Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.

模糊逻辑的采用软件开发工作量估算

软件估算是有半个世纪发展历史的计算机科学领域的一个巨大挑战，因为软件估算涉及到软件项目的成本和计划。开发人员需要能够获得基于他们自己的程序得到的包含了工作量估算的实践。软件成本估算主要估算开发软件系统所需的工作量、时间、人力资源等。一种有效的方式是在项目早期确定成本时估算工作量。软件成本主要依据项目的需求规格说明书来确定。目前，实施可靠、准确的成本估算仍是软件工程领域的一个挑战。 在项目早期阶段，许多项目属性尚未确定。此时的软件估算通常是不准确的，估算 的准确程度取决于用于估算的可靠且可用的信息的数量。在后续的项目分析和设计阶段，对项目的了解更加深入，估算不确定性减少，估算准确性提高。大部分估算模型未考虑这种不确定性，只是得到了确定的估算结果。这些模型需要改进，以得到估计范围和估算结果的发生概率。 新的方法（如：模糊逻辑）可能提供了软件工作量估算的替代方案。软件开发总是可以用一组具有一定程度模糊性的参数来表征。这就需要在模型中引入一定程度的不确定性，以使模型更接近实际。模糊逻辑在这方面很合适。应用模糊逻辑可以解决目前工作量估算模型存在的许多问题。而且，模糊逻辑已经与算法的和非算法的工作量估算模型结合，用于解决固有不确定性问题。 本文提出一种基于模糊逻辑规模的软件开发工作量估算方法。软件规模不是一个单个数字，可以看作是一个三角模糊数(triangular fuzzy number, TFN)。应用本文方法，可以通过改变约束条件对任意常数中的工作量估算结果进行优化。基于对本文方法中模糊权重的平均方差解释%(Variance Accounted For, VAF%) ， 提出了一种最优化算法。应用COCOMO 公开数据集进行了验证实验。与实际工作量估算的比较结果表明，基于偏差系数，本文提出的模型提供了较好的估算结果。 最后，提出了一种改进的基于模糊案例的推理(Fuzzy Case-Based Reasoning , FCBR)方法，该方法集成了agent 技术，可以从多个组织的分布式数据库中找到相似项目。基于该方法，可以从分布式预定义的项目成本数据库中收集软件成本数据，进而建立软件成本估算模型。该模型应用FCBR 方法，在不同软件组织的历史软件项目度量数据中找到类似项目。

An intelligent assistant for design and material engineers in the submarine cable industry

This paper describes an industrial application of case-based reasoning in engineering. The application involves an integration of case-based reasoning (CBR) retrieval techniques with a relational database. The database is specially designed as a repository of experiential knowledge and with the CBR application in mind such as to include qualitative search indices. The application is for an intelligent assistant for design and material engineers in the submarine cable industry. The system consists of three components; a material classifier and a database of experiential knowledge and a CBR system is used to retrieve similar past cases based on component descriptions. Work has shown that an uncommon retrieval technique, hierarchical searching, well represents several search indices and that this techniques aids the implementation of advanced techniques such as context sensitive weights. The system is currently undergoing user testing at the Alcatel Submarine Cables site in Greenwich. Plans are for wider testing and deployment over several sites internationally.

A blackboard architecture for a hybrid CBR system for scientific software

This paper describes the use of a blackboard architecture for building a hybrid case based reasoning (CBR) system. The Smartfire fire field modelling package has been built using this architecture and includes a CBR component. It allows the integration into the system of qualitative spatial reasoning knowledge from domain experts. The system can be used for the automatic set-up of fire field models. This enables fire safety practitioners who are not expert in modelling techniques to use a fire modelling tool. The paper discusses the integrating powers of the architecture, which is based on a common knowledge representation comprising a metric diagram and place vocabulary and mechanisms for adaptation and conflict resolution built on the Blackboard.

Measuring similarity of software designs using graph matching for CBR

This paper examines different ways of measuring similarity between software design models for Case Based Reasoning (CBR) to facilitate reuse of software design and code. The paper considers structural and behavioural aspects of similarity between software design models. Similarity metrics for comparing static class structures are defined and discussed. A Graph representation of UML class diagrams and corresponding similarity measures for UML class diagrams are defined. A full search graph matching algorithm for measuring structural similarity diagrams based on the identification of the Maximum Common Sub-graph (MCS) is presented. Finally, a simple evaluation of the approach is presented and discussed.

