A Heuristic Approach to the Discovery of Macro-Operators

This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program, MACLEARN, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance improves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems. MACLEARN is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized Hi-Q puzzle), and tile sliding (generalized Fifteen puzzle). The results indicate that MACLEARN'S filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.     

A Universal Method of Scientific Inquiry

A paradigm of scientific discovery is defined within a first-order logical framework. Within this paradigm, the concept of successful scientific inquiry is formalized and investigated. Among other results, it is shown that a simple method of scientific inquiry is universal in the sense that it leads to success on every problem for which success is in principle possible.     

选择性集成算法分类与比较

选择性集成是当前机器学习领域的研究热点之一。由于选择性集成属于NP"难"问题,人们多利用启发式方法将选择性集成转化为其他问题来求得近似最优解,因为各种算法的出发点和描述角度各不相同,现有的大量选择性集成算法显得繁杂而没有规律。为便于研究人员迅速了解和应用本领域的最新进展,本文根据选择过程中核心策略的特征将选择性集成算法分为四类,即迭代优化法、排名法、分簇法、模式挖掘法;然后利用UCI数据库的20个常用数据集,从预测性能、选择时间、结果集成分类器大小三个方面对这些典型算法进行了实验比较;最后总结了各类方法的优缺点,并展望了选择性集成的未来研究重点。     

任务与兴趣驱动教学法在《计算机网络实用技术》教学中的应用

针对现阶段职业教育中计算机网络专业普遍存在的问题,从兴趣任务驱动教学法入手,以《计算机网络实用技术》课程为例,对课程的教学理念、教学实施过程及实施效果进行了探索。     

实验室网络教学资源与人才培养创新理论的研究

实验室网络教学资源与人才培养创新理论的研究,要灵活多样,寻求多种途径,利用虚拟实验室及网络等现有资源,开展开放式实验实训和校外实训,利用远程教学,将实训教学与职业技能鉴定相结合,以网络资讯平台为基础,通过电视式校园媒体平台,以市场为导向的方针,规划人力资源,以就业为核心的现代化的传输手段,实现适应市场需求的各类人才的培养计划,努力建设一个以高校校园市场为基础的、完善的、能沟通企业、学校和学生的产、教、学信息平台。     

赋权学生:混合式学习模式的应有之意——以通识类课程社会学的教学实践为例

工业化时代的传统教学模式已滞后于后福特主义生产模式对劳动者的需要。混合式学习模式在最近二十年发展起来,具有知识载体的多元化、知识获取的易得性,以及知识形式的丰富性等显著特征。在社会学的课程实践中,运用混合式学习模式,将赋权学生的教学理念融入课前、课中和课后三个阶段,精心的教学设计激发学生的主体意识,取得较好的教学效果。     

Learning experiences from digital laboratory safety training

We present a digital laboratory safety training platform called AALTOLAB which we have developed to address the need for more flexible and engaging ways to teach laboratory safety for chemical engineers. AALTOLAB consists of an interactive, web-based 360° virtual laboratory, integrated with a Moodle-based digital exam with automatic assessment. The modular platform offers different modules from general comprehensive laboratory training to more specialised modules. Students and staff can freely navigate in the laboratory rooms at any time and interact with different action points including interactive videos, slideshows, and minigames. Our technical solution allows teachers even with limited technological skills to easily modify the training materials in real time. The relationship of students’ learning experiences and level of learning is also discussed. We believe that our safety training concept facilitates deep learning and students’ engagement, thus improving safety culture in our laboratories.     

Learning control in spatial coordinates for the path-following of autonomous vehicles

We prove the existence of a P-type (proportional-type) space-learning control, which, on the basis of a kinematic third order nonlinear model of an autonomous nonholonomic vehicle and by a proper choice of the proportional control gain, guarantees asymptotic tracking of planar curves whose uncertain curvature is L$L$-periodic in the curvilinear abscissa. The behavior of a human driver, who repetitively learns the correct action from the past experience in the space, is mathematically reproduced. A stability analysis is presented while simulation results demonstrate the effectiveness of the presented approach.     

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.

Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.     

An intelligent negotiator agent design for bilateral contracts of electrical energy

In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.