Exploring aspects of cell intelligence with artificial reaction networks

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence.     

Translating production rules into a forward reasoning Prolog program

Several attempts have been made to design a production system using Prolog. To construct a forward reasoning system, the rule interpreter is often written in Prolog, but its execution is slow. To develop an efficient production system, we propose a rule translation method where production rules are translated into a Prolog program and forward reasoning is done by the translated program. To translate the rules, we adopted the technique developed in BUP, the bottom-up parsing system in Prolog. Man-machine dialogue functions were added to the production system and showed the potential of our method to be applied to expert systems.     

An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting

Short-term electric load forecasting (STLF) is an essential tool for power generation planning, transmission dispatching, and day-to-day utility operations. A number of techniques are used and reported in the literature to build an accurate forecasting model. Out of them Artificial Neural Networks (ANN) are proven most promising technique for STLF model building. Many learning schemes are being used to boost the ANN performance with improved results. This motivated us to explore better optimization approaches to devise a more suitable prediction technique. In this study, we propose a new hybrid model for STLF by combining greater optimization ability of artificial bee colony (ABC) algorithm with ANN. The ABC is used as an alternative learning scheme to get optimized set of neuron connection weights for ANN. This formulation showed improved convergence rate without trapping into local minimum. Forecasting results obtained by this new approach have been presented and compared with other mature and competitive approaches, which confirms its applicability in forecasting domain.     

Expert Systems and the "Myth" of Symbolic Reasoning

Elements of the artificial intelligence approach to expert systems offer great productivity advantages over traditional approaches to application systems development, even though the end result may be a program employing no AI techniques. These productivity advantages are the hidden truths behind the "myth" that symbolic reasoning programs are better than ordinary ones.     

Using simple scenarios for wheelchair driving assistance

Artificial intelligence can be used to recognize and anticipate dynamic situations. Several computational methods based on mathematical tools already exist, but most of the time their implementation is complex and takes a long time to execute. In this article, we propose another learning and anticipation method in order to assist users in dynamic situations. We call it ‘scenario‐based reasoning’ algorithm. It is inspired by case‐based reasoning. It works with symbolic data and its aim is to make real‐time predictions. To do so, manipulated knowledge is specially designed to limit our solution's complexity and to facilitate learning and anticipation.     

Using hybrid expert system approaches for engineering applications

In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a numer of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.This paper is an attempt to provide some guidelines to expert system development based on limited experience with a handful of tools. It is not the intent of this paper to recommend or endorse the software, manufacturers, or organizations named within, nor to slight others not mentioned. We would appreciate feedback on omissions or oversights, so that more objective information about specific tools can be made public.Work performed while Aerospace Engineer, Artificial Intelligence Section, Mission Planning and Analysis Division, NASA/Johnson Space Center, Houston, TX.     

一种面向临床决策推理的可视诊疗技术

海量医学信息的快速增长已远远超出人类认知能力,医疗服务环境和用户人群的复杂多样性使得海量数据难以在现有能力和工具的支持下满足广大用户对于信息服务的需求.临床诊疗服务的可视化、智能化程度不高导致现有的医学知识服务水平难以保证海量资源信息的充分利用.在分析了临床诊疗环境下人机协同认知特性的基础上给出了一种基于语义层次的信息组织方式;分析了符合该数据组织模式的可视形态及自然的可视交互技术;在上述工作的基础上构建了一个面向临床决策推理的可视诊疗分析框架,并给出了原型系统实例加以验证.结果表明,通过结合交互式可视化和自动分析技术,可以有效地帮助人们从海量数据中获取到有用的信息模式,减轻人们对数据进行分析的负担,为医疗诊断过程提供决策支持服务.     

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.     

3DyRM: a dynamic roofline model including memory latency information

Modern systems present complex memory hierarchies and heterogeneity among cores and processors. As a consequence, efficient programming is challenging. An easy-to-understand performance model, offering guidelines and information about the behaviour of a code, may be useful to alleviate these issues. In this paper, we present two extensions of the well-known Berkeley Roofline Model. The first of these extensions, the Dynamic Roofline Model (DyRM), takes into consideration the complexities of multicore and heterogeneous systems, offering a more detailed view of the evolution of the execution of a code. The second, the 3DyRM, also adds information about the latency of memory accesses to better represent the behaviour on systems with complex memory hierarchies. A set of tools to obtain and represent the models has been implemented. These tools obtain the needed data from hardware counters, with low overhead. Different views are displayed by the tool that can be used to extract the main features of the code. Results of studying, with these tools, the NAS Parallel Benchmarks for OpenMP on two different systems are presented.     

Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement

The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.     

Artificial intelligence at Edinburgh university

The development of research into the area of artificial intelligence at Edinburgh University is described in this paper. It was first recognized by Edinburgh University as an independent discipline in 1966 and there is now an Artificial Intelligence Applications Institute. The main areas of artificial intelligence research are summarized. The five projects carried out with Alvey funding are examined in more detail. They cover such topics as natural language and text processing, 3D modelling and expert systems.     

