基于人工智能的金融监管信息系统

本文介绍了人工智能和知识工程的有关技术，并针对我国金融监管的现状，讨论了如何建立有效的基于人工智能的金融监管信息系统进行风险 评估和预警。     

一种基于多智能体的ODSS的设计与实现

我智能体和组织决策支持系统是近年来人工智能和系统工程中的研究热点,本文在研究多智能体和组织决策支持系统的基础上,设计开发了一种基于多智能体的组织决策支持系统,并将该系统应用于彭风机厂的ＣＩＭＳ工程中,以验证该系统的功能与效果,最后,对系统进行了简要地总结。     

Product creation: An appropriate coupling of human and artificial intelligence

Small batch manufacture dominates the manufacturing sector of a growing number of industrialised countries. The organisational structures and management methods currently adopted in such enterprises are firmly based upon historical developments which started with individual craftsmen. These structures and methods are primarily concerned with the co-ordination of human activities, rather than with the management of theknowledge process underlying the creation of products.This paper argues that it is the failure to understand this knowledge process and its effective integration at aKnowledge Level which presents the real barrier to increased flexibility, not, as is presently perceived, a lack of suitableInformation Level integration. Potential techniques and methodologies for achievingKnowledge Level integration are beginning to emerge from Artificial Intelligence research. Realisation of full Knowledge Level integration will not only require further research into the AI techniques and methodologies involved, but also an understanding of the wider human aspects of their application. Some questions concerning the effective coupling of human and artificial intelligence to achieve Knowledge Level integration of the product creation process are presented.     

Workshop report: The two-day workshop on Research Issues in the Intersection between Software Engineering and Artificial Intelligence (held in conjunction with ICSE-16)

As the need for more complex software systems increases so does the need for developing systematic and standardized methods for software design and maintenance. Artificial Intelligence can play an important role in this activity as it may provide efficient, adaptable and customizable solutions. Domain analysis, program representation, process modeling, software testing and software verification are all areas that can benefit from the use of A.I techniques, including knowledge acquisition, knowledge representation, problem solving algorithms and theorem proving. This paper discusses the use of Artificial Intelligence techniques in Software Engineering, as it was presented in the ICSE 16's workshop on Research Issues in the Intersection between Software Engineering and Artificial Intelligence.     

基于多Agent的组织决策支持系统(ODSS)的设计与研究

近年来，Agent技术给软件的发展带来了日新月异的变化．这种技术提供了软件系统概念化、设计与实现的全新模式，多Agent与组织决策支持系统(ODSS)也是人工智能和系统工程中的研究热点。本文在研究多Agent与0DSS的基础上，设计了一种基于多Agent的组织决策支持系统，并对它进行了适当的分析和展望。     

专家系统开发平台的对比

在人工智能领域中，专家系统是应用最广泛、最成功的领域之一。专家系统的开发技术水平直接影响专家系统的开发效率，甚至决定着一个专家系统的最终命运。本文首先介绍了专家系统的各种软件环境，同时分析用各种平台开发专家系统的优点和特点，最后提出未来的软件发展方向。     

专家系统开发平台的对比

在人工智能领域中,专家系统是应用最广泛、最成功的领域之一。专家系统的开发技术水平直接影响专家系统的开发效率,甚至决定着一个专家系统的最终命运。本文首先介绍了专家系统的各种软件环境,同时分析用各种平台开发专家系统的优点和特点,最后提出未来的软件发展方向。     

Meaning in Artificial Agents: The Symbol Grounding Problem Revisited

The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues present different problems in the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between John Searle’s intentionality notion and Harnad’s Symbol Grounding Problem.     

