The 'what' and 'how' of learning in design, invited paper

Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process

Space station advanced automation

In the development of a safe, productive and maintainable space station, Automation and Robotics (A and R) has been identified as an enabling technology which will allow efficient operation at a reasonable cost. The Space Station Freedom's (SSF) systems are very complex, and interdependent. The usage of Advanced Automation (AA) will help restructure, and integrate system status so that station and ground personnel can operate more efficiently. To use AA technology for the augmentation of system management functions requires a development model which consists of well defined phases of: evaluation, development, integration, and maintenance. The evaluation phase will consider system management functions against traditional solutions, implementation techniques and requirements; the end result of this phase should be a well developed concept along with a feasibility analysis. In the development phase the AA system will be developed in accordance with a traditional Life Cycle Model (LCM) modified for Knowledge Based System (KBS) applications. A way by which both knowledge bases and reasoning techniques can be reused to control costs is explained. During the integration phase the KBS software must be integrated with conventional software, and verified and validated. The Verification and Validation (V and V) techniques applicable to these KBS are based on the ideas of consistency, minimal competency, and graph theory. The maintenance phase will be aided by having well designed and documented KBS software

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on significant domain expertise.Comment: updated version, incorporating conference feedbac

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Enabling Value co-creation with customers through Artificial Intelligence: A case study approach

The digital movement has radically altered how manufacturing companies interact with their customers. By using Artificial Intelligence (AI), companies have drastically changed their value co-creation (VCC) strategies. We adopt a case study approach and engage with an original equipment manufacturer (OEM) to get a grasp of the phenomenon. Among the data collection methods we assume, we conduct semi-structured interviews with the company project team and its customer base. In addition, we collect secondary data and run focus groups within the studied firm\u27s management team. This research in progress will advance a framework linking VCC with service maturity and identify the performance metrics required for the AI-based journey. Such a framework may assist practitioners in building services based on AI and VCC. Ultimately, we plan to offer theoretical implications to progress the AI and VCC debate and propose future research suggestions

Attempts to regulate artificial intelligence: regulatory practices from the United States, the European Union, and the People's Republic of China

A század egyik legmegdöbbentőbb és előremutató vívmánya a mesterséges intelligencia, vagy röviden az MI. A mesterséges intelligencia a gépek általi intelligencia, ami szemben áll az emberek és más állatok által mutatott természetes intelligenciával. Egyre több iparág alkalmazza a mesterséges intelligenciát, és az elkövetkező években várhatóan tovább fog nőni a számuk. Az MI alkalmazások segíthetnek az embereknek a bonyolult problémák elemzésében és a hatékony megoldások meghatározásában. Emellett az MI technológiákat egyre több iparágban és vállalkozásban tervezik alkalmazni, ami új területek kialakulását és újfajta technológiák fejlesztését ösztönzi. A különböző potenciális előnyök ellenére az MI algoritmusok fejlesztése és alkalmazása néhány jelenlegi és jövőbeli kihívást is felvet. Ezért különösen fontos odafigyelni arra, hogy hogyan történik az algoritmusok fejlesztése és alkalmazása. Ha ezt gondatlanul teszik, a technológiával való helytelen bánásmódnak súlyos következményei lehetnek. Ráadásul további akadályok is felmerülnek, mivel a mesterséges intelligenciával kapcsolatos egyik legjelentősebb probléma a növekvő komplexitás, ami megnehezíti az alkalmazott algoritmusok megértését és értékelését. Ennek eredményeképpen a szabályozás kidolgozásakor meg kell vizsgálni a mesterséges intelligenciával kapcsolatos főbb etikai kérdéseket. A túlzott szabályozás továbbá gátolhatja az innovációt és akadályozhatja a jobb MI alkalmazások fejlesztését. Biztosítani kell, hogy a fejlesztés etikusan történjen, és az egész emberiség javát szolgáló módon használják, ha azt akarjuk, hogy a technológia minden előnyét kiaknázzuk. E szempontok alapján a kutatás célja, hogy összehasonlítsa és szembeállítsa az USA, az EU és Kína által elfogadott különböző szabályozási stratégiákat

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class