Access control policies (ACPs) are essential for creating a secure access control system. ACPs are often studied and specified based on access control models, such as attribute-based access control (ABAC). Moreover, the execution of business process instances is typically recorded in a business process event log. Ensuring conformance with ABAC policies for the process log at the time of post-execution is crucial. To perform conformance testing of ABAC policies for event logs, it is necessary to formalize the ABAC policies. However, this formalization is typically carried out manually, leading to low efficiency and maintainability, as well as a high risk of errors and difficulty in detecting them. Also, the top-down approach for ABAC policy engineering is often less feasible due to the challenges and costs associated with manually developing ABAC policies, which makes it difficult to document security requirements. Besides, there is a lack of an ABAC metamodel that supports the formalization and conformance testing of ABAC policies, and little attention is paid to constructing ABAC policies from existing event logs. This paper presents a fine-grained and highly automated model-driven framework enabling the formalization and conformance testing of ABAC policies for business processes. In our approach, an ABAC metamodel and its patterns are proposed to solve the problems mentioned above. The approach is experimented with and evaluated on three business processes: One simulated and two real-world processes.

Event logs often record the execution of business process instances. Detecting traces in the event logs that do not comply with access control policies, such as role-based access control (RBAC) policies, is essential to ensuring system security. Moreover, process mining has been extensively utilized for security analysis in recent years. However, pattern-based approaches for designing and analyzing RBAC policies in the context of business processes through process mining are notably absent. In this paper, we present a systematic framework for checking the conformance of RBAC implemented in the event logs of business processes with the RBAC policies specified in domain knowledge. To facilitate the representation of the RBAC policies derived from the domain knowledge, we employ an RBAC domain-specific language (DSL) combined with our RBAC-driven object constraint language (OCL) invariant patterns built from the various types of RBAC constraints. The implemented RBAC in an event log is represented as snapshots within our framework. Then, we validate the snapshots with the RBAC policies to be able to detect RBAC conformance issues. The proposed framework is experimented with and evaluated on two business process logs, one simulated log and one real-world event log named “BPI Challenge 2017”.

Privacy-preserving data mining techniques are useful for analyzing various information, such as Internet of Things data and COVID-19-related patient data. However, collecting a large amount of sensitive personal information is a challenging task. In addition, this information may have missing values, which are not considered in the existing methods for collecting personal information while ensuring data privacy. Failure to account for missing values reduces the accuracy of the data analysis. In this paper, we propose a method for privacy-preserving data collection that considers many missing values. The patient data are anonymized and sent to a data collection server. The data collection server creates a generative model and a contingency table suitable for multi-attribute analysis based on expectation-maximization and Gaussian copula methods. Using differential privacy (the de facto standard) as a privacy metric, we conduct experiments on synthetic and real data, including COVID-19-related data. The results are 50--80\% more accurate than those of existing methods that do not consider missing values.

Reflection is a technique that enables a system to inspect or change its structure and/or behavior at runtime. It is a key enabler of many techniques for developing systems that have to function despite rapidly changing requirements and environments. A crucial issue in developing reflective systems is to ensure the correctness of their behaviors, because object-level behaviors are affected by metalevel behaviors. In this paper, we present an extended labeled transition system (LTS), which we call a metalevel LTS (MLTS), that supports data representation of another LTS for use in modeling a reflective tower. We show that two of the existing state reduction techniques for an LTS (symmetry reductionanddivergence-sensitive stutter bisimulation) are also applicable to an MLTS. Then, we introduce two strategies for implementing an MLTS model in Promela, thereby enabling verification with the SPIN model checker. We also present case studies of applying MLTSs to two reflection applications: self-adaptation of a reconnaissance robot system, and dynamic evolution of an Internet-of-things (IoT) system. The case studies demonstrate the applicability of our approach and its scalability improvement through the state reduction techniques.

Anti-spam measures include methods for determining unsolicited email from the email content and methods for using sender information. If it can be determined from the sender's IP address of sender information and the sender's domain name whether the email should be received, it is possible to reduce the processing of the spam filter by the email content that has a high processing load for the determination. This study uses sender authentication technology to identify the sender of forwarded email. We consider that the sender of this forwarded email is the legitimate email sender to receive, and propose to use these as an allow list. In this paper, we propose a method to further improve the method we proposed and reduce misjudgment of the allow list. We verified that this new method is effective by using the log information of the emails actually received.

