Cooperative agent-based software architecture for distributed simulation

This paper proposes a cooperative multiagent model using distributed object-based systems for supporting distributed virtual environment and distributed simulation technologies for military and government applications. The agent model will use the condition-event driven rule based system as the basis for representing knowledge. In this model, the updates and revision of beliefs of agents corresponds to modifying the knowledge base. These agents are reactive and respond to stimulus as well as the environment in which they are embedded. Further, these agents are smart and can learn from their actions. The distributed agent-based software architecture will enable us to realise human behaviour model environment and computer-generated forces (also called computer-generated actor (CGA)) architectures. The design of the cooperative agent-based architecture will be based on mobile agents, interactive distributed computing models, and advanced logical modes of programming. This cooperative architecture will be developed using Java based tools and distributed databases

A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department

Objectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs

A software agent model of muscle myosin nanomotor

The state-of-the-art in information and robotic systems deals with analyzing of natural systems at nanoscale to apply them for constructing potential bio-nanosystems. This paper employs agent technology and introduces a software agent model of muscle myosin nanomotor which illustrates a set of information processes which are running during the mechanism of the nanomotor. Muscle myosin, as a desired dynamic component of potential bio-nanorobotic systems, is the driven motor of muscle contractions. In this work, firstly, muscle myosin nanomotor was introduced as a physical intelligent agent. Then, we have designed the internal decision-making process of the nanomotor using subsumption architecture of agent technology. The agent-based architectural model of the nanomotor was proposed with mapping the subsumption rules of the nanomotor to its respective Deterministic Finite Automaton (DFA). The proposed agent-based architectural DFA model of muscle myosin nanomotor demonstrated that the nanomotor could receive inputs from its environment, analyze data, and generate outputs. Also, the proposed agent-based architectural DFA model of muscle myosin nanomotor was in good agreement with the behavior of the nanomotor inside the muscle cells. Finally, the proposed agent-based architectural DFA model was implemented as a software agent model of the nanomotor. The developed software agent model of muscle myosin nanomotor traced the real behavior of the nanomotor in nature

Laboratory for Simulation Develpment - LSD

LSD is one of many programming languages designed to develop agent-based models. LSD implements time-driven models expressed in formats equivalent to discrete systems of equations, where each equation computes the value of a generic instance of a variable at a generic time step. LSD models are therefore extremely parsimonious in terms of details that users must provide to the system. When a model has been described, the system automatically generates a working program implementing the model, endowed with a complete set of interfaces for any possible operation on the model. The major feature of is that users can rely on an automatic scheduling system and on automatic retrieval of data required for the equations. Such features are particularly attractive in complex, multi-herarchical models. They permit even non- expert programmers to develop even relatively complex models with minimal training. The systems interfaces guarantee the complete control of the model at building, at run-time and at post-simulation analysis, facilitating debugging, revisions and detailed analysis of model results, which are useful properties especially when developing large models for ambitious projects. The design of LSD is based on an "open architecture", so that LSD can be used to implement any type of model, including even-driven models and models based on customized data structures. The intrinsic modularity of LSD models make them easily scalable facilitating the development of highly complex models by demanding users. The underlining layer of C++, accessible by the users, allows the inclusions of external libraries or of complex data structures, besides an extreme speed and dimensions of the model. This work reports on the major features of the design of LSD outlining its most prominent advantages for users of simulation models in research, particularly for agent- based simulations.Simulations models, programming languages

Designing Multi-Agent Systems - The NDA Approach Applied in Health Care

In this paper we introduce inherent problems of information logistics in health care. Promising research results on agentbased systems have allowed us to conclude that this approach is especially suitable to coping with these problems. In order to adequately capture the requirements of a complex setting, we present an approach for the design of agent-based systems. The basis forms the ethnography-based requirements analysis approach Needs Driven Approach (NDA). The NDA supports the participating observation of work processes and guides the construction of domain models. As a result of a field study, a Technische Universitat Munchen meta-model is constructed which reflects the interrelationships of its elements. According to basic ideas of the Model Driven Architecture (MDA), the elements of the meta-model are mapped to constructs of software engineering

Multi-Agent Approach to Modeling and Implementing Fault-Tolerance in Reactive Autonomic Systems

