Engineering artificial intelligence software

Artificial Intelligence (AI) software is a reality, but only for limited classes of problems. In general, AI problems are significantly different from those of conventional software engineering. The differences suggest a different program development methodology for AI problems: one that does not readily yield programs with the desiderata of practical software (reliability, robustness, etc.). In addition, the problem of machine learning must be solved (to some degree) before the full potential of AI can be realized, but the resultant self-adaptive software is likely to further aggravate the software crisis. Realization of the full potential of AI in practical software awaits some prerequisite breakthroughs in both basic AI problems and an appropriate AI software development methodology.     

基于Agent的RoboCup数学建模

随着计算机软硬件技术的快速发展,人工智能的研究领域出现了一些新的机遇和挑战。Agent作为一种新的软件开发范型,受到了AI(ArtificialIntelligence)研究者的青睐。论文根据Agent的信念(Belief)、愿望(Desire)和意图(In-tention),给出了RoboCup中射门函数的数学模型。     

Describing generic expertise models as object-oriented analysis patterns: the heuristic multi-attribute decision pattern

We report on work concerning the use of object-oriented analysis and design (OAD) methods in the development of artificial intelligence (AI) software applications, in which we compare such techniques to software development methods more commonly used in AI, in particular CommonKADS. As a contribution to clarifying the role of OAD methods in AI, in this paper we compare the analysis models of the object-oriented methods and the CommonKADS high-level expertise model. In particular, we study the correspondences between generic tasks, methods and ontologies in methodologies such as CommonKADS and analysis patterns in object-oriented analysis. Our aim in carrying out this study is to explore to what extent, in areas of AI where the object-oriented paradigm may be the most adequate way of conceiving applications, an analysis level 'pattern language' could play the role of the libraries of generic knowledge models in the more commonly used AI software development methods. As a case study we use the decision task — its importance arising from its status as the basic task of the intelligent agent — and the associated heuristic multi-attribute decision method, for which we derive a corresponding decision pattern described in the unified modelling language, a de facto standard in OAD.     

AI research in China: 50 years down the road

Chinese researchers have been conducting AI research for more than four decades. Here, we sample some of the most promising areas Chinese AI researchers are studying and predict related future activities. The AI areas research area follows: automatic geometrical theorem proving: beyond mathematical mechanization, intelligence science: toward a molecular-level understanding, large-scale knowledge processing: an open approach, computer-facilitated art and animation: from research to industry, knowledge as a commodity: from software to Knowware.     

Rapid goal-oriented automated software testing using MEA-graph planning

With the rapid growth in the development of sophisticated modern software applications, the complexity of the software development process has increased enormously, posing an urgent need for the automation of some of the more time-consuming aspects of the development process. One of the key stages in the software development process is system testing. In this paper, we evaluate the potential application of AI planning techniques in automated software testing. The key contributions of this paper include the following: (1) A formal model of software systems from the perspective of software testing that is applicable to important classes of systems and is amenable to automation using AI planning methods. (2) The design of a framework for an automated planning system (APS) for applying AI planning techniques for testing software systems. (3) Assessment of the test automation framework and a specific AI Planning algorithm, namely, MEA-Graphplan (Means-Ends Analysis Graphplan), algorithm to automatically generate test data. (4) A case study is presented to evaluate the proposed automated testing method and compare the performance of MEA-Graphplan with that of Graphplan. The empirical results show that for software testing, the MEA-Graphplan algorithm can perform computationally more efficiently and effectively than the basic Graph Planning algorithm.

智能软件定义网络

近年来,人工智能(artificial intelligence,简称AI)以强劲势头吸引着学术界和工业界的目光,并被广泛应用于各种领域.计算机网络为人工智能的实现提供了关键的计算基础设施.然而,传统网络固有的分布式结构往往无法快速、精准地提供人工智能所需要的计算能力,导致人工智能难以实际应用和部署.软件定义网络(so...     

