The article reviews the book “Information Rules: A Strategic Guide to the Network Economy,” by Carl Shapiro and Hal R. Varian.

Information Rules: A Strategic Guide to the Network Economy

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

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Research-paper recommender systems: a literature survey

This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce.

Agent-Mediated Electronic Commerce

Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified

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Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Mobile agent technology is an emerging technology that allows easier design, implementation, and maintenance of distributed systems. Mobility enables agents to reduce network load, overcome network latency, and handle network disconnections. Few mobile agent systems support Prolog programming, and no system supports GUI programming on Prolog programming. Prolog's first order logic representation has benefits for constructing mobile agents. In this paper, we present a Prolog-based programming framework, iML, for constructing mobile agents with graphical user interfaces. The iML framework consists with three parts, an integrated development environment(IDE), runtime classes for standalone deployment, and an agent code generator. We evaluate the iML framework from two viewpoints: migration performance and the usability of the framework. Experimental results show that the iML method is 6.7 times smaller in size, and 6.0 times faster in migration speed in comparison with the Java serialization technique. We used the iML framework at our knowledge programming lecture. During the lecture, 60 students attended after which 45 students could create GUI-based mobile agents, and 16 students could create excellent applications by using the framework.

iML: a logic-based framework for constructing graphical user interface on mobile agents

Forming effective coalitions is a major issue in multiagent systems. The existing coalition structure generation (CSG) algorithms fail to determine the structure 's robustness because agent withdrawals cannot be identified in all the coalition structures. The coalition lattice, which was proposed in a previous study, provides an evaluation of the coalition structure's robustness and enables to apply the existing CSG algorithms. However, to construct the coalition lattice, the lower bounds for the coalition utilities must be considered. In the present study, robustness is redefined and an improved conversion algorithm is proposed. In particular, a method to estimate the upper and lower bound of the coalition values is presented. Furthermore, an effective algorithm to convert the problem of finding the coalition structures' robustness into a CSG problem is proposed. A case-study evaluation of the proposed method shows that it constructs the coalition lattice more efficiently.

A Local Adaptive Perfect Coalition Structure Generation Problem Considering Coalition Breakup

We propose a Generational Layered (GL) canvas mechanism to reduce delays of synchronization for collaborative Web applications. The delays consist of network delays and drawing delays. The network delays are well-argued topic by various researches. We focus on the drawing delays, which are a primary bottleneck of synchronization of objects on canvas. In our previous work, we have proposed a Drawing-Frequency based Layered (DFL) canvas mechanism. The DFL canvas mechanism solved the drawing problem but requires to manually assign a parameter how frequently an object is redrawn, respectively. The GL canvas mechanism achieves the automatic assignment of canvas objects to elicit high performance when the drawing-frequency is unknown. The automatic assignment algorithm is inspired by the generational garbage collection. We implemented and evaluated the mechanism, and then the mechanism elicited higher performance up to 3.5 times faster when the canvas had the sufficient number of layers. Developers can create fast Web applications using the mechanism. The mechanism enhances the potential of the Web applications running within low performance devices such as tablets and smartphones.

Generational Layered Canvas Mechanism for Collaborative Web Applications

Nurse Scheduling System based on Dynamic Weighted Maximal Constraint Satisfaction Problem-Solution Stability based on the Provisional Constraint-

Summary In this paper, we propose an intelligent notification of a new e-mail, and describe an implementation of the notification system. There are two ways to read e-mails by using a computer. One way is a mail system on a user’s personal computer. The other way is a web mail system on a web server. Using a web mail system is better for users because they can read e-mails by using other person’s personal computers. A web mail system has a problem. A web mail system is low immediacy. Users have to look at a web page of a web mail system again and again to check new e-mails. To solve this problem, we implemented a web mail system WisdomMail. WisdomMail is a web application based on a web agent. WisdomMail has a function that shows a notification of new e-mails without special plug-ins in a web browser. This system uses the web agent framework MiSpider. MiSpider enables developers to implement a web agent having a persistent function, a message passing function, and a graphical user interface. By using MiSpider, a notification of a new e-mail appears on a web page that a user is browsing. A feature of this system is that a notification of a new e-mail appears on any web pages. Also, the notification system automatically adjusts notification timing and a position of a notification on a web page. By using the notification system, users can read new e-mails without looking at a web page of a web mail system. Finally, we evaluated the scalability of the notification system and show experimental results.

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Toramatsu Shintani

Papers

iML: a logic-based framework for constructing graphical user interface on mobile agents

A Local Adaptive Perfect Coalition Structure Generation Problem Considering Coalition Breakup

Generational Layered Canvas Mechanism for Collaborative Web Applications

Nurse Scheduling System based on Dynamic Weighted Maximal Constraint Satisfaction Problem-Solution Stability based on the Provisional Constraint-

On a Web Mail System based on Web Agents