PLANGENT: an approach to making mobile agents intelligent

Network environments give computer users the option of employing distributed information and services to complete a task. However, gathering information and using services distributed in networks requires knowing exactly what kinds of information and services are required for a task, where they are, and how they can be obtained or utilized. Tracking down the answers to these questions can be difficult, time consuming tasks for users. Mobile agent technology is expected to release them from having to do so. Instead, “intelligent” mobile agents will comprehend the user's requirements, search network nodes autonomously for appropriate information and services, and return with the answers. But several problems must be solved before we can expect agents to perform such actions effectively. We focus on the question of intelligence as a prerequisite for agent functions. What sort of intelligence is expected of agents? We have adopted a model based on the ability to make flexible plans. Specifically, we think mobile agents must be able to: understand user requirements; plan actions that will satisfy the requirements act according to the plan; modify the plan according to actual conditions when they differ from those initially expected; and execute the modified plan. We have implemented these functions in the Plangent system and validated their effectiveness in several example applications. We describe how we combined these planning functions with mobile agent facilities, and show how the agents behave intelligently in an example application of personal travel assistance     

Secretion-recapture process of apolipoprotein E in hepatic uptake of chylomicron remnants in transgenic mice

To investigate the role of apoE in hepatic uptake of chylomicron remnants, we studied chylomicron metabolism in transgenic mice overexpressing apoE in the liver. Plasma clearance of injected 125I-labeled human chylomicrons was fivefold faster in transgenic mice than in controls. Immunohistochemistry demonstrated that apoE was specifically localized at the basolateral surface of hepatocytes from fasted transgenic mice. After injection of a large amount of chylomicrons, the density of the cell surface apoE was markedly reduced and vesicular staining was observed in the cytoplasm, suggesting that the cell surface apoE was used for hepatic endocytosis of chylomicrons and remnants. Polyacrylamide gel analysis of chylomicrons and remnants that had been reisolated from plasma and from liver membrane after the injection of chylomicrons showed the particles to be enriched with apoE mainly after their influx into the liver rather than during their residence in plasma. These results provide strong evidence for the secretion-recapture process of apoE, whereby chylomicron remnants enter the sinusoidal space, acquire apoE molecules, and subsequently are endocytosed. Data from experiments with very low density lipoprotein and LDL showed that this system is specific for chylomicron remnants.     

KOSI — An integrated system for discovering functional relations from databases

This paper describes an integrated system called KOSI (Knowledge Oriented Statistic Inference) for discovering functional relations from databases. The key feature of KOSI is that AI techniques and statistical methods are cooperatively used in the discovery process based on incipient hypothesis generation and evaluation, in which multi-search is performed in meta control. Two types of search, which use respectively different type of heuristics, are used inattribute calculation which is a kind of operation in which new attribute is generated as a function of the existing attributes. Third type of search, which is based on regression analysis, is mainly used for evaluating/selecting the best functional relation from the results of attribute calculation. Furthermore, a model-base and meta/domain knowledge are used for controlling the multi-search, and the methods of forming scopes/clusters can be used as a step of pre-processing before the search. KOSI is organized into multi-level structure for knowledge discovery and management efficiently. We try to provide a systematic manner of discovering functional relations, to support qualitative/quantitative discovery, and develop a more robust, general-purpose discovery system.     

Knowledge-based man-machine systems

A new method of increase user gain in man-machine interaction is discussed. In particular, attention is directed to describing a model in the computer. An intelligent system provided with the knowledge base and the inference mechanism is proposed for the purpose, whereby illustration is given of the system describing both structure and attributes of the real object in the same framework.     

Method to assist the building and expression of subjective concepts and its application to design problems

The paper discusses how computers can assist human creativity. A support system has been built and applied to the domain of automobile design. The system uses multidimensional data and statistical methods, and configures a space for inspiring the users' design activity. In the first step, it assists them in forming individual design concepts. In the second step, it assists them in deciding their target design concepts. It has been confirmed that it works effectively through experiments. Some of the open questions on creativity support systems are also discussed.     

Development of a distributed power outage scheduling system by using bee‐gent

The Power Outage Scheduling Problem is a distributed constraint satisfaction problem in which scattered local power stations have to make schedules that are consistent with one another. However, synchronous backtrack algorithms, a well‐known method for distributed constraint satisfaction problems, have difficulty handling rapid schedule adjustments and impartial assignment of power station schedules. Thus, we propose two kinds of heuristics: parallel assignment and multiple priority strategies. We also developed a distributed system for the power outage scheduling problem which makes use of the above heuristics in order to ensure efficiency. The system is based on Bee‐gent (Bonding and Encapsulation Enhancement aGENT framework) and consists of schedulers for each power station and mediation agents which have cloning and merging functions to support the implementation of the heuristics. The result of the experiment shows an improvement when handling rapid adjustment and impartiality issues and indicates that this improvement is provided with reasonable computational overhead. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 139(3): 62–72, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.1161     

Framework of knowledge-based systems : Multiple meta-level architecture for representing problems and problem-solving processes

Currently available expert systems have a performance limit because of the lack of capability to describe problems and problem-solving methods. It is closely related with knowledge representation language, but this is not the only concern with this issue. Real world problems and problem-solving methods are not so simple as to be represented always in the same way by the same language. Their representations must be different depending on various factors involved in the problems themselves and the situations these problems are surrounded with. In this paper, the author discusses first the intrinsic nature of problem representation and problem-solving process representation. The requirements for and the conceptual framework of a knowledge-based system that is suited for dealing with various problems then become apparent quite naturally. The author asserts that a multiple meta-level architecture is necessary as well as a knowledge-representation language that can describe complex data structures as the basic framework of knowledge-based systems.