Many Internet-of-Things (IoT) systems use machine learning techniques, such as deep neural networks. IoT systems can predict attributes, such as age, sex, car speed, human walking speed, and types of animals, using machine learning techniques. Although the functionality of machine learning is undeniable, the prediction accuracy is not always high. When a machine learning model is used to recognize several objects in an object counting system, the estimated count will have a significant error because of the accumulation of the recognition error of each object. In this study, a count estimation method that uses a confusion matrix generated in the training phase was proposed. The proposed method consists of an iterative Bayesian technique with the confusion matrix for count estimation and mitigating over-iterations technique for reducing estimated errors. The proposed method can be used even for a low-accuracy machine learning model. Experiments with synthetic and real data sets were conducted to demonstrate the functionality of the proposed method. The estimation errors of the proposed method were reduced by 64.3% in average compared to the baseline method in the experiments.

In recent years, just about all subjects require students to learn panoramic information. Because the need exists for cross-curriculum learning aimed at relating subject areas, it is useful for multiple-choice questions to include panoramic information for learners. A question including panoramic information refers to content that includes transverse related information and makes respondents grasp the whole knowledge. However, it is costly to manually generate and collect appropriate multiple-choice questions for questioners and learners. Therefore, in this research, we propose a method for the automatic generation of multiple-choice questions including panoramic information using Linked Data. Linked Data is graphical data that can link structured data, and it is used as a technology for data integration and utilization. Some attempts have been made to use Linked Data as a resource for creating teaching material, and the possibility of using Semantic Web technology in education has been verified. In this paper, we aim to realize a system for automatically generating two types of multiple-choice questions by implementing an approach to generating questions and choices. An evaluation method for the generation of questions and choices involves setting indicators for each evaluation item, such as validity and the degree of the inclusion of panoramic information.

Contemporary societies face many urban issues. To address these issues, governments, corporations and individuals should disclose and share their related statistical and sensory data. However, existing published data appear in various formats and contain defects. Therefore, few problems have been solved using these data. In this research, we sought to address this problem, by considering a university campus as a microcosm of society, designed data integration schema, and consolidated data into a knowledge graph. We then, applied and modified existing completion methods. In particular, regarding the bicycle environment, we trained our knowledge graph and evaluated it with the conventional method and our proposed derivative method, respectively. Using approximately 650 parking data with various dates and times, our method correctly estimated 54.5 more bicycles than the conventional method by comparing each time's mean absolute error.

In order to keep up with a changing business environment, organizations need to understand their current business processes and business goals. The business process model and the goal model are complementary elements and are effective means for describing the present state of the organization. There are various methods to repair these models, but in order to analyze the current state of the organization, it is necessary to analyze not only these conceptual models but also logs of organizational daily operations. Although process mining is an effective means, there is no established technique for repairing both goal models and business process models according to logs. In this paper, we propose a pattern-based method to repair goal models dealing with the repair of business process models. By doing so, it is possible to analyze the current situation of business processes and business goals while maintaining the relationship between the goal model and the business process model. We represented the effectiveness of our method by carrying out a case study.

A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: An anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer's objective. Our proposed method was experimentally evaluated using real data sets.

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing numerous tasks at a low cost. The number of workers who process crowdsourcing tasks is increasing along with the expansion of domains in which crowdsourcing is utilized. However, there is insufficient support for crowdsourcing workers, such as education and improvement of their work environment. This problem may be due to crowdsourcing workers being numerous and unspecified, which also makes them easy to employ and terminate. Poor worker management could lead to declining quality of worker records and unjustified worker termination. In this study, we propose a grade-based training method for workers. Our training method utilizes probabilistic networks to estimate correlations between tasks based on worker records for 18.5 million tasks, then allocates pre-learning tasks to workers to raise the accuracy of target tasks according to task correlations. In an experiment, the method automatically allocated 31 pre-learning task categories for 9 target task categories, and after pre-learning task training, we confirmed that target task accuracy increased by 7.8 points on average. This result was comparatively higher than those for pre-learning tasks allocated using other methods, such as decision trees. We therefore confirmed that task correlations can be estimated from a large number of worker records, and that these are useful for grade-based training of low-quality workers.

