Knowledge-based treatment planning for adolescent early intervention of mental healthcare: a hybrid case-based reasoning approach

Abstract: Treatment planning is a crucial and complex task in the social services industry. There is an increasing need for knowledge-based systems for supporting caseworkers in the decision-making of treatment planning. This paper presents a hybrid case-based reasoning approach for building a knowledge-based treatment planning system for adolescent early intervention of mental healthcare. The hybrid case-based reasoning approach combines aspects of case-based reasoning, rule-based reasoning and fuzzy theory. The knowledge base of case-based reasoning is a case base of client records consisting of documented experience while that for rule-based reasoning is a set of IF–THEN rules based on the experience of social service professionals. Fuzzy theory is adopted to deal with the uncertain nature of treatment planning. A prototype system has been implemented in a social services company and its performance is evaluated by a group of caseworkers. The results indicate that hybrid case-based reasoning has an enhanced performance and the knowledge-based treatment planning system enables caseworkers to construct more efficient treatment planning in less cost and less time.     

REPLANNING MECHANISM FOR DELIBERATIVE AGENTS IN DYNAMIC CHANGING ENVIRONMENTS

This paper proposes a replanning mechanism for deliberative agents as a new approach to tackling the frame problem. We propose a beliefs desires and intentions (BDI) agent architecture using a case-based planning (CBP) mechanism for reasoning. We discuss the characteristics of the problems faced with planning where constraint satisfaction problems (CSP) resources are limited and formulate, through variation techniques, a reasoning model agent to resolve them. The design of the agent proposed, named MRP-Ag (most-replanable agent), will be evaluated in different environments using a series of simulation experiments, comparing it with others such as E-Ag (Efficient Agent) and O-Ag (Optimum Agent). Last, the most important results will be summarized, and the notion of an adaptable agent will be introduced.     

基于案例和模糊推理的农业虫害专家系统研究

为了满足实.际虫害诊断问题对专家系统的要求,根据虫害特征诊断的现实特点和要求,首次将模糊技术和案例推理相融合,引入到虫害诊断专家系统的设计中.阐述了模糊案例推理的知识表示,给出了模糊案例推理技术的推理过程,提出了基于案例与模糊推理的虫害诊断推理机制.对二者的结合方式做了阐述,给出了专家系统的整体实现结构,并时各个模块的实现方法进行了详尽的阐述,最终以一个实例演示了实例诊断的流程.     

A Survey on Case-Based Planning

Case-based planning is the reuse of past successful plansin order to solve new planning problems.This paper presents a survey of case-based planning, in terms ofits historical roots, underlying foundations, methods andtechniques currently used, limitations, and future trends.Several authors have given overviews on case-based reasoningand specific topics such as case retrieval, case adaptation,and learning. This overview differs in focus.Its aim is to emphasize the case-based approach to planning,its methodological issues, and its relation to classical planningand the other kinds of case-based reasoning.It also provides some reference models.     

基于实例推理的电子工艺规划

本文针对PCB检修工艺中存在的效率低下、标准不统一的问题，提出了一种基于实例推理的工艺规划。在阐述基于实例推理基本原理的基础上，构造了一种推理模型，并讨论了实例匹配算法。实际应用表明，这种设计方案有效地提高了PCB检修工艺的设计效率。     

基于案例模糊的无人机故障诊断系统

无人机是一个结构复杂的机电一体化系统,为了满足故障诊断的需求,以模糊数学理论为基础,并将案例推理融合到模糊推理机中.重点论述如何确定模糊关系矩阵和案例式推理的原理,详细介绍了案例匹配流程,提出了一种新的模糊推理机制,并最终设计实现系统功能,提高了无人机故障诊断效率、准确度、可靠性.     

基于混合推理机制的故障诊断专家系统

多数故障诊断专家系统采用单一的推理机制,或者基于规则的推理,或者基于事例的推理。而这两种推理机制都各有优缺点,采用单一推理机制会造成诊断的不准确性。论文将基于规则的推理和基于事例的推理相结合,设计了混合推理机制。在此基础上,论文设计了一个既有专家知识库,又有故障事例库,具有自学习能力的故障诊断专家系统(AFDES)。实验结果表明,论文设计的混合推理机制是比较有效的。     

Integrating Case-Based and Rule-Based Reasoning to Meet Multiple Design Constraints

Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of "what if" analysis needed for creative design.
The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria.
We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.     