关于知识表示的讨论

知识表示是人工智能（ＡＩ）研究中基本问题之一,对ＡＩ来说,知识表示的研究分成两个层次：（２）知识表示方法的研究,（２）表示观的研究,首先,本文在总结各种表示方法的基础上,提出了一种“知识表示体系树”,以观察ＡＩ研究的发展趋势,其次,文中收集了目前ＡＩ学界对这个问题讨论了各种主张,并将其归纳为评论论、本体论及知识工程等三类对表示的观点;最后,还讨论了ＡＩ一些研究分支与表示的关系。     

Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Public opinion channel for communities in the information age

In this paper, we propose as a new challenge a public opinion channel which can provide a novel communication medium for sharing and exchanging opinions in a community. Rather than simply developing a means of investigating public opinion, we aim at an active medium that can facilitate mutual understanding, discussion, and public opinion formation. First, we elaborate the idea of public opinion channels and identify key issues. Second, we describe our first step towards the goal using the talking virtualized egos metaphor. Finally, we discuss a research agenda towards the goal. Toyoaki Nishida, Dr.Eng.: He is a professor of Department of Information and Communication Engineering, School of Engineering, The University of Tokyo. He received the B.E., the M.E., and the Doctor of Engineering degrees from Kyoto University in 1977, 1979, and 1984 respectively. His research centers on artificial intelligence in general. His current research focuses on community computing and support systems, including knowledge sharing, knowledge media, and agent technology. He has been leading the Breakthrough 21 Nishida Project, sponsored by Ministry of Posts and Telecommunications, Japan, aiming at understanding and assisting networked communities. Since 1997, he is a trustee for JSAI (Japanese Society for Artificial Intelligence), and serves as the program chair of 1999 JSAI Annual Convention. He is an area editor (intelligent systems) of New Generation Computing and an editor of Autonomous Agents and Multiagent Systems. Nobuhiko Fujihara, Ph.D.: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E., the M.E., and the Ph.D. in Human Sciences degrees from Osaka University in 1992, 1994, and 1998 respectively. He has a cognitive psychological background. His current research focuses on: (1) cognitive psychological analysis of human behavior in a networked community, (2) investigation of information comprehension process, (3) assessment and proposition of communication tools in networking society. Shintaro Azechi: He is a fellow of Breakthrough 21 Nishida project, Communications Research Laboratory sponsored by Ministry of Posts and Telecommunications, Japan. He received the B.E. and the M.E. of Human Sciences degrees from Osaka University in 1994 and 1996 respectively. He is a Doctoral Candidate of Graduate School of Human Sciences, Osaka University. His current researches focus on (1) human behavior in networking community (2) social infomation process in human mind (3) development of acessment technique for communication tools in networkingsociety. His approach is from social psychological view. Kaoru Sumi, Dr.Eng.: She is a Researcher of Breakthrough 21 Nishida Project. She received her Bachelor of Science at School of Physics, Science University of Tokyo. She received her Master of Systems Management at Graduate School of Systems Management, The university of Tsukuba. She received her Doctor of engineering at Graduate School of Engineering, The University of Tokyo. Her research interests include knowledge-based systems, creativity supporting systems, and their applications for facilitating human collaboration. She is a member of the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI). Hiroyuki Yano, Dr.Eng.: He is a senior research official of Kansai Advanced Research Center, Communications Research Laboratory, Ministry of Posts and Telecommunications. He received the B.E., the M.E., and the Doctor of Engineering degrees from Tohoku University in 1986, 1988, and 1993 respectively. His interests of research include cognitive mechanism of human communications. His current research focuses on discourse structure, human interface, and dialogue systems for human natural dialogues. He is a member of the Japanese Society for Artificial Intelligence, the Association for Natural Language Processing, and the Japanese Cognitive Science Society. Takashi Hirata: He is a doctor course student in Graduate School of Information Scienc at Nara Institute of Science and Technology (NAIST). He received a master of engineering from NAIST in 1998. His research interest is knowledge media and knowledge sharing. He is a member of Information Processing Society of Japan (IPSJ), Japan Association for Artificial Intelligence (JSAI) and The Institute of Systems, Control and Information Engineers (ISCIE).     

Distributed control of production systems

This editorial introduces the special issue of the Elsevier journal, Engineering Application of Artificial Intelligence, on Distributed control of production systems. The current technology in communication and embedded systems allows products and production resources to play a more active role in the production process. This new active capacity will generate major changes in organizations and information systems (e.g., Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES)). New approaches are now required for modelling, testing and assessing the features made possible by the decisional and informational capabilities of these new active entities. One among the many possibilities is to use agents and holons, since agent and holon-based approaches assume interaction between intelligent entities to facilitate the emergence of a global behavior. This special issue thus focuses on the possible applications of distributed approaches for the design, evaluation and implementation of new control architectures for production systems. Both fundamental and applied research papers are presented.     

