evidences are used to automatically extract the shape primitives of a part and determine the relations between these primitives from the boundary representation CAD data, thus producing the higher-level semantic information. A case-based planner uses the information produced by the shape description system to construct a conceptual plan for machining the part. Examples illustrate the reasoning performed by the system and show its advantages. >

Explaining and repairing plans that fail

A persistent problem in machine planning is that of repairing plans that fail. One approach to this problem that has been shown to be quite powerful is based on the idea that detailed descriptions of the causes of a failure can be used to decide between the different repairs that can be applied. This paper presents an approach to repair in which plan failures are described in terms of causal explanations of why they occurred. These domain-level explanations are used to access abstract repair strategies, which are then used to make specific changes to the faulty plans. The approach is demonstrated using examples from CHEF, a case-based planner that creates and debugs plans in the domain of Szechwan cooking. While the approach discussed here is examined in terms of actual plan failures, this technique can also be used in the repair of plans that are discovered to be faulty prior to their actual running.

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.

New Books

This work presents a theory of planning called Case-Based Planning that suggests the use of a planner's dynamic memory of experience rather than a static base of rules. This view of planning from experience is supported by a learning system that incorporates new experiences into the planner's episodic memory. A process of plan repair is also presented in which plan failures are diagnosed through a casual analysis of the steps and states that led to their occurrence. Successful plans, failures, and past repairs are stored, with successes being accessed and modified to create new plans, failures being used to warn the planner of impending problems, and repair strategies being referenced to tell the planner how to deal with faulty plans at hand. 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.