Block assembly planning in shipbuilding using case-based reasoning

A process planning system using case-based reasoning (CBR) is developed for block assembly in shipbuilding. A block assembly planning problem is modeled as a constraint satisfaction problem where the precedence relations between operations are considered constraints. In order to find similar cases, we propose two similarity coefficients for finding similar cases and for finding similar relations. Due to the limited number of operation types, the process planning system first matches the parts of the problem and those of the case-based on their roles in the assembly, and then it matches the relations related to the matched part–pairs. The parts involved in more operations are considered first. The process planning system is applied to simple examples for verification and comparison. An interface system is also developed for extracting information from CAD model, for preparing data for process planning, and for visually verifying the assembly sequence.     

Robotic inspection plan optimizationby case-based reasoning

Inspection planning is discussed in a framework where a rich choice of instruments is available and robots can also participate in the inspection process. The problem of constrained plan optimization is exposed, and a solution is suggested that is based on task grouping. After outlining the overall planning process, we give details of the optimization stage where case-based reasoning is applied. Finally, it will be shown how the implemented knowledge-based system can operate as a knowledge server.     

MARS: A mergers and acquisitions reasoning system

Mergers and acquisitions (M&A) are currently revolutionizing the structure of corporate U.S.A. and annually involve deals totalling billions of dollars. Consequently, it is an area of intense activity and interest within the financial community. The process of planning an M&A is enormously complex and involves sophisticated reasoning and planning, by several parties such as the raider, the target company, investment banks, etc. Computer based tools are often invaluable for planning several stages of an M&A, such as generating forecasted cash flows. Current computer aids for M&A however do not provide adequate support for many essential features such as real time planning, reasoning under uncertainty, nonmonotonic inference, case-based reasoning, etc. MARS is a prototype M&A reasoning tool developed at General Electric Corporate R&D that attempts to provide such features in an integrated environment. MARS both simulates and provides advice regarding the complex reasoning and planning involved in an M&A deal. In doing so, it provides an excellent test bed architecture for the testing, development and integration of several ideas from artificial intelligence. MARS is implemented in COMMON LISP using RUM [15] on top of KEE [18]. RUM, a development environment for reasoning under uncertainty is based on Bonissone's theory of plausible reasoning [2–4] and was also developed at General Electric Corporate R&D.     

MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.     

基于案例推理的供应商选择决策支持系统研究

在介绍了基于案例推理方法的基本原理基础之上,分析了基于案例推理技术的供应商选择决策支持系统的工作原理、框架结构及功能;重点论述了基于案例推理的供应商选择决策支持系统中的一些关键步骤,并结合实例给出了基于案例推理的供应商选择与评价方法,用来验证基于案例推理技术在供应商选择决策支持系统中应用的可行性和有效性,为企业供应商选择决策提供了一个系统模型。     

CaBMA: a case-based reasoning system for capturing,refining, and reusing project plans

In this paper, we present CaBMA, a prototype of a knowledge-based system designed to assist with project planning tasks using case-based reasoning. CaBMA introduces a novel approach to project planning in that, for the first time, a knowledge layer is added on top of traditional project management software. Project management software provides editing and bookkeeping capabilities. CaBMA enhances these capabilities by automatically capturing project plans in the form of cases, refining these cases over time to avoid potential inconsistency between them, reusing these cases to generate plans for new projects, and indicating possible repairs for project plans when they derive away from existing knowledge. We will give an overview of the system, provide a detailed explanation on each component, and present an empirical study based on synthetic data.     

几何因果定性推理的基本原理和算法

因果定性推理是一种通过分析描述物理系统行为和关系的约束找出系统内部各个成分之间的因果结构的推理方法．本文提出一种基于约束和变量分析的因果定性分析模型和算法．该方法在产品设计中有广泛的应用，利用这个模型和算法可较好地解决参数化设计中的几何推理问题，还可用作概念设计的工具，用于完成复杂系统设计任务的划分及定序、设计变量之间相互依赖关系分析等工作．算法具有应用性强、效率和稳定性好、支持欠约束和多解问题等优点.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

Integrating diverse information resources in a case-based design environment

The success of case-based design aids depends both on the case-based reasoning processes they apply and on effectively integrating those processes into the larger task context: on making the case-based reasoning component present case information at the right time and in the right way, on exploiting additional information resources as needed to supplement the case library and to guide case application, on capturing useful information from current reasoning and providing it to up- and down-stream designers, and on unobtrusively learning new cases during the design process. This article presents a set of principles and techniques for integrated case-based design support systems and illustrates their application through a case study of the Stamping Advisor, a system to support feasibility analysis for sheet metal automotive parts.     

用神经网络来实现基于范例的推理系统

范例推理与神经网络有一种自然的联系，神经网络有许多优点，利用神经网络来实现范例推理可以取得非常好的效果。文章首先详细探讨了在范你推理中使用的神经网络模型与技术，并给出了其上的搜索与学习算法以及数据挖掘算法，旨在提高范例推理系统的鲁棒性和知识获取的自动化程度。     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.