Creating knowledge maps by exploiting dependent relationships

Knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. In more recent times, researchers have investigated knowledge in a more applied way with the chief aim of bringing knowledge to life in machines. Artificial Intelligence has provided some degree of rigour to the study of knowledge and Expert Systems are able to use knowledge to solve problems and answer questions.Current business, social, political and technological pressures have forced organisations to take greater control of the knowledge asset. Software suppliers and others offering valuable solutions in this area have unfortunately clouded the issue of knowledge. Information and data control are seen as implicit knowledge management tools and many have abandoned the search for explicit knowledge management methods.Knowledge representation schemes help to identify knowledge. They allow for human understanding and machine application and they can support the automated use of knowledge in problem solving. Some of these representation methods also employ spatial techniques that add an extra dimension to human understanding.Knowledge mapping defined in this work uses learning dependency to organise the map and draws on the ideas of what knowledge is and on spatial representation structures. Knowledge maps can support metrics that provide information about the knowledge asset. Knowledge maps create a visible knowledge framework that supports the explicit management of knowledge by organisation managers and directors. Knowledge maps also offer other advantages to the organisation, the individual and to educational institutions.     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.     

基于知识点库的数据库学习系统

智能化计算机辅助教学(ICAI)将计算机技术、多媒体技术、Internet技术与传统教育相结合,通过教学模式、教学媒体、学生模型三方面的智能化设计,创造出一种新的教学形式。基于知识点库的数据库智能学习系统正是运用此技术,将其应用于数据库课程的教学开发中,实现本课程的远程、多样化学习等功能。     

From eager to lazy constrained data acquisition: A general framework

^*1 Constraint Satisfaction Problems (CSPs)¹⁷⁾ are an effective framework for modeling a variety of real life applications and many techniques have been proposed for solving them efficiently. CSPs are based on the assumption that all constrained data (values in variable domains) are available at the beginning of the computation. However, many non-toy problems derive their parameters from an external environment. Data retrieval can be a hard task, because data can come from a third-party system that has to convert information encoded with signals (derived from sensors) into symbolic information (exploitable by a CSP solver). Also, data can be provided by the user or have to be queried to a database. For this purpose, we introduce an extension of the widely used CSP model, called Interactive Constraint Satisfaction Problem (ICSP) model. The variable domain values can be acquired when needed during the resolution process by means of Interactive Constraints, which retrieve (possibly consistent) information. A general framework for constraint propagation algorithms is proposed which is parametric in the number of acquisitions performed at each step. Experimental results show the effectiveness of the proposed approach. Some applications which can benefit from the proposed solution are also discussed. This paper is an extended and revised version of the paper presented at IJCAI’99 (Stockholm, August 1999)⁴⁾. Paola Mello, Ph.D.: She received her degree in Electronic Engineering from University of Bologna, Italy, in 1982 and her Ph.D. degree in Computer Science in 1989. Since 1994 she is full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna where she teaches Artificial Intelligence. Her research activity focuses around: programming languages, with particular reference to logic languages and their extensions towards modular and object-oriented programming; artificial intelligence; knowledge representation; expert systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national (Progetti Finalizzati e MURST) and international (UE) research projects in the context of computational logic. Michela Milano, Ph.D.: She is a Researcher in the Department of Electronics, Computer Science and Systems at the University of Bologna. From the same University she obtained her master degree in 1994 and her Ph.D. in 1998. In 1999 she had a post-doc position at the University of Ferrara. Her research focuses on Artificial Intelligence, Constraint Satisfaction and Constraint Programming. In particular, she worked on using and extending the constraint-based paradigm for solving real-life problems such as scheduling, routing, object recognition and planning. She has served on the program committees of several international conferences in the area of Constraint Satisfaction and Programming, and she has served as referee in several related international journals. Marco Gavanelli: He is currently a Ph.D. Student in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. His research interest include Artificial Intelligence, Constraint Logic Programming, Constraint Satisfaction and visual recognition. He is a member of ALP (the Association for Logic Programming) and AI^*IA (the Italian Association for Artificial Intelligence). Evelina Lamma, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on logic programming languages, Artificial Intelligence and software engineering. She was co-organizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. She is a member of the Executive Committee of the Italian Association for Artificial Intelligence (AI^*IA). Currently, she is Full Professor at the University of Ferrara, where she teaches Artificial Intelligence and Fondations of Computer Science. Massimo Piccardi, Ph.D.: He graduated in electronic engineering at the University of Bologna, Italy, in 1991, where he received a Ph.D. in computer science and computer engineering in 1995. He currently an assistant professor of computer science with the Faculty of Engineering at the University of Ferrara, Italy, where he teaches courses on computer architecture and microprocessor systems. Massimo Piccardi participated in several research projects in the area of computer vision and pattern recognition. His research interests include architectures, algorithms and benchmarks for computer vision and pattern recognition. He is author of more than forty papers on international scientific journals and conference proceedings. Dr. Piccardi is a member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter. Rita Cucchiara, Ph.D.: She is an associate professor of computer science at the Faculty of Engineering at the University of Modena and Reggio Emilia, Italy, where she teaches courses on computer architecture and computer vision. She graduated in electronic engineering at the University of Bologna, Italy, in 1989 and she received a Ph.D. in electronic engineering and computer science from the same university in 1993. From 1993 to 1998 she been an assistant professor of computer science with the University of Ferrara, Italy. She participated in many research projects, including a SIMD parallel system for vision in the context of an Italian advanced research program in robotics, funded by CNR (the Italian National Research Council). Her research interests include architecture and algorithms for computer vision and multimedia systems. She is author of several papers on scientific journals and conference proceedings. She is member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter.     