Crowdsourcing platforms provide an attractive solution for processing numerous tasks at low cost. However, insufficient quality control remains a major concern. In the present study, we propose a grade-based training method for workers. Our training method utilizes probabilistic networks to estimate correlations between tasks based on workers' records for 18.5 million tasks and then allocates pre-learning tasks to the workers to raise the accuracy of target tasks according to the task correlations. In an experiment, the method automatically allocated 31 pre-learning task categories for 9 target task categories, and after the training of the pre-learning tasks, we confirmed that the accuracy of the target tasks was raised by 7.8 points on average. We thus confirmed that the task correlations can be estimated using a large amount of worker records, and that these are useful for the grade-based training of low-quality workers.

Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users' sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies.

Mobile devices that can sense their location using GPS or Wi-Fi have become extremely popular. However, many users hesitate to provide their accurate location information to unreliable third parties if it means that their identities or sensitive attribute values will be disclosed by doing so. Many approaches for anonymization, such as k-anonymity, have been proposed to tackle this issue. Existing studies for k-anonymity usually anonymize each user's location so that the anonymized area contains k or more users. Existing studies, however, do not consider location errors and the probability that each user actually exists at the anonymized area. As a result, a specific user might be identified by untrusted third parties. We propose novel privacy and utility metrics that can treat the location and an efficient algorithm to anonymize the information associated with users' locations. This is the first work that anonymizes location while considering location errors and the probability that each user is actually present at the anonymized area. By means of simulations, we have proven that our proposed method can reduce the risk of the user's attributes being identified while maintaining the utility of the anonymized data.

Participatory sensing, which collects environmental information from mobile phone users, is growing in popularity. The collected information can be used for national policy or decision-making for companies. However, sensing users may violate their privacy. Recent studies have proposed negative surveys which can analyze the attributes of users statistically without precise information about each user's information. The traditional negative surveys need a lot of samples for proper estimation. These days, several types of negative surveys are used that can estimate the distribution of user attributes with a high degree of accuracy. However, privacy levels of these methods are relatively low. Moreover, existing studies assume that the privacy levels of all users are the same. In this paper, we propose a new negative survey that can estimate data distributions with more precision and can be used in a situation where the privacy levels are different based on each user's demand. By simulations of a synthetic and a real data set, we prove that our proposed method can estimate more precisely than existing methods.

The illegal parking of bicycles is an urban problem in Tokyo and other urban areas. The purpose of this study was to sustainably build Linked Open Data (LOD) for the illegally parked bicycles and to support the problem solving by raising social awareness, in cooperation with the Bureau of General Affairs of Tokyo. We first extracted information on the problem factors and designed LOD schema for illegally parked bicycles. Then we collected pieces of data from Social Networking Service (SNS) and websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the missing data in the LOD based on the causal relations from the problem factors. As a result, the number of illegally parked bicycles can be inferred with 70.9 % accuracy. Finally, we published the complemented LOD and a Web application to visualize the distribution of illegally parked bicycles in the city. We hope this raises social attention on this issue.

Current crowdsourcing platforms provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. In the present work, we developed a private crowdsourcing system (PCSS) running in an intranetwork, which allows us to devise quality control methods. We introduce four worker selection methods and a grade-based training method. The four worker selection methods consist of preprocessing filtering, real-time filtering, post-processing filtering, and guess-processing filtering. In addition to a basic approach involving initial training or the use of gold-standard data, these methods include a novel approach, utilizing collaborative filtering techniques. We collected a large amount of vocabulary data for natural language processing (NLP), such as voice recognition and text to speech using PCSS. The quality control methods increased accuracy 32.4 points in collecting vocabulary tasks. We also implemented the grade-based training method to avoid claims of unfair dismissal and shrinkage of the market of crowdsourcing caused by excluding workers. This training method uses Bayesian networks to calculate correlations between tasks based on workers’ records, and then allocates learning tasks to the workers to raise the results of target tasks according to the correlations. In an experiment, the method automatically allocated learning tasks for target tasks, and after the training of the workers, we confirmed that the workers raised the accuracy of target task 10.77 points on average. Therefore, by combining the filtering methods and the training method, task requesters in microtask crowdsourcing can obtain higher-quality results without dismissing valuable workers.

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.

