Motivated learning agent model for distributed collaborative systems

The paper develops and discusses a theoretical model for collaborative design systems based on motivated learning agents, however, with a novel self-development module to help the system improve itself. Self-Development Agent based on previous work is an intelligent agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. Two case studies with different system setups are described in detail to help better understand this model. The theoretical model is not limited to the specific systems described in this paper, but could be adapted to other collaboration systems as well.     

A computational knowledge representation model for cognitive computers

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.     

A distributed learning model for Freshwater Ecology practical classes

Abstract This paper describes a case study where a distributed learning model was used to enhance the traditional teaching of a freshwater ecology practical class by providing students with a student-centred constructivist environment. Prior to the practical session, a website was created and students were encouraged to think about the morphological adaptations of stream organisms. They were then divided into groups and taken to a real stream to collect specimens and bring them back to the laboratory for further examination and discussion. After the practical, each student group was required to produce a web page concerning the adaptations of the stream organisms to answer a set of online tutorial questions, and to participate in web-based discussion. Feedback by questionnaire showed that the great majority of students enjoyed working with the website and found the content very useful. More than half of the students preferred this type of web-assisted, student-centred practical to conventional practicals, while about a third showed no special preference between the two approaches.     

Computer systems for distributed and distance learning

Abstract Network-based learning is now such an important area that it would seem timely to examine progress to date and to draw conclusions regarding the direction of further research. This paper is the result of a survey of computer systems for distributed and distance learning, focusing on projects that help to illustrate the evolution of this important field. An examination such as this is important in its own right as a resource for other researchers wishing to pursue the subject further, but the survey also helps to highlight some of the major trends of past projects and to suggest some of the ways in which progress may be made in the future.     

A computational model for developing semantic web-based educational systems

Recently, some initiatives to start the so-called semantic web-based educational systems (SWBES) have emerged in the field of artificial intelligence in education (AIED). The main idea is to incorporate semantic web resources to the design of AIED systems aiming to update their architectures to provide more adaptability, robustness and richer learning environments. However, the construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This paper presents a computational model for developing SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available.     

A flexible architecture for distributed knowledge based systems with nomadic access through handheld devices

Nowadays any Knowledge Based System (KBS) realization needs of intercommunication among distributed components and to use non-connected and distributed data sources, which poses several challenges to the classical Artificial Intelligence field of KBS.

The multiagent paradigm and the use of ontologies are considered to be suitable tools to face the problems of designing and developing today KBS. On the other hand, using such networked KBS through handheld devices makes more efficient exploitation and interaction with the system.

This paper presents an open and flexible architecture for a distributed KBS and an application of it to construct a system for Psychological Disorders consulting, the so called PDA² (Psychological Disorder Assistant through PDA). We analyze the main features of the architecture as well as the agent tools we may use to construct it. Additionally, we present a support ontology for Psychological Disorders.     

Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment

Researchers in the field of AI and Law have developed a number of computational models of the arguments that skilled attorneys make based on past cases. However, these models have not accounted for the ways that attorneys use middle-level normative background knowledge (1) to organize multi-case arguments, (2) to reason about the significance of differences between cases, and (3) to assess the relevance of precedent cases to a given problem situation. We present a novel model, that accounts for these argumentation phenomena. An evaluation study showed that arguments about the significance of distinctions based on this model help predict the outcome of cases in the area of trade secrets law, confirming the quality of these arguments. The model forms the basis of an intelligent learning environment called CATO, which was designed to help beginning law students acquire basic argumentation skills. CATO uses the model for a number of purposes, including the dynamic generation of argumentation examples. In a second evaluation study, carried out in the context of an actual legal writing course, we compared instruction with CATO against the best traditional legal writing instruction. The results indicate that CATO's example-based instructional approach is effective in teaching basic argumentation skills. However, a more “integrated” approach appears to be needed if students are to achieve better transfer of these skills to more complex contexts. CATO's argumentation model and instructional environment are a contribution to the research fields of AI and Law, Case-Based Reasoning, and AI and Education.     

Task allocation model for distributed systems

The problem of distributing tasks to processors in a distributed computing system is addressed. A task should be assigned to a processor whose capabilities are most appropriate for the execution of that task and excessive interprocessor communication is avoided. A simple algorithm for task allocation is presented. The execution costs and communication costs of the tasks are represented by arrays. A task is either assigned to a processor or fused with another task using a simple criterion. The execution and communication costs are then modified suitably. The process continues until all the tasks are assigned to processors. This algorithm also facilitates incorporation of various system constraints. It is applicable to random program structures and to a system containing any number of processors.     

非正则分布参数系统的迭代学习控制

针对一类非正则分布参数系统的迭代学习控制问题进行讨论, 该类分布参数系统由抛物型偏微分方程构成. 基于非正则系统的特点, 使用D型学习律构建得到迭代学习控制律, 并基于压缩映射原理, 证明得到输出跟踪误差在??² 范数意义下沿迭代轴方向的收敛性结论. 仿真算例表明了所提出结论的有效性.