Lifelong learning through digital storytelling in corporate training

In this paper we discuss collaborative learning strategies based on the use of digital stories in corporate training and lifelong learning. The text starts with a concise review on theoretical and technical foundations about the use of digital technologies in collaborative strategies in lifelong learning. We will also discuss if the corporate training may be improved by the use of individual audio-visual experience in learning process. Careful planning, scripting and production of audio-visual digital stories can help in the construction of collaborative learning spaces in which adults are in the context of vocational training throughout life. Our analysis concludes emphasizing on the need to experience the routing performance of digital stories in the context of corporate training, following the reference levels mentioned here, so we can have in a future more theoretical and empirical elements for the validation and conceptualization in the use of digital stories in the context of corporate training. Ultimately we believe that lifelong learning can be improved with the use of strategies that promote the production of personal audio-visual for those involved in teaching and learning process in organizational context.

Two-part Segmentation of Text Documents

We consider the problem of segmenting text documents that have a
two-part structure such as a problem part and a solution part. Documents
of this genre include incident reports that typically involve
description of events relating to a problem followed by those pertaining
to the solution that was tried. Segmenting such documents
into the component two parts would render them usable in knowledge
reuse frameworks such as Case-Based Reasoning. This segmentation
problem presents a hard case for traditional text segmentation
due to the lexical inter-relatedness of the segments. We develop
a two-part segmentation technique that can harness a corpus
of similar documents to model the behavior of the two segments
and their inter-relatedness using language models and translation
models respectively. In particular, we use separate language models
for the problem and solution segment types, whereas the interrelatedness
between segment types is modeled using an IBM Model
1 translation model. We model documents as being generated starting
from the problem part that comprises of words sampled from
the problem language model, followed by the solution part whose
words are sampled either from the solution language model or from
a translation model conditioned on the words already chosen in the
problem part. We show, through an extensive set of experiments on
real-world data, that our approach outperforms the state-of-the-art
text segmentation algorithms in the accuracy of segmentation, and
that such improved accuracy translates well to improved usability
in Case-based Reasoning systems. We also analyze the robustness
of our technique to varying amounts and types of noise and empirically
illustrate that our technique is quite noise tolerant, and
degrades gracefully with increasing amounts of noise

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Intelligent Micro Grid Management using a Multi-Agent approach

Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players’ behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents’ reaction to price changes, is an interesting tool for the micro grid operator.

Enseñar estrategias de razonamiento y pensamiento crítico enfermero a los alumnos de grado: el qué y el cómo

La enseñanza basada en las competencias es más compleja que la aplicación de saberes y la realización de casos prácticos específicos, puesto que es necesario ejecutar también las operaciones mentales abstractas que permitan al estudiante consolidar los conocimientos, y demostrar las habilidades y las actitudes necesarias para ser competente. Para que el alumno universitario aprenda este proceso debe, en primer lugar, ser consciente de esta necesidad y debe saber identificar las operaciones mentales implicadas y saber representarlas o expresarlas de forma manuscrita o esquematizada. Estas operaciones no son, según alumnos y profesores, tareas fáciles, pero si son, sin embargo, elementos que se perciben como muy necesarios para aprender estrategias de razonamiento y pensamiento crítico que repercutirán positivamente en la calidad de los cuidados de enfermería realizados en el paciente y en la comunidad en un futuro más o menos próximo. La asignatura optativa de tercer curso Resolución de casos desde un enfoque enfermero del Departamento de Enfermería Fundamental y Medico-quirúrgica de la Universidad de Barcelona nace con la intención de dar respuesta a la necesidad del estudiante de mejorar sus habilidades profesionales enseñando el marco teórico de estrategias de razonamiento clínicos para resolver casos simulados del entorno sanitario y profesional, de forma más efectiva y completa. La presente comunicación tiene por objetivos principales presentar el marco teórico que sustenta el proyecto docente implementado y exponer las características metodológicas desarrolladas

Dissolution of Dynamic Electronic Institutions, a first approach: Relevant factors and causes

In the context of the digital business ecosystems, small organizations cooperate between them in order to achieve common goals or offer new services for expanding their markets. There are different approaches for these cooperation models such as virtual enterprises, virtual organizations or dynamic electronic institutions which in their lifecycle have in common a dissolution phase. However this phase has not been studied deeply in the current literature and it lacks formalization. In this paper a first approach for achieving and managing the dissolution phase is proposed, as well as a CBR process in order to support it in a multi-agent system

Visual management of sags and incidents gathered in distribution substations for power quality management

Monitor a distribution network implies working with a huge amount of data coining from the different elements that interact in the network. This paper presents a visualization tool that simplifies the task of searching the database for useful information applicable to fault management or preventive maintenance of the network