In recent years, several large-scale knowledge bases (KBs) have been constructed, such as YAGO, DBpedia, and Google Knowledge Graph. Although automatic extractio techniques that extract facts and rules from the Web is necessary for constructing such large-scale KBs, incorporation of noisy, unreliable knowledge cannot be unavoidable. Thus, Google Knowledge Vault assigns extracted knowledge with confidence scores based on consistency with the existing KBs. In this paper, we propose a new approach for associating confidence scores with knowledge based on a large amount of raw data for domains, where there is no existing KB. We first construct knowledge in a specific domain as a semantic network, and then design a probabilistic network, that corresponds to the semantic network. To associate the confidence scores with the semantic network, we train the probabilistic network with a large amount of open data, provided by the Osaka central wholesale market in Japan. We also confirm the validity of the confidence scores with the accuracy of reasoning on the probabilistic network. A semantic network associated with confidence scores, that is, a weighted labeled graph is advantageous not only for reducing the noisy, unreliable knowledge with low confidence, but also for making retrieval results ranking on the KB. In the future, probabilistic reasoning on semantic networks may also be possible.

Many surprising recipes which have different the ingredients or the cooking processes from the normal recipes exist in the user-generated recipe sites. The easiest way to find surprising recipes is to use the search function of the recipe sites. However, the title of surprising recipes do not always include the keyword “surprise”. Therefore, we cannot find surprising recipes in an easy way. In this paper, we propose a method to extract surprising recipes from the user-generated recipe sites. We propose RF-IIF (Recipe Frequency-Inverse Ingredient Frequency) based on TF-IDF (Term Frequency-Inverse Ingredient Frequency). First, we calculate the surprising value of the ingredients by using RF-IIF. Then, we calculate the surprising value of each recipe by summing the surprising value of the ingredients appearing in a recipe. Finally, we extract recipes which have high surprising value of the recipe as surprising recipes of the dish category. In the evaluation experiment, the subjects were requested an evaluation about each surprising recipe. As a results, we showed that the extracted recipes were valid recipe and had the element of surprise. And, we showed the usefulness of the our proposed method.

Nowadays, anybody can easily express their opinion publicly through Consumer Generated Media. Because of this, a phenomenon of flooding criticism on the Internet, called flaming, frequently occurs. Although there are strong demands for flaming management, namely, a service to reduce damage caused by a flaming after one occurs, it is very difficult to properly do so in practice. We are trying to keep the flaming from happening. Concretely, we propose methods to identify a potential tweet which will be a likely candidate of a flaming on Twitter, considering public opinion among twitter users. We divide flamings into three categories: criminal episodes, struggles between conflicting values and secret exposures. The first two represent the vast majority of flaming cases. As for the CEs, a Naive Bayes-based method has been promising to identify the cases. As for the SBCVs, we propose a dynamic P/N analysis based on daily polarity, which represents the strength of the polarity of public opinion on a given topic. An experiment using a past flaming case has shown that the method has successfully explained the case as one caused by a gap between the polarity of the tweet and that of public opinion.

When a data holder wants to share databases that contain personal attributes, individual privacy needs to be considered. Existing anonymization techniques, such as l-diversity, remove identifiers and generalize quasi-identifiers (QIDs) from the database to ensure that adversaries cannot specify each individual's sensitive attributes. Usually, the database is anonymized based on one-size-fits-all measures. Therefore, it is possible that several QIDs that a data user focuses on are all generalized, and the anonymized database has no value for the user. Moreover, if a database does not satisfy the eligibility requirement, we cannot anonymize it by existing methods. In this paper, we propose a new technique for l-diversity, which keeps QIDs unchanged and randomizes sensitive attributes of each individual so that data users can analyze it based on QIDs they focus on and does not require the eligibility requirement. Through mathematical analysis and simulations, we will prove that our proposed method for l-diversity can result in a better tradeoff between privacy and utility of the anonymized database.

There is a need for the development of systems that are compliant with laws in public administration, because their administrative activities are based on laws. When new laws are made or existing laws are amended, however, civil servants need to develop or modify the systems in the short time before the laws are issued. Related work in requirements elicitation from the legal texts includes approaches using ontology but there are difficulties in building an ontology for practical use. In this paper we propose pre-defined templates with the expression of functional requirements to identify legal texts, including their functional requirements, and a support tool consisting of two functions, one for automatic summary creation from complicated legal texts and one for the suggestion of the legal texts, including their functional requirements. We have also applied this approach to Japanese laws and have evaluated its accuracy. Our research revealed that using this approach can identify functional requirements with high accuracy.