     

Abstract communication model for distributed systems

In some distributed and mobile communication models, a message disappears in one place and miraculously appears in another. In reality, of course, there are no miracles. A message goes from one network to another; it can be lost or corrupted in the process. Here, we present a realistic but high-level communication model where abstract communicators represent various nets and subnets. The model was originally developed in the process of specifying a particular network architecture, namely, the Universal Plug and Play architecture. But, it is general. Our contention is that every message-based distributed system, properly abstracted, gives rise to a specialization of our abstract communication model. The purpose of the abstract communication model is not to design a new kind of network; rather, it is to discover the common part of all message-based communication networks. The generality of the model has been confirmed by its successful reuse for very different distributed architectures. The model is based on distributed abstract state machines. It is implemented in the specification language AsmL and is used for testing distributed systems.     

General framework for distributed knowledge management in mechatronic systems

This paper is concerned with the application of knowledge management on the mechatronic system. First, data, information and knowledge terms have been explained according to their disparities and similarities. Then, knowledge management has been described in general. The Internet—based CNC machining center has been considered and its knowledge management model has been prepared. The model prepared has been analyzed for machining performance of the manufacturing system.     

A parallel fuzzy inference model with distributed prediction schemefor reinforcement learning

This paper proposes a three-layered parallel fuzzy inference model called reinforcement fuzzy neural network with distributed prediction scheme (RFNN-DPS), which performs reinforcement learning with a novel distributed prediction scheme. In RFNN-DPS, an additional predictor for predicting the external reinforcement signal is not necessary, and the internal reinforcement information is distributed into fuzzy rules (rule nodes). Therefore, using RFNN-DPS, only one network is needed to construct a fuzzy logic system with the abilities of parallel inference and reinforcement learning. Basically, the information for prediction in RFNN-DPS is composed of credit values stored in fuzzy rule nodes, where each node holds a credit vector to represent the reliability of the corresponding fuzzy rule. The credit values are not only accessed for predicting external reinforcement signals, but also provide a more profitable internal reinforcement signal to each fuzzy rule itself. RFNN-DPS performs a credit-based exploratory algorithm to adjust its internal status according to the internal reinforcement signal. During learning, the RFNN-DPS network is constructed by a single-step or multistep reinforcement learning algorithm based on the ART concept. According to our experimental results, RFNN-DPS shows the advantages of simple network structure, fast learning speed, and explicit representation of rule reliability.     

Algorithms for some graph problems on a distributed computational model

This paper presents distributed algorithms for some graph problems on a network model of computation. These graph problems include breadth-first and breadth-depth searches of graphs, recognition of directed acyclic graphs and strong connectedness, finding weights of all the shortest paths from a single source to all other nodes in a weighted directed acyclic graph, and analyzing activity networks. The results of computations (i.e., the outputs) of all the algorithms but the algorithm for recognition of directed acyclic graphs are available in a distributed manner. For algorithms in such a computational model, two types of complexity measures are important. One is the time complexity and the other is the message or communication complexity. Both of these complexities are obtained for each of the aforesaid distributed algorithms.     

Biotope: an integrated framework for simulating distributed multiagent computational systems

The study of distributed computational systems issues, such as heterogeneity, concurrency, control, and coordination, has yielded a number of models and architectures, which aspire to provide satisfying solutions to each of the above problems. One of the most intriguing and complex classes of distributed systems are computational ecosystems, which add an "ecological" perspective to these issues and introduce the characteristic of self-organization. Extending previous research work on self-organizing communities, we have developed Biotope, which is an agent simulation framework, where each one of its members is dynamic and self-maintaining. The system provides a highly configurable interface for modeling various environments as well as the "living" or computational entities that reside in them, while it introduces a series of tools for monitoring system evolution. Classifier systems and genetic algorithms have been employed for agent learning, while the dispersal distance theory has been adopted for agent replication. The framework has been used for the development of a characteristic demonstrator, where Biotope agents are engaged in well-known vital activities-nutrition, communication, growth, death-directed toward their own self-replication, just like in natural environments. This paper presents an analytical overview of the work conducted and concludes with a methodology for simulating distributed multiagent computational systems.     

A model for scheduling of object-based,distributed real-time systems

This paper describes a general model for pre-run-time scheduling of distributed real-time systems that are composed of abstract data types (definable in languages such as Ada, Clu and Modula-2) and abstract data objects (which can be defined in C++, Eiffel and RT Euclid). An architecture model, a programming paradigm, and execution and communication paradigms form the basis for this general model. The model includesabsolute timing constraints to represent periodicity and deadlines,relative timing constraints to model several kinds of timed precedence relations and synchronization requirements,independency constraints to capture non-determinism of conditionals and repetitions, andconsistency constraints to enforce consistent use of resources. In this paper, the model is formalized to obtain a mathematical foundation on which assignment (Verhoosel et al. 1993a, Welch 1992, Welch et al. 1993) and pre-run-time scheduling problems (Verhoosel et al. 1991, Verhoosel 1992, 1993a, 1993c) are defined. Additionally, the model is extended to allow exploitation of parallelism from programs, a technique that can be used during assignment and scheduling for meeting timing constraints.     

