Stock market one-day ahead movement prediction using disparate data sources

There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of “crowd-sourced” knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the public’s collective interest. Google and Wikipedia have become important “knowledge bases” for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our “inference engine”. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional “experts” like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.     

Designer®—Intelligent expert system for intelligent manufacturing systems design

This survey on expert systems activities and trends in Yugoslavia offers some results already obtained in the domain of manufacturing science and technology. In the scope of a long-term research project “Intelligent Manufacturing Systems (IMS)—Theory and Application” a Designer® Intelligent Expert System for mafacturing engineering has been proposed and partially developed. Designer® IES is based on new developed knowledge automata theory enhanced with cellular automata concept. Induction learning by analogy and Quasimorphism knowledge mapping from real world to model world is used to generate a reasoning structure. The Intelligent Expert System is divided into three main subsystems, with a very large knowledge base:

• •Product designer
• •Process Designer, and
• •Production Planning and Control Designer.

All these segments were developed in pilot versions of expert systems for specific groups of activities inside each of these three domains.     

Information quality measurement of medical encoding support based on usability

Medical encoding support systems for diagnoses and medical procedures are an emerging technology that begins to play a key role in billing, reimbursement, and health policies decisions. A significant problem to exploit these systems is how to measure the appropriateness of any automatically generated list of codes, in terms of fitness for use, i.e. their quality. Until now, only information retrieval performance measurements have been applied to estimate the accuracy of codes lists as quality indicator. Such measurements do not give the value of codes lists for practical medical encoding, and cannot be used to globally compare the quality of multiple codes lists. This paper defines and validates a new encoding information quality measure that addresses the problem of measuring medical codes lists quality. It is based on a usability study of how expert coders and physicians apply computer-assisted medical encoding. The proposed measure, named ADN, evaluates codes Accuracy, Dispersion and Noise, and is adapted to the variable length and content of generated codes lists, coping with limitations of previous measures. According to the ADN measure, the information quality of a codes list is fully represented by a single point, within a suitably constrained feature space. Using one scheme, our approach is reliable to measure and compare the information quality of hundreds of codes lists, showing their practical value for medical encoding. Its pertinence is demonstrated by simulation and application to real data corresponding to 502 inpatient stays in four clinic departments. Results are compared to the consensus of three expert coders who also coded this anonymized database of discharge summaries, and to five information retrieval measures. Information quality assessment applying the ADN measure showed the degree of encoding-support system variability from one clinic department to another, providing a global evaluation of quality measurement trends.     

Dynamic evaluation of the development process of knowledge-based information systems

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system’s performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system—the cost of keeping a domain expert on-line—is reduced.     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

A domain-driven approach to improving search effectiveness in traditional online catalogs

Search performance can be greatly improved by using domain knowledge to assist users in developing a problem specification tailored to the information contained in the system. A methodology is presented for utilizing intelligent information retrieval techniques and domain-specific knowledge to improve user searching. For databases involving a relatively narrow domain, a “system thesaurus” combined with expert systems technology can be used to create an intelligent front end to assist the user in retrieving information with greater precision and recall. Evaluation of the prototype showed greatly improved search effectiveness and satisfaction over the traditional catalog system.     

A neuro-based expert system for facility layout construction

Motivated by the success of implementing expert systems (ESs) based on artificial neural networks (ANNs) to improved classical rule-based expert systems (RBESs), this paper reports on the development of a neuro-based expert system (NBES) for facility layout construction in a manufacturing system. In an artificial intelligence (AI) technique such as the NBES, the semantic structure of If-Then rules is preserved, while incorporating the learning capability of ANNs into the inference mechanism. Unlike implementing a popular back propagation network (BPN) as an ES, the proposed BAMFLO (Bidirectional Associative Memories for Facility LayOut) system is an intelligent layout consultant system consisting of pipeline BAM neural networks with simple, fast incremental learning and multiple bidirectional generalization characteristics. This incrementability makes BAMFLO effective at acquiring, adding or adapting learned layout knowledge; thus it is possible to memorize newly extended If-Then layout rules without retraining old ones. The multi-bidirectionality gives BAMFLO the ability to quickly and reliably generalize a layout solution, and to further infer unknown facts from known facts through a complex knowledge base (memorization) without losing information. The solution process of BAMFLO contains three essential steps: training example generation, incremental learning and solution generalization. The examples (layout knowledge) can be generated from practical experience and/or classical layout software solutions for incrementally training BAMFLO; the process then derives multiply bidirectionally generalized construction layout solutions. The experimental results show that the BAMFLO scheme outperforms five classical layout methods used to generate training examples.     

An expert system for managing medical appropriateness criteria based on computer algebra techniques

This article presents the development of an expert system for managing medical appropriateness criteria together with an outline of its theoretical foundations. Techniques borrowed from computer algebra (Gröbner bases) are applied to this field of medicine.

The steps of the expert system construction process are as follows. First, the knowledge provided in table format by experts in coronary diseases is translated into a set of production rules of a rule-based expert system (RBES). Kleene's three-valued logic augmented with modal operators is chosen in order to manage uncertainty.     

Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system

The so-called "first generation" expert systems were rule-based and offered a successful framework for building applications systems for certain kinds of tasks. Spatial, temporal, and causal reasoning, knowledge abstractions, and structuring are among topics of research for "second generation" expert systems. It is proposed that one of the keys for such research is knowledge organization. Knowledge organization determines control structure design, explanation and evaluation capabilities for the resultant knowledge base, and has strong influence on system performance. We are exploring a framework for expert system design that focuses on knowledge organization, for a specific class of input data, namely, continuous, time-varying data (image sequences or other signal forms). Such data are rich in temporal relationships as well as temporal changes of spatial relations, and are thus a very appropriate testbed for studies involving spatio-temporal reasoning. In particular, the representation formalism specifies the semantics of the organization of knowledge classes along the relationships of generalization/specialization, decomposition/aggregation, temporal precedence, instantiation, and expectation-activated similarity. Á hypothesize-and-test control structure is driven by the class organizational principles, and includes several interacting dimensions of search (data-driven, model-driven, goal-driven temporal, and failure-driven search). The hypothesis ranking scheme is based on temporal cooperative computation, with hypothesis "fields of influence" being defined by the hypothesis' organizational relationships. This control structure has proven to be robust enough to handle a variety of interpretation tasks for continuous temporal data. A particular incarnation, the ALVEN system, for left ventricular performance assessment from X-ray image sequences, will be summarized in this paper.     

Modelling law using a feminist theoretical perspective 1

In recent years a schism has become apparent in artificial intelligence and law between those who claim that legal expert systems cannot be built without first establishing a satisfactory theoretical model of law ("purists") and those whose main desire is to build working systems with or without theoretical underpinning ("pragmatists"). Most attempts at finding a jurisprudential model for building expert systems, however, have inconclusively attempted to apply traditional “grand theories” and have embedded themselves in the long standing controversies of analytical jurisprudence. This paper highlights other theoretical possibilities for modelling law which have been sidelined in the AI and law field. In particular, it promotes the adoption of a feminist theoretical perspective on law and legal knowledge representation. Feminist legal critique has a discrete, concrete and pragmatic approach and so may be a good tool for the resolution of the demands of both purists and pragmatists. The paper applies these insights to a proposed child custody expert system.     

知识工程中的知识库、本体与专家系统

随着语义Web思想的兴起,对专家系统的互操作性和共享性也提出了更高的要求,对新型知识表示方式和新型知识系统的研究是大势所趋.本体作为共享概念模型的明确的形式化规范说明,为不同系统之间的互操作提供了基本的框架,是解决共享和互操作问题的有效的方法.一些初学者对知识库、本体以及专家系统的概念产生了各种疑惑,根据笔者的理解与实践,对知识工程中的本体、知识库以及专家系统做出较系统的比较分析,对这几个术语做一个澄清.     

Paradigms for knowledge-based systems in industrial engineering

When developing expert systems, expertise lies not only in formulating the knowledge to be put into the knowledge base, but also in deciding upon the knowledge representation and inference mechanism most suited to the application. Six detailed knowledge bases demonstrate the application of various AI-based systems to industrial engineering problems. They illustrate a number of approaches: expert systems, which are based upon practical experience; decision systems, which derive from modelling skills; and situation-action systems, which rely on production process design skills. The six paradigms presented describe a logical expert system for selecting material handling equipment; a multi-valued expert system for selecting a dispatching rule for automatic guided vehicles; a profile matching expert system for selecting project management software; a confidence building expert system for selecting a machine feeder; a tandem decision system for developing a production schedule; and a situation-action system for controlling job allocation in a flexible manufacturing cell. The relationships between these various paradigms and the characteristics of problems to which they can be applied are categorized by the nature of the expert and his expertise; the features of the environment; the decision or decisions to be taken; and the manner in which AI-system performance can be evaluated. A knowledge base is proposed for determining which architecture is most appropriate for a given application.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

Mapping knowledge to rules for scheduling expert systems

Although the theoretical framework of expert systems has been well established, the process of developing a non-trivial expert system is still considered a difficult task. The main reason for this is that the nature of expert systems is knowledge-intensive. Also, it is usually difficult for domain experts to explain or communicate their expertise to the system professionals. Many methodologies have been proposed to overcome this domain knowledge representation problem. Most of them require the assistance of an expert system shell (tool). However, with a purpose of helping the system development in mind, most of them were not satisfactory. This research takes the experience of implementing a course scheduling expert system, and suggests two analysis methods to describe the characteristics of course scheduling knowledge. It is shown that these methods provide assistance on clarifying the complicated scheduling problem. Another favorable advantage of this method is its capability helping the transferring of domain knowledge to rules in the knowledge base.     

Brussels revisited: The European conflicts guide

In this paper, we examine the soundness of Capper & Susskind's recommended legal expert system development methodology in the areas of knowledge acquisition and knowledge representation. Legal expert systems have not yet had the impact in the United Kingdom that might be expected. We argue that this is partly the result of developers paying insufficient attention to the ‘third estate’: user interfaces. We make suggestions about both the look and feel of legal expert systems, and the facilities that such systems should offer. Lastly, we claim that we have developed an exploratory expert system encapsulating the Brussels Convention 1968 which can contribute to the development of a useful computer‐based guide to an important legal domain.     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。     

一种用于建造多塔精馏过程故障诊断专家系统知识库的方法

提出了一种用于建造多塔精馏过程故障诊断专家系统知识库的方法，该方法借助于实例，通过基于解释学习的学习模型及定量深层知识库来产生故障诊断领域知识，若用该方法建立专家系统知识库，则相应的专家系统在进行蒸馏系统故障诊断时比较快速、准确。     

基于相似性度量的氧化铝配料知识库在线维护

针对烧结法氧化铝优化配料专家系统知识库的结构特点,提出了一种基于相似性度量的专家知识库在线维护方法。构造规则的相似性度量函数,以此为基础进行规则不一致性判断,并遵循原有的知识组织策略在线实现规则的有序添加和修改,从而保证高效的专家推理,提出的方法已成功用于工业应用。