Recently, urban greening and agriculture have been receiving increased attention, but the cultivation of greenery is not a simple matter in a restricted space. Therefore, we propose Flower Voice, a voice assistant for smartphones that answers questions users will be faced on site, and provides a mechanism for registering the work. The system uses Linked Open Data as a knowledge source, and features improvemnt of accuracy based on user feedback and acquisitions of new data by user participation. This paper presents Plant LOD and an architecture of the sytem, and then evaluates the improvement of the accuracy.

Software evolution is an essential activity that adapts existing software to changes in requirements. Localizing the impact of changes is one of the most efficient strategies for successful evolution. We exploit requirements descriptions in order to extract loosely coupled components and localize changes for evolution. We define a process of elaboration for the goal model that extracts a set of control loops from the requirements descriptions as components that constitute extensible systems. We regard control loops to be independent components that prevent the impact of a change from spreading outside them. To support the elaboration, we introduce two patterns: one to extract control loops from the goal model and another to detect possible conflicts between control loops. We experimentally evaluated our approach in two types of software development and the results demonstrate that our elaboration technique helps us to analyze the impact of changes in the source code and prevent the complexity of the code from increasing.

Recently, the video hosting is one of the most popular services on the Web. The service user can search movies by title, tag, date, and so on. However, the user can hardly obtain information on a particular scene of a movie. Thus, it is very difficult to determine whether a movie contains interesting scenes or not. This research aims to provide information in order to help a user to choose a movie. Concretely, this paper proposes a method for labeling scenes in a movie using a video hosting service called "Nico Nico Douga". The proposed method extracts important scenes in a movie from "Nico Nico Douga" based on the statistics of social annotations attached to them. Also, using the characteristics of folksonomy the authors extract feature words for labeling. Moreover, in order to take advantage of the feature words, the authors estimate their semantic categories. We use the attached comments themselves to label the important scenes. In order to select the comments, the authors take into account the importance of the feature words with semantic categories in a scene. Finally, the authors carry out experiments to evaluate the proposed method and to discuss future works.

Enterprise information systems are currently developed as Web-based applications in the service-oriented architecture style, which is implied by the stack of Web services standards. One of the most critical aspects in enterprise information systems is to maintain their continuous operation as long as possible. Conventional service invocation mechanism identifies Web services with similar signatures found by a service discovery as distinctively different ones, resulting in the suspension of a service requester and the modification of its invocation procedures. In this paper, we propose a reasonable solution to realize runtime invocation of Web services, regarding structural similarities in service signatures as subsumption relations of XML scheme. Given Web services with subsumed signatures, this solution enables a service requester to invoke the Web services by translating the scheme types of the invocation parameters.

As wide-area open networks like the Internet and intranets grow larger, mobile agent technology is attracting more attention. Agents are units of software that can deal with environmental changes and the various requirements of open networks through features such as autonomy, mobility, intelligence, cooperation, and reactivity. However, since the usual development methods of the agent systems are not sufficiently investigated, the technology is not yet widespread. In previous papers, we introduced behavior patterns for mobile agent systems and the development method. The behavior patterns represent typical and recurring structures and behaviors of agents. The patterns are classified according to their appropriate architectural levels and the degree to which they depend on specific agent platforms. We evaluate the advantages of our method from the development process viewpoints. Our evaluation focuses on the development efficiency, the applicability, the extensibility and the understandability of our method.

This paper reports a directory server in a distributed middleware for realizing the interoperability between a wide variety of mobile agent systems. The ODP trader, an ITU-T recommendation for direction service, is customized for the specific requirements. The key ideas are (1) to extend the managed information so that program codes can be members, and (2) to provide Proxy Offer for utilizing services located outside the directory. Performance measurement shows that Proxy Offer is more efficient than dynamic property of the ODP trader in the current application.