Recently, autonomic computing has been proposed as a promising solution for software complexity in IT industry. As an autonomic approach, the Reactive Autonomic Systems Framework (RASF) proposes a formal modeling based on mathematical category theory, which addresses the self-* properties of reactive autonomic systems in a more abstract level. This thesis is about the specification and implementation of the reactive autonomic systems (RAS) through multi-agent approach by laying emphasis on the fault-tolerance property of RAS. Furthermore, this thesis proposes a model-driven approach to transform the RAS model to agent templates in multi-agent model using Extensible Stylesheet Language Transformation (XSLT). The multi-agent approach in this research is implemented by Jadex, a high-level Java-based agent programming language. The intelligent agents are created in Jadex based on the Belief-Desire-Intension (BDI) agent architecture. The approach is illustrated on a case study

Long Short-Term Planning for Conversational Recommendation Systems

In Conversational Recommendation Systems (CRS), the central question is how the conversational agent can naturally ask for user preferences and provide suitable recommendations. Existing works mainly follow the hierarchical architecture, where a higher policy decides whether to invoke the conversation module (to ask questions) or the recommendation module (to make recommendations). This architecture prevents these two components from fully interacting with each other. In contrast, this paper proposes a novel architecture, the long short-term feedback architecture, to connect these two essential components in CRS. Specifically, the recommendation predicts the long-term recommendation target based on the conversational context and the user history. Driven by the targeted recommendation, the conversational model predicts the next topic or attribute to verify if the user preference matches the target. The balance feedback loop continues until the short-term planner output matches the long-term planner output, that is when the system should make the recommendation.Comment: 14 pages, 3 figures. Accepted by ICONIP 202

Model-driven engineering techniques for the development of multi-agent systems

Model-driven engineering (MDE), implicitly based upon meta-model principles, is gaining more and more attention in software systems due to its inherent benefits. Its use normally improves the quality of the developed systems in terms of productivity, portability, inter-operability and maintenance. Therefore, its exploitation for the development of multi-agent systems (MAS) emerges in a natural way. In this paper, agent-oriented software development (AOSD) and MDE paradigms are fully integrated for the development of MAS. Meta-modeling techniques are explicitly used to speed up several phases of the process. The Prometheus methodology is used for the purpose of validating the proposal. The meta-object facility (MOF) architecture is used as a guideline for developing a MAS editor according to the language provided by Prometheus methodology. Firstly, an Ecore meta-model for Prometheus language is developed. Ecore is a powerful tool for designing model-driven architectures (MDA). Next, facilities provided by the Graphical Modeling Framework (GMF) are used to generate the graphical editor. It offers support to develop agent models conform to the meta-model specified. Afterwards, it is also described how an agent code generator can be developed. In this way, code is automatically generated using as input the model specified with the graphical editor. A case of study validates the method put in practice for the development of a multi-agent surveillance system

Design of a Multi-Agent System for Process Monitoring and Supervision

New process monitoring and control strategies are developing every day together with process automation strategies to satisfy the needs of diverse industries. New automation systems are being developed with more capabilities for safety and reliability issues. Fault detection and diagnosis, and process monitoring and supervision are some of the new and promising growth areas in process control. With the help of the development of powerful computer systems, the extensive amount of process data from all over the plant can be put to use in an efficient manner by storing and manipulation. With this development, data-driven process monitoring approaches had the chance to emerge compared to model-based process monitoring approaches, where the quantitative model is known as a priori knowledge. Therefore, the objective of this research is to layout the basis for designing and implementing a multi-agent system for process monitoring and supervision. The agent-based programming approach adopted in our research provides a number of advantages, such as, flexibility, adaptation and ease of use. In its current status, the designed multi-agent system architecture has the three different functionalities ready for use for process monitoring and supervision. It allows: a) easy manipulation and preprocessing of plant data both for training and online application; b) detection of process faults; and c) diagnosis of the source of the fault. In addition, a number of alternative data driven techniques were implemented to perform monitoring and supervision tasks: Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), and Self-Organizing Maps (SOM). The process system designed in this research project is generic in the sense that it can be used for multiple applications. The process monitoring system is successfully tested with Tennessee Eastman Process application. Fault detection rates and fault diagnosis rates are compared amongst PCA, FDA, and SOM for different faults using the proposed framework