Human decision making & the symbolic search space paradigm in AI

In this paper I shall describe the symbolic search space paradigm which is the dominant model for most of AI. Coupled with the mechanisms of logic it yields the predominant methodology underlying expert systems which are the most successful application of AI technology to date. Human decision making, more precisely, expert human decision making is the function that expert systems aspire to emulate, if not surpass.Expert systems technology has not yet proved to be a decisive success — it appears to fare better in some areas of human expertise than others. As a result subdomains of human expertise are variously categorised and we shall examine a few of the suggested classification schemes. A particular line of argument explored is one which maintains that certain types of human decision making, at least, are not adequately approximated by the symbolic search space paradigm of AI. Furthermore, attempts to project this inadequate model of human decision making via implementations of expert systems will be detrimental to both our image of ourselves and the future possibilities for AI software.Finally, we examine one possible route to the realization of AI, perhaps even practical applications of AI, that is a significant alternative to the model offered by the symbolic search space paradigm.     

Does AI have a methodology which is different from software engineering?

This paper argues that the conventional methodology of software engineering is inappropriate to AI, but that the failure of many in AI to see this is producing a Kuhnian paradigm crisis. The key point is that classic software engineering methodology (which we call SPIV: Specify-Prove-Implement-Verify) requires that the problem be capable of being circumscribed or surveyed in a way that it is not, for areas of AI, like natural language processing. In addition, it also requires that a program be open to formal proof of correctness. We contrast this methodology with a weaker form complete Specification And Testability (SAT — where the last term is used in a strong sense: every execution of the program gives decidably correct/incorrect results) which captures both the essence of SPIV and the key assumptions in practical software engineering. We argue that failure to recognize the inability to apply the SAT methodology to areas of AI has prevented development of a disciplined methodology (which is unique to AI and which we call RUDE: Run-Understand-Debug-Edit) that will accommodate the peculiarities of AI and also yield robust, reliable, comprehensible, and hence maintainable AI software.     

Towards the design and implementation of computer-modeled opponents for use in entertainment and educational software: Some initial considerations

While advances in computer technology have led to major improvements in the presentation and appearance of modern computer games, there has been no equivalent improvement in the artificial intelligence (AI) performance of these games. Traditional AI designs have had surprisingly little impact on the development of game-based intelligent systems. New approaches to AI, particularly those using autonomous software agents encapsulated in software robots (softbots), have the potential to make an enormous impact on game-based AI systems.Softbot-based autonomous software agents provide a framework capable of supporting psychologically based models of human behavior in a game environment. Used in this manner, softbots provide a new set of tools to explore the synthesis of human-like reasoning in complex and dynamic environments.This paper explores some initial attempts to develop an exploratory, game-based, experimental environment capable of hosting softbot-based, psychological models of human behavior. Some preliminary work in the design and implementation of a simple, extensible, softbot-based, computer-modeled opponent is also discussed.     

人工智能与软件工程的交叉研究

本文讨论了人工智能和软件工程之间的交叉领域，探索了二个领域间相互作用的主要形式，并讨论了基于AI的支持环境和实际软件中的AI机制．AI和SE的结合将改变软件生产的被动局面，导致新的软件开发规范的形成，因此对交叉领域的探索具有一定意义．     

Intelligent control of external software systems

This paper focuses on the relatively unexplored set of issues that arises when an intelligent agent attempts to use external software systems (EESs). The issues are illustrated initially in the context of the complex agent-ESS interactions in an engineering design example. Approaching the area from the perspective of artificial intelligence (AI) research, we find that in general, agent-ESS interactions vary widely. We characterize the possible variations in terms of performance capabilities required, skill levels at which performance is exhibited, and knowledge sources from which capabilities can be acquired. We are exploring these variations using Soar as our candidate AI agent; the document briefly describes seven Soar-based projects in early stages of development, in which agent-ESS issues are addressed. We conclude by placing agent-ESS research in the context of other work on software technology, and discuss the research agenda we have set for ourselves in this area.     

