PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture

In this paper, a generic architecture, designed to support the implementation of applications aimed at managing information among different and heterogeneous sources, is presented. Information is filtered and organized according to personal interests explicitly stated by the user. User pro- files are improved and refined throughout time by suitable adaptation techniques. The overall architecture has been called PACMAS, being a support for implementing Personalized, Adaptive, and Cooperative MultiAgent Systems. PACMAS agents are autonomous and flexible, and can be made personal, adaptive and cooperative, depending on the given application. The peculiarities of the architecture are highlighted by illustrating three relevant case studies focused on giving a support to undergraduate and graduate students, on predicting protein secondary structure, and on classifying newspaper articles, respectively

Conceptualizing Architectures for E-Business Systems

E-Business (E-Commerce) infrastructure requires organizations and their IT systems to be flexible and adaptive to changes in the dynamic business environment. Component-based development is seen to be the solution for rapidly creating modifiable and maintainable e- Business systems. Organizations have attempted to strategically align their IT and organizational goals in developing mission-critical e-Business systems. In this research we propose a architecture for designing such systems based on the notion of business capabilities and technology capabilities. This tiered architecture provides a formal framework for understanding the cohesive relationships between IT and business capabilities in organizations. It allows for rapid “what-if” analysis for managing IT investments and offers the flexibility needed to manage changes in designing and developing information systems for e-Business. The architecture supports the packaging of existing legacy systems and links them to the e-Business systems to create outward facing mission-critical information systems demanded by the e-Business infrastructure

A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study

Background: Internet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable. Objective: This study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems. Methods: This study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients’ next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project’s problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes). Results: This paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts. Conclusions: Our results showed that the proposed RA is flexible and capable of adapting interventions based on patients’ needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.publishedVersio

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

Self-Adaptive Architecture for Multi-sensor Embedded Vision System

International audienceArchitectural optimization for heterogeneous multi-sensor processing is a real technological challenge. Most of the vision systems involve only one single color sensor and they do not address the heterogeneous sensors challenge. However, more and more applications require other types of sensor in addition, such as infrared or low-light sensor, so that the vision system could face various luminosity conditions. These heterogeneous sensors could differ in the spectral band, the resolution or even the frame rate. Such sensor variety needs huge computing performance , but embedded systems have stringent area and power constraints. Reconfigurable architecture makes possible flexible computing while respecting the latter constraints. Many reconfigurable architectures for vision application have been proposed in the past. Yet, few of them propose a real dynamic adaptation capability to manage sensor heterogeneity. In this paper, a self-adaptive architecture is proposed to deal with heterogeneous sensors dynamically. This architecture supports on-the-fly sensor switch. Architecture of the system is self-adapted thanks to a system monitor and an adaptation controller. A stream header concept is used to convey sensor information to the self-adaptive architecture. The proposed architecture was implemented in Altera Cyclone V FPGA. In this implementation, adaptation of the architecture consists in Dynamic and Partial Reconfiguration of FPGA. The self-adaptive ability of the architecture has been proved with low resource overhead and an average global adaptation time of 75 ms

Measuring the BDARX architecture by agent oriented system a case study

Distributed systems are progressively designed as multi-agent systems that are helpful in designing high strength complex industrial software. Recently, distributed systems cooperative applications are openly access, dynamic and large scales. Nowadays, it hardly seems necessary to emphasis on the potential of decentralized software solutions. This is because the main benefit lies in the distributed nature of information, resources and action. On the other hand, the progression in multi agent systems creates new challenges to the traditional methodologies of fault-tolerance that typically relies on centralized and offline solution. Research on multi-agent systems had gained attention for designing software that operates in distributed and open environments, such as the Internet. DARX (Dynamic Agent Replication eXtension) is one of the architecture which aimed at building reliable software that would prove to be both flexible and scalable and also aimed to provide adaptive fault tolerance by using dynamic replication methodologies. Therefore, the enhancement of DARX known as BDARX can provide dynamic solution of byzantine faults for the agent based systems that embedded DARX. The BDARX architecture improves the fault tolerance ability of multi-agent systems in long run and strengthens the software to be more robust against such arbitrary faults. The BDARX provide the solution for the Byzantine fault tolerance in DARX by making replicas on the both sides of communication agents by using BFT protocol for agent systems instead of making replicas only on server end and assuming client as failure free. This paper shows that the dynamic behaviour of agents avoid us from making discrimination between server and client replicas

Database systems: different interfaces for different users

Users of database systems, especially public information systems, can vary widely in their aptitudes and experience with using computer systems. This study investigates whether users with different aptitudes and experience are suited to database system interfaces with different dialogue styles. Experiments were conducted to examine the performance of users on interfaces with different dialogue styles. The results suggested that dialogue styles which aid navigation through the database, and which constrain the dialogue, are suitable for users with both a low spatial ability and a low experience of using command language style interfaces. However dialogue styles which offer little assistance with navigation, and which allow an open and flexible dialogue, are suitable for users with a high spatial ability whatever their experience, and for users with a low spatial ability but high experience of using command language style interfaces. The suggestion that different users of a database system are suited to interfaces with different dialogue styles, raises the issue of how to present different users with different interfaces. The study investigates the possibility of producing an adaptive database system, which automatically provides each user with the interface which suits them. A demonstration adaptive version of one of the databases used in the experimental work was developed. The system was designed to present an interface which aided navigation, and constrained the dialogue, to users with low spatial ability and low actual and potential experience of using command style interfaces. The system was designed to present an interface allowing an open and flexible dialogue to users with high spatial ability, or low spatial ability but high actual or potential experience of using command style interfaces. The adaptive system was constructed with the aid of a prototype 'adaptive system shell", designed to provide a generic architecture for the mechanisms necessary for an adaptive system

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Advancing interactive systems with liquid crystal network-based adaptive electronics

Achieving adaptive behavior in artificial systems, analogous to living organisms, has been a long-standing goal in electronics and materials science. Efforts to integrate adaptive capabilities into synthetic electronics traditionally involved a typical architecture comprising of sensors, an external controller, and actuators constructed from multiple materials. However, challenges arise when attempting to unite these three components into a single entity capable of independently coping with dynamic environments. Here, we unveil an adaptive electronic unit based on a liquid crystal polymer that seamlessly incorporates sensing, signal processing, and actuating functionalities. The polymer forms a film that undergoes anisotropic deformations when exposed to a minor heat pulse generated by human touch. We integrate this property into an electric circuit to facilitate switching. We showcase the concept by creating an interactive system that features distributed information processing including feedback loops and enabling cascading signal transmission across multiple adaptive units. This system responds progressively, in a multi-layered cascade to a dynamic change in its environment. The incorporation of adaptive capabilities into a single piece of responsive material holds immense potential for expediting progress in next-generation flexible electronics, soft robotics, and swarm intelligence.</p