Hand posture and gesture recognition technology

Hand gestures that are performed by one or two hands can be categorized according to their applications into different categories including conversational, controlling, manipulative and communicative gestures. Generally, hand gesture recognition aims to identify specific human gestures and use them to convey information. The process of hand gesture recognition composes mainly of four stages: hand gesture images collection, gesture image preprocessing using some techniques including edge detection, filtering and normalization, capture the main characteristics of the gesture images and the evaluation (or classification) stage where the image is classified to its corresponding gesture class. There are many methods that have been used in the classification stage of hand gesture recognition such as Artificial Neural Networks, template matching, Hidden Markov Models and Dynamic Time Warping. This exploratory survey aims to provide a progress report on hand posture and gesture recognition technology.     

An explainable deep learning model for prediction of early-stage chronic kidney disease

Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end-stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early-stage CKD. The health care applications require interpretability over the predictions of the black-box model to build conviction towards the model's prediction. Hence, the predictions of the DNN-CKD model are explained by the local interpretable model-agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN-CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN-CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.     

集对分析在人工智能中的应用与进展

人工智能是我国发展战略,集对分析从自主原创角度为人工智能提供一种基础性思路,具有重要意义。集对分析把确定的数学计算与不确定性系统分析有机结合,已在人工智能基础、模式识别、不确定性推理、智能决策、知识生态学、自然语言理解、专家系统、神经网络、智能工程、智能社会网络社区划分与演化等研究中得到应用。本文在概述集对分析的原理和联系数之后,综述集对分析在人工智能中的应用和进展,以期推动集对分析在人工智能中的进一步应用。     

ARPO-2: An expert system for legal advice on the breach of building contracts

Although Berman and Hafner [Berman 1989, pp. 928–938] presented the possibility to adapt the model of reasoning of development of an expert system for medical diagnosis to the reasoning of a judge when he/she sentences criminals does not resemble the reasoning found in the decisions of physicians, mathematicians or statisticians.When a lawyer reasons, he/she not only looks for the solution of a case; he/she simultaneously looks for the bases on which his/her reasoning can rest [Galindo 1992, pp. 363–367]. That is to say, he/she not only needs to find the solution but moreover he/she has to find the references (laws, jurisprudence and bibliography) that allow him/her to argue the solution.In many cases, computer solutions to these reasoning processes have been made in a separated way: the solution to the cases using expert systems, and the search of documentation using information retrieval systems.This paper presents the ARPO-2 prototype, a solution integrating the two aspects of legal reasoning: an expert system which is able to simultaneously find the solution to a problem and to give the necessary references so that the lawyer argues the solution. The subject on which the prototype solves problems is the breach of building contracts.In this paper, we describe the process of development of an expert system for solving, justification and documentation of breach of contracts, giving details on the way how the objects that intervene in the case were defined as well as on the reasoning followed.This paper was funded in part by DGICYT, Spanish Civil Law Computerization Project: PB870-632.     

Service-oriented intelligent group decision support system: Application in transportation management

In today’s ever changing consumer driven market economy, it is imperative for providers to respond expeditiously to the changes demanded by the customer. This phenomenon is no different in the transportation sector in which a service-oriented Group Decision Support System (GDSS) provides an important role in transportation enterprise to effectively manage and rapidly respond to the varying needs of the customer. In this paper, we explore the integration problem of service-oriented system and intelligence technology through the use of a GDSS. Initially, we analyze a service-oriented architecture and then, propose the design architecture of a service-oriented GDSS. Next, we put forward a general framework that integrates the intelligent techniques as a component into the architecture of service oriented GDSS. In addition, we illustrate how Artificial Intelligence techniques can resolve the conflicts of distributed group decisions. The paper is concluded by providing a number of applications in the railway management system that demonstrates the benefits of the utilization of a service oriented intelligent GDSS.     

医疗诊断专家系统推理机的设计与实现

专家系统是人工智能领域的重要分支,推理机是专家系统的重要组成部分。文中用关系数据库SQLServer2000设计诊断知识库,构造了一个医疗诊断专家系统,其推理机能够有效模拟医生的诊断思维。因此,它可以作为医生诊断疾病的一种辅助工具。根据疾病诊断的要求,仅对医疗诊断推理机的设计与实现方法进行了探讨,提出了一种基于正-反向推理的精确与不精确推理相结合的推理策略,并采用关系数据库和面向对象的技术来具体编程实现,从而提高了推理机的效率,获得了较好的推理效果。     

A direct and neural controller performance study with beam wandering mitigation control in free space optical link

The beam wander on the detector plane is one of the main causes for major power loss which severely degrades the performance of Free Space Optical (FSO) link. Designing a suitable controller to correct the beam motion at a faster rate to increase the beam stability becomes significant. This paper presents an investigation on the performance of two types of controller designed for aiming a laser beam to be at a particular point under dynamic disturbances. The first design is based on the Taguchi’s method (direct controller) while the second is the Artificial Neural Network (ANN) method (neural-controller). These controllers process the beam location information and generate the necessary outputs to mitigate the beam wandering. The pipelined-parallel architecture for both controllers are developed in a Field Programmable Gate Array (FPGA) and installed at the receiver station. Evidence of the suitability and the effectiveness of the proposed controller in terms of prediction exactness, prediction error, reduction of focal point wander, response to impulse and scintillation are provided through experimental results from the FSO link established for the horizontal range of 0.5 km at an altitude of 15.25 m.