We propose a new concept called Rental Service. Recently, Rental Application has been applied as ASP (Application Service Provider). For the next step in advancing ASP, we would prefer to provide Service which creates new functions, coordinating and facilitating already-existing functions, provided by many applications in the network. In this paper, we firstly made a list of problems for adding such service into the current ASP systems. Then, we developed a framework as a solution to the problems, in which the service itself is regarded as an agent. Moreover, we introduced a practical system developed by the framework, and finally examined its effectiveness for the above problems through that practical case.

Interoperability between different systems is becoming a more important issue as computer networks expand. In this paper, we propose Bee-gent (Bonding and Encapsulation Enhancement Agent), a distributed system development-framework that aims to provide coordination between RDBMSs, legacy systems, software packages, and so forth. The Bee-gent coordination mechanism is an integration of interaction protocols that explicitly describe the relationship of the elements of a distributed system. Our approach is to split interaction protocols into the flow for the overall problem solving sequence and the location dependent local flow. Further mobile mediation agents coordinate the behavior of each element according to the flow of the problem solving sequence. We present an example of system development using Bee-gent that demonstrates how maintenance becomes simpler in the face of modifying the distributed system components.

As wide-area open networks such as the Internet and intranets grow larger, agent technology is attracting more attention.Agents are units of software that can deal with environmental changes and the various requirements of open networks through features such as autonomy, mobility, intelligence, cooperation, and reactivity.However, since the usual development method of the agent systems is not sufficiently investigated, the technology is not yet widespread.This paper proposes a method of agent system development based on agent patterns that represent typical and recurring structures and behaviors of agents.The agent patterns are classified according to their appropriate architectural levels and the degree to which they depend on specific agent platforms.Our method enables developers to design agent systems efficiently since' they can construct complicated system architectures and behaviors by dividing the design process into two architectural levels and applying the appropriate agent patterns.In addition, the higher level designs are independent of specific agent platforms and can be therefore easily reused.

This paper proposes a method of agent system development based on agent patterns that represent typical and recurring structures and behaviors of agents. The agent patterns are classified according to their appropriate architectural levels and the degree of their dependence on specific agent platforms. Our method enables developers to design agent systems efficiently since they can construct complicated system architectures and behaviors by dividing the design process into two architectural levels and applying the appropriate agent patterns. In addition, the higher level designs are independent of specific agent platforms and can be therefore easily reused.

We propose two new concepts, evolutional agent and field. The main purpose of the work is to provide a framework for building software which adapts to changes of requirements autonomously. In open networks, the adaptability to changes of requirements or environments is essential, while there are many free applications and components to serve various requirements. Focusing on this point, we introduce concurrent objects with meta-base architecture, agents, and fields, another kind of object. In our model, adaptive agents, called evolutional agents, adapt to changes by traversing a network and acquiring components as their own functions. Fields are receptacles of software components in networks. Agents evolve into enhanced ones by traveling to fields and acquiring components from the fields. Flage is a framework for building the software architecture based on these concepts.

Although many CASE tools for object-oriented methods (OO CASE tools) have been proposed, few, if any, can verify that the constructed analysis and design models actually match the requirements of the system being developed. In order to realize this kind of verification, we propose a software development method amalgamating OO CASE tools and algebraic specification techniques. We are developing an experimental system named COOAD (CASE tool for Object-Oriented Analysis and Design) in order to examine the effectiveness of our proposition. COOAD supports object-oriented analysis and design, verification of the analysis and design, and generation of code. In this paper, we propose the software development method, introduce COOAD, and illustrate the facilities of COOAD with an example.

Although many CASE tools for object-oriented methods (OO-CASE tools) have been proposed, few, if any, can verify that the constructed analysis and design models actually match the requirements of the system being developed. In order to realize this kind of verification, we propose a software development method amalgamating OO-CASE tools and algebraic specification techniques. We are developing an experimental system named COOAD (CASE tool for Object-Oriented Analysis and Design) in order to examine the effectiveness of our proposition. COOAD supports object-oriented analysis and design, verification of the analysis and design, and generation of code. In this paper, we propose the software development method, introduce COOAD, and illustrate the facilities of COOAD with several examples.>

We propose Flage, a language for building software modules (agents) which adapt to a change of environment in an open distributed system. Environments are defined as multiple agents and specifications which consist of constraints for cooperation among the agents. Flage provides a description of environments by the notion of agents and fields. We describe the concept of Flage and the adaptation technique.>