Adopting Software Engineering Trends in AI

Designing and developing reliable, robust, well-architected, and easy-to-extend software applications or tools in any field requires conformance to sound principles and rules of software engineering. Intelligent systems, especially AI development tools, are no exception. Although AI has always been a wellspring of ideas that software engineering has later adopted, most of its gems remain buried in laboratories, available only to a few AI practitioners. This paper believes that AI tools should be integrated with mainstream SE tools and thus become more widely known and used. To that end, this paper presents the development of Air, an integrated AI development environment based on model-driven-architecture concepts. Using the philosophy of MDA in Air, familiar and mainstream software technologies are used and expanded with new functionalities     

Deus est machina [artificial intelligence]

What happens if the artificial intelligence community, in its quest to build intelligent systems, succeeds too well and creates an AI whose intelligence exceeds the threshold marked out by our own? Up to now, it is humans who develop the software and hardware and who drive all progress in capability. After crossing the threshold, however, the AI itself will rapidly augment its own capabilities. What's the intuition here? Although we use technology to help us conceptualize, design, and build today's computers and software there's no doubt that we remain in the driver's seat. But imagine the software design process reaching a level of complexity at which human designers exert only executive oversight.     

The Comprehensibility Theorem and the Foundations of Artificial Intelligence

Problem-solving software that is not-necessarily infallible is central to AI. Such software whose correctness and incorrectness properties are deducible by agents is an issue at the foundations of AI. The Comprehensibility Theorem, which appeared in a journal for specialists in formal mathematical logic, might provide a limitation concerning this issue and might be applicable to any agents, regardless of whether the agents are artificial or natural. The present article, aimed at researchers interested in the foundations of AI, addresses many questions related to that theorem, including differences between it and results of Gödel and Turing that have sometimes played key roles in Minds and Machines articles. This study also suggests that—if one is willing to assume a thesis due to Donald Knuth—the Comprehensibility Theorem is the first mathematical theorem implying the impossibility of any AI agent or natural agent—including a not-necessarily infallible human agent—satisfying a rigorous and deductive interpretation of the self-comprehensibility challenge. Some have pointed out the difficulty of self-comprehensibility, even according to presumably a less rigorous interpretation. This includes Socrates, who considered it to be among the most important of intellectual tasks. Self-comprehensibility in some form might be essential for a kind of self-reflection useful for self-improvement that might enable some agents to increase their success. We use the methods of applied mathematics, rather than philosophy, although some topics considered could be of interest to philosophers.     

人工智能在智能教学系统中的应用

智能教学系统(ITS)是人工智能与教育结合的主要形式。本文在总结国际上相关研究成果的基础上，结合我们在开发智能多媒体汉德语言教学系统《二十一世纪汉语》的过程中累积的实践经验，介绍了ITS的历史、结构和主要技术，着重讨论了人工智能技术与方法在其中的应用，并指出了当今这个领域上存在的一些问题，在文章最后，提出了今后研究工作的努力方向。     

Intelligent software product line configurations: A literature review

A software product line (SPL) is a set of industrial software-intensive systems for configuring similar software products in which personalized feature sets are configured by different business teams. The integration of these feature sets can generate inconsistencies that are typically resolved through manual deliberation. This is a time-consuming process and leads to a potential loss of business resources. Artificial intelligence (AI) techniques can provide the best solution to address this issue autonomously through more efficient configurations, lesser inconsistencies and optimized resources. This paper presents the first literature review of both research and industrial AI applications to SPL configuration issues. Our results reveal only 19 relevant research works which employ traditional AI techniques on small feature sets with no real-life testing or application in industry. We categorize these works in a typology by identifying 8 perspectives of SPL. We also show that only 2 standard industrial SPL tools employ AI in a limited way to resolve inconsistencies. To inject more interest and application in this domain, we motivate and present future research directions. Particularly, using real-world SPL data, we demonstrate how predictive analytics (a state of the art AI technique) can separately model inconsistent and consistent patterns, and then predict inconsistencies in advance to help SPL designers during the configuration of a product.