A distributed digital rights management model for secure information-distribution systems

There is a need to protect digital information content and the associated usage rights from unauthorized access, use, and dissemination. The protection mechanisms should meet the requirements for the correct management of fine-grained access and usage controls and the protection of user privacy. Digital rights management (DRM) solutions have significant relevance in this context. This paper describes a distributed DRM model for a secure information-distribution system consisting of six trust-building blocks. These are (i) the user application, (ii) the authentication and authorization module, (iii) Rights-Carrying and Self-Enforcing Objects (SEOs), (iv) the privacy enforcement module, (v) theUsage Tracking and Monitoring Proxy (UTMP), and (vi) thesecurity infrastructure. SEOs are information objects that carry access and usage rights and are responsible for the fine-grained enforcement of these rights. The security infrastructure plays a pivotal role in the creation, distribution, storage, manipulation, and communication of information objects across organizational boundaries with the required level of security. Our model was originally developed for an Internet-based learning project in Norwegian schools and meets most of the aforementioned requirements.     

A new model for distributed database systems - RETRACTED ARTICLE

A model is developed for allocating tables in a distributed database system. The model considers memory cost, transmission cost, table size and request rates, as well as updating rates of tables, the maximum allowable expected access times to tables at each computer and the memory capacity of each computer. The objective function is concerned with overall operating cost optimality. In this regard, the model is formulated as a non-linear integer zero–one programming problem, which can be converted into a linear zero–one programming model.     

A macroeconomic model for resource allocation in large-scale distributed systems

In this paper we discuss an economic model for resource sharing in large-scale distributed systems. The model captures traditional concepts such as consumer satisfaction and provider revenues and enables us to analyze the effect of different pricing strategies upon measures of performance important for the consumers and the providers. We show that given a particular set of model parameters the satisfaction reaches an optimum; this value represents the perfect balance between the utility and the price paid for resources. Our results confirm that brokers play a very important role and can influence positively the market. We also show that consumer satisfaction does not track the consumer utility; these two important performance measures for consumers behave differently under different pricing strategies. Pricing strategies also affect the revenues obtained by providers, as well as, the ability to satisfy a larger population of users.     

Modelling knowledge and action in distributed systems

We present a formal model that captures the subtle interaction between knowledge and action in distributed systems. We view a distributed system as a set ofruns, where a run is a function from time toglobal states and a global state is a tuple consisting of anenvironment state and alocal state for earch process in the system. This model is a generalization of those used in many previous papers.Actions in this model are associated with functions from global states to global states. Aprotocol is a function from local states to actions. We extend the standard notion of a protocol by definingknowledge-based protocols, ones in which a process' actions may depend explicitly on its knowledge. Knowledge-based protocols provide a natural way of describing how actions should take place in a distributed system. Finally, we show how the notion of one protocolimplementing another can be captured in our model. Joseph Y. Halpern received a B.Sc. in mathematics from the University of Toronto in 1975, and a Ph.D. in mathematics from Harvard University in 1981. In between, he spent two years as the head of the Mathematics Department at Bawku Secondary School, in Ghana. After a year as a visiting scientist at MIT, he joined IBM in 1982. He is currently the manager of the Mathematics and Related Computer Science Department at the IBM Almaden Research Center, and a consulting professor in the Computer Science Department at Stanford. His major research interests are reasoning about knowledge, distributed computation, and logics of programs. He was program chairman and organizer of the first conference of Theoretical Aspects of Reasoning About Knowledge, program chairman of the Fifth ACM Symposium on Principles of Distributed Computing, and was the co-recipient (with Ronald Fagin) of the MIT Publisher's Prize for the Best Paper Paper at the 1985 International Joint Conference on Artificial Intelligence. Ronald Fagin is manager of the Foundations of Computer Science group at the IBM Almaden Research Center. He received his B.A. degree in mathematics from Dartmouth College in 1967 and his Ph.D. in mathematics, specializing in mathematical logic, from the University of California at Berkeley in 1973. He joined IBM in 1973 at the Thomas J. Watson Research Center. In 1975, he transferred to the San Jose Research Laboratory (now the IBM Almaden Research Center) where most of his research has centered on applications of logic to computer science. In particular, he has done research on the theory of relational databases and, more recently, on theories of knowledge and belief. He has received three IBM Outstanding Innovation Awards for his contributions to relational database theory, extendible hashing, and reasoning about knowledge. He was co-recipient (with Joe Halpern) of the MIT Press Publisher's Prize for the Best Paper at the 1985 International Joint Conference on Artificial Interlligence.Some material in this paper appeared in preliminary form in Halpern and Fagin (1985). An abridged version of the paper appeared in Vogt F (ed) Proceeding of Concurrency 88 (Lecture Notes in Computer Science Vol. 335) Springer-Verlag, 1988, pp 18–32