A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

Satellite-enabled interactive education: scenarios and systems architectures

There are specific sectors of the economy that can benefit from satellite-based tele-education. Areas, such as maritime and agriculture, share common needs for both broadband connectivity at remote geographical areas that cannot otherwise be covered, and for innovative content for tele-education purposes. Furthermore, each area has special requirements with regard to the type of content to be delivered. In this paper we propose a set of architectural designs and case scenarios that will realise such interactive end-to-end education systems based on satellite communications. Services requirements in this setting are also identified and discussed

A goal-oriented requirements modelling language for enterprise architecture

Methods for enterprise architecture, such as TOGAF, acknowledge the importance of requirements engineering in the development of enterprise architectures. Modelling support is needed to specify, document, communicate and reason about goals and requirements. Current modelling techniques for enterprise architecture focus on the products, services, processes and applications of an enterprise. In addition, techniques may be provided to describe structured requirements lists and use cases. Little support is available however for modelling the underlying motivation of enterprise architectures in terms of stakeholder concerns and the high-level goals that address these concerns. This paper describes a language that supports the modelling of this motivation. The definition of the language is based on existing work on high-level goal and requirements modelling and is aligned with an existing standard for enterprise modelling: the ArchiMate language. Furthermore, the paper illustrates how enterprise architecture can benefit from analysis techniques in the requirements domain

Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a recently developed cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity

A Comparison of Different Cognitive Paradigms Using Simple Animats in a Virtual Laboratory, with Implications to the Notion of Cognition

In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no best model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical aid, I note that there is no best approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on proper approaches for cognition: all approaches are a bit proper, but none will be proper enough. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition

Designing Software Architectures As a Composition of Specializations of Knowledge Domains

This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience

VXA: A Virtual Architecture for Durable Compressed Archives

Data compression algorithms change frequently, and obsolete decoders do not always run on new hardware and operating systems, threatening the long-term usability of content archived using those algorithms. Re-encoding content into new formats is cumbersome, and highly undesirable when lossy compression is involved. Processor architectures, in contrast, have remained comparatively stable over recent decades. VXA, an archival storage system designed around this observation, archives executable decoders along with the encoded content it stores. VXA decoders run in a specialized virtual machine that implements an OS-independent execution environment based on the standard x86 architecture. The VXA virtual machine strictly limits access to host system services, making decoders safe to run even if an archive contains malicious code. VXA's adoption of a "native" processor architecture instead of type-safe language technology allows reuse of existing "hand-optimized" decoders in C and assembly language, and permits decoders access to performance-enhancing architecture features such as vector processing instructions. The performance cost of VXA's virtualization is typically less than 15% compared with the same decoders running natively. The storage cost of archived decoders, typically 30-130KB each, can be amortized across many archived files sharing the same compression method.Comment: 14 pages, 7 figures, 2 table