A comparative evaluation of expert system tools

The results of an assessment of the functionality and performance of tools with different architectural approaches are presented. The goal was to gain insight into the advantages and disadvantages of the various architectures. The tools evaluated in the study were (1) the Automated Reasoning Tool for Information Management (ARTIM), (2) the C Language Integrated Production System (CLIPS), (3) the Knowledge Engineering System (KES), (4) Level 5, and (5) VAX OPS5. Emphasis was on tools implemented in languages other than Lisp. KES and Level 5 were found to be inadequate for an application that required constructive problem solving, but both tools could be expected to provide improved performance for problems based on a heuristic classification approach due to their backward-chaining orientation. On the other hand, ART-IM, CLIPS, and VAX OPS5 might prove less effective for heuristic classification problem solving than for synthesis because they lack support for backward chaining. The results reinforce the fact that all expert system tools have strengths and weaknesses and that no single tool is dominant for a wide spectrum of applications or over a wide range of functionality     

A perspective on learning for intelligent educational systems*

Abstract In this paper two observations are the starting points for a proposal as to how learning, rather than computing considerations, can be made the main focus in the development of Intelligent Educational Systems (IESs). These observations are: a) the predominant use of computers in education is for the running of general tool software; and b) the symbolic approach to Artificial Intelligence is being questioned from a situated cognition perspective.
Learning interactions are seen as consisting of a Task Level, and a higher-level Discussion Level comprising planning and evaluation of, and reflection about, Task Level activity. It is proposed that IESs should be developed by adding a module to support Discussion Level interaction with the learner. The Mayday project is described, in which expert human teachers are being studied as they support Discussion Level with a learner working at computer-based lexical activities. It is argued that such studies of human learning is a necessary step towards the development of the proposed IESs.     

机制主义方法与人工智能统一理论——人工智能的新方法与新进展

报导人工智能研究的一项重要进展:由于发现了人工智能研究的"机制主义"模拟方法和"知识的生态学结构",使原来"鼎足三分"的3个主流学说(结构模拟、功能模拟和行为模拟学说)在"机制主义方法"的基础上实现了和谐的统一,形成了统一理论,从而为人工智能